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title: Associations between dietary intake and glucose tolerance in clinical and metabolomics-based
metabotypes
authors:
- Amanda Rundblad
- Jacob J. Christensen
- Kristin S. Hustad
- Nasser E. Bastani
- Inger Ottestad
- Kirsten B. Holven
- Stine M. Ulven
journal: Genes & Nutrition
year: 2023
pmcid: PMC10007735
doi: 10.1186/s12263-023-00721-6
license: CC BY 4.0
---
# Associations between dietary intake and glucose tolerance in clinical and metabolomics-based metabotypes
## Abstract
### Background
Metabotyping is a novel concept to group metabolically similar individuals. Different metabotypes may respond differently to dietary interventions; hence, metabotyping may become an important future tool in precision nutrition strategies. However, it is not known if metabotyping based on comprehensive omic data provides more useful identification of metabotypes compared to metabotyping based on only a few clinically relevant metabolites.
### Aim
This study aimed to investigate if associations between habitual dietary intake and glucose tolerance depend on metabotypes identified from standard clinical variables or comprehensive nuclear magnetic resonance (NMR) metabolomics.
### Methods
We used cross-sectional data from participants recruited through advertisements aimed at people at risk of type 2 diabetes mellitus ($$n = 203$$). Glucose tolerance was assessed with a 2-h oral glucose tolerance test (OGTT), and habitual dietary intake was recorded with a food frequency questionnaire. Lipoprotein subclasses and various metabolites were quantified with NMR spectroscopy, and plasma carotenoids were quantified using high-performance liquid chromatography. We divided participants into favorable and unfavorable clinical metabotypes based on established cutoffs for HbA1c and fasting and 2-h OGTT glucose. Favorable and unfavorable NMR metabotypes were created using k-means clustering of NMR metabolites.
### Results
While the clinical metabotypes were separated by glycemic variables, the NMR metabotypes were mainly separated by variables related to lipoproteins. A high intake of vegetables was associated with a better glucose tolerance in the unfavorable, but not the favorable clinical metabotype (interaction, $$p \leq 0.01$$). This interaction was confirmed using plasma concentrations of lutein and zeaxanthin, objective biomarkers of vegetable intake. Although non-significantly, the association between glucose tolerance and fiber intake depended on the clinical metabotypes, while the association between glucose tolerance and intake of saturated fatty acids and dietary fat sources depended on the NMR metabotypes.
### Conclusion
Metabotyping may be a useful tool to tailor dietary interventions that will benefit specific groups of individuals. The variables that are used to create metabotypes will affect the association between dietary intake and disease risk.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12263-023-00721-6.
## Background
Type 2 diabetes mellitus (T2DM) is one of the major causes of death globally, and the number of people with T2DM increases rapidly [1]. The important risk factors for T2DM include obesity, an unhealthy diet, and a sedentary lifestyle [2]. Evidence indicates that it is possible to prevent T2DM and improve glycemic control by replacing saturated with polyunsaturated fats and refined grain with whole grain, having a moderate alcohol consumption, limiting intake of processed meat and sugar-sweetened beverages, as well as consuming nuts, coffee, and low-fat dairy [3–5]. However, studies are inconsistent, in part because metabolic characteristics may influence diet-disease associations [6, 7].
The metabolome is the totality of small molecules present in cells, tissues, or body fluids. The genome, transcriptome, and proteome as well as the gut microbiota and environmental factors, such as diet and drugs, produce the metabolome [8]. Hence, the interactions that shape the metabolome also shape disease risk [9]. A metabolic phenotype, also called metabotype, refers to a group of individuals with a similar metabolic profile [10]. Metabotyping can be used in personalized medicine to predict drug response, and investigating associations between metabotypes and disease risk may provide insight into risk factors and improved treatment strategies [11, 12]. Metabotypes can be generated using different approaches, using a few selected or a large variety of metabolites, in a fasting state or as a response to an intervention [6]. In the simplest sense, metabotyping can be based on diagnosis criteria or subgrouping of patients, while a more complex approach is to metabotype based on omics-technologies, including metabolomics, transcriptomics, and epigenomics [6]. However, to justify the use of expensive and time-consuming technologies to generate metabotypes, these omics-based metabotypes should be more useful than the more simple clinical metabotypes.
The metabolic phenotype may modify the response to dietary intake on risk of lifestyle diseases [6, 13]. Hence, metabotyping can be used to identify and stratify groups of individuals that respond differently to dietary intake that therefore could benefit from targeted nutritional recommendations [14]. Previous randomized controlled trials that did not succeed to improve glucose tolerance by dietary interventions may have provided dietary interventions that are not optimal for the whole group [15, 16]. Hence, in future studies, the use of metabotyping may guide researchers to tailor dietary interventions to metabotypes that are more likely to benefit from specific dietary modifications.
With this in mind, we aimed to investigate the association between long-term habitual dietary intake and glucose tolerance in metabotyped subjects, based on standard clinical variables or comprehensive NMR metabolomics. We hypothesized that metabotypes with more unfavorable characteristics would show stronger associations between glucose tolerance and dietary intake than the more favorable metabotypes.
## Participants
This cross-sectional study was conducted between August 2018 and September 2019 at the University of Oslo, Norway. Participants were recruited through advertisements, aimed to reach those at risk of T2DM, in social media and medical practices at the University of Oslo. After a telephone interview, individuals not diagnosed with T2DM or using drugs affecting blood glucose levels attended a screening visit to screen for eligibility to participate in a randomized controlled trial examining the effects of intake of salmon fish protein [17]. Data collected at this screening visit was used in the current cross-sectional sub study. The study was conducted according to the guidelines laid down in the Declaration of Helsinki. All participants gave their written informed consent, and the Regional Ethics Committee for Medical Research in South-East Norway approved the study. The study was registered at ClinicalTrials.gov (ClinicalTrials.gov Identifier: NCT03764423).
## Clinical assessment
The participants’ body weights were measured on a digital scale with a stadiometer (SECA GmbH, Germany) with light clothing and without shoes, and waist circumference was measured according to WHO guidelines [18]. Blood pressure was measured in the non-dominant arm after a 10-min rest by a Carescape V100 monitor (GE Healthcare, USA). Three measurements were obtained with a 1-min interval, and the average of the last two measurements was calculated. Information about the use of hormonal contraceptives and other drugs, as well as information about the menopausal status was obtained by a questionnaire.
## Blood sampling, OGTT, and laboratory analyses
The participants were instructed to avoid consuming alcohol and doing strenuous physical activity the day before the visit. After an overnight fast, venous blood samples were drawn. For the oral glucose tolerance test (OGTT), the participants were instructed to drink a 75-g anhydrated glucose drink (Esteriplas, Portugal) in less than 5 min within 10 min after a fasting venous blood sample. Then, the participants were instructed to avoid eating, drinking, and doing any activity and to remain in the waiting room until the postprandial blood samples were drawn 2 h after finishing the glucose drink.
We obtained serum from silica gel tubes (Becton, Dickinson and Company) kept at room temperature for 30–60 min before centrifugation (1500 g, 15 min). Plasma was obtained from EDTA tubes (Becton, Dickinson and Company) that were immediately placed on ice and centrifuged within 10 min (2000 g, 4 °C, 15 min). Serum concentrations of standard biochemical parameters, including fasting and 2-h OGTT glucose, insulin, HbA1c, triglycerides, total-, LDL-, and HDL-cholesterol and high-sensitive C-reactive protein were measured by standard methods at an accredited routine laboratory (Fürst Medical Laboratory, Norway).
## NMR spectroscopy
About 250 metabolic biomarkers were quantified from EDTA plasma using a commercial high-throughput nuclear magnetic resonance (NMR) spectroscopy platform (Nightingale Health, www.nightingalehealth.com). This platform quantifies metabolites in three molecular windows; lipids, lipoproteins, and low molecular weight metabolites. The lipids quantified include SFA, MUFA, and PUFA, as well as some specific fatty acids, sphingomyelins, and cholines. Among lipoproteins, 14 lipoprotein subclass particles are quantified, as well as the particles’ concentrations of total lipids, phospholipids, total and free cholesterol, cholesteryl esters, and triglycerides. The lipoprotein subclasses are defined by their average diameter; > 75 nm, 64 nm, 53.6 nm, 44.5 nm, 36.8 nm, and 31.3 nm for the six VLDL subclasses; 28.6 nm for intermediate density lipoprotein (IDL); 25.5 nm, 23.0 nm, and 18.7 nm for the three LDL subclasses; and 14.3 nm, 12.1 nm, 10.9 nm, and 8.7 nm for the four HDL subclasses. The low molecular weight metabolites quantified include amino acids, albumin, creatinine, glycoprotein acyls, ketone bodies, and glycolysis-related metabolites. In this study, variables expressing ratios and percentages were removed, and a total of 168 metabolites were used for clustering analyses (Supplemental file 1). Details of this NMR metabolomics platform have previously been described [19, 20].
## Dietary assessment
We assessed habitual food intake from the preceding year using food-frequency questionnaires (FFQ) [21]. The FFQ included questions about the frequency of intake and portion sizes for 270 food items. From the FFQ, we obtained data on intake of food items (g/person/day) and as intake of nutrients as energy percent (E%). Food groups were constructed manually by categorizing food items as shown in Table 1.Table 1Grouping of food items into food groups used in regression analysesFood groupFood itemsVegetablesCarrot, rutabaga, cabbage, cauliflower, broccoli, onion, lettuce, cucumber, squash, tomato, bell pepper, spinach, peas, beans, mushroom, canned vegetables, pickled vegetables, vegetable dishes, and vegetarian productsNuts and seedsSalted and unsalted nuts, seedsRiceRicePastaPasta and pasta dishesPotatoBoiled, pan fried, fried, and mashed potatoes, potato gratin, potato saladWhole grainWhole grain (> $50\%$) bread, crisp bread, grains, oatmeal, unsweetened cereals, porridgeRefined grainWhite bread, bread with < $50\%$ whole grain flour, sweetened cereals, tortillas, buns, cookies, cakesFruitsCitrus fruit, apple, pear, other fresh fruits and berries, fruit dishes and products, dried fruitsPoultryPoultry and poultry sausagesRed meatBeef, game, sheepProcessed meatSalted, cured and canned meat, minced meat, sausages, ham, liver pâté, other meat productsTotal fish and shellfishCod, pollock, salmon, trout, herring, mackerel, shellfish, sushi, fish spreads, fish products, breaded fish, other fishLean fishCod, pollock, breaded fish, fish productsFatty fishSalmon, trout, herring, mackerelTotal dairyMilk, yoghurt, flavored milk, fermented milk, quark, skyr, cream and sour cream, milk and cream products, ice cream, cheese, low-fat cheese, whey cheese, butterLow-fat dairyMilk, yoghurt, flavored milk, fermented milk, quark, skyr, low-fat cheese (< $20\%$ fat), low fat whey cheeseHigh-fat dairyCream, sour cream, ice cream, Cheese (> $20\%$ fat), whey cheese, butterFermented dairyYoghurt, fermented milk, quark, skyr, cheeseNon-fermented dairyMilk, flavored milk, ice cream, milk and cream products, whey cheese, butterOil and oil productsVegetable oils, mayonnaise, salad dressings, mayonnaise based saladsMargarineMargarine and low-fat margarineCoffeeCoffeeTeaTeaASBArtificially sweetened soda, lemonade and energy drinksAlcoholic beveragesBeer, wine, spiritsSweetsJam, marmalade, juice, sugar, honey, syrup, sweet bread spreads, chocolate, candy, desserts, popsicle, SSB, potato chips, other snacksEggEgg, omelet, scrambled eggsASB artificially sweetened beverages, SSB sugar-sweetened beverages
## Quantification of plasma carotenoids
The EDTA plasma concentration of the carotenoids lutein, zeaxanthin, β-cryptoxanthin, α-carotene, β-carotene, and lycopene were determined by high-performance liquid chromatography with ultraviolet detection (HPLC–UV) as described previously [22]. Briefly, plasma samples were precipitated by the addition of a 4.5 times volume of isopropanol containing internal standard. Plasma calibrators and controls were quantified against the standardized reference material 968c from the National Institute of Standards and Technology.
## Generation of metabotypes and statistical analyses
Because we wanted to address how the large heterogeneity in metabotype generation may determine the findings in metabotype studies, we generated metabotypes in two distinct ways. The clinical metabotypes were generated by categorizing participants based on thresholds of a small set of clinically relevant variables. The NMR metabotypes, on the other hand, were generated by clustering participants based on comprehensive omics data.
## Clinical metabotypes
The participants with either fasting glucose > 5.5 mmol/L, 2-h glucose > 6.4 mmol/L or HbA1c ≥ $5.8\%$ were categorized as unfavorable clinical metabotype. All other participants were categorized as favorable clinical metabotype.
## Clustering of participants into NMR-based metabotypes
After imputing missing data using the k-nearest neighbor and scaling to mean = 0 and SD = 1, we used NMR metabolomics data to cluster participants into metabotypes by three different approaches. In the first approach, we clustered the scaled data directly, using k-means clustering. In the second approach, we did principle component analysis (PCA) with the scaled NMR data as input and clustered the participants using the first four principal components using k-means clustering. Finally, in the third approach, we regressed all the scaled NMR variables on sex, age, BMI, smoking, and use of statins with a linear model, before we clustered the participants using the residuals from the regression model.
## Associations between a 2-h glucose and food intake, and interactions with metabotypes
The intake of most food groups were right skewed; hence, all food group variables were log-transformed (log(x + 1)) to obtain more normally distributed variables. We used linear models to analyze if there were associations between 2-h glucose and intake of food groups, adjusted for sex, age, BMI, smoking, statin use, and energy intake (kJ) in the whole sample. To analyze if there were food group-metabotype interactions on the association with a 2-h glucose, we used linear models with a food group-metabotype interaction term, adjusted for the same covariates. These interaction analyses compare the favorable to the unfavorable clinical metabotype and the favorable to the unfavorable NMR metabotype. There are no statistical comparisons between metabotyping strategies. The corresponding models were used for carotenoid-metabotype interaction analyses. To visualize the associations between intake of food groups and 2-h glucose, we regressed 2-h glucose on the food groups, adjusted for the same covariates, but without the interaction term, with data from each metabotype separately. The corresponding analyses for nutrient intake as E% were performed in the same manner, except that these analyses were not adjusted for energy intake. Finally, we investigated the important confounding factors age and sex using a linear model with a 2-h glucose as the dependent variable and with an interaction term between the food variables and these factors. All statistical analyses were performed in R, version 4.0.3 [23].
## Characteristics of the metabotypes
Clinical, NMR, and FFQ data, after excluding participants with an energy intake > 20 000 kJ ($$n = 4$$), were available for 203 participants that were used for analyses in this study. No participants were excluded for having an energy intake < 4 000 kJ. The favorable clinical metabotype ($$n = 99$$) had lower fasting and 2-h glucose and HbA1c than the unfavorable clinical metabotype ($$n = 104$$, Table 2), as expected, as these were the variables we used to generate the clinical metabotypes. In addition, the favorable clinical metabotype was younger and had more premenopausal women and women using hormonal contraceptives, a lower BMI, waist circumference, insulin, and CRP and higher Lp(a) than the unfavorable clinical metabotype. Table 2Characteristics of the clinical metabotypesFavorable clinical metabotype ($$n = 99$$)*Unfavorable clinical metabotype ($$n = 104$$)**n (%) Men31 (31.3)39 (37.5) Statin users5 (5.1)24 (23.1) Anti-inflammatory drug users3 [3]4 (3.8) Premenopausal women48 [71]18 [28] Women using hormonal contraceptives27 [40]5 [8]Mean (SD) Age (years)44 [12]55 [10] BMI (kg/m2)30.9 (5.1)33.3 (4.7) Waist circumference (cm)101.2 (14.0)111.9 (12.0) HbA1c (mmol/mol)27 [14]33 [16] Total-C (mmol/L)5.0 (0.9)5.1 (1.0) LDL-C (mmol/L)3.3 (0.8)3.5 (1.0)Median (IQR) HDL-C (mmol/L)1.4 (0.5)1.3 (0.4) Triglycerides (mmol/L)1.15 (0.56)1.46 (1.02) Lp(a) (mg/L)316 [498]202 [425] Fasting glucose (mmol/L)4.9 (0.4)5.8 (0.8) Insulin (pmol/L)61 [42]100 [77] 2-h glucose (mmol/L)4.7 (1.4)6.6 (2.6) Systolic BP (mmHg)116 [14]124 [21] Diastolic BP (mmHg)68 [12]72 [13] CRP (mg/L)2.1 (3.1)3.8 (5.1)BMI, body mass index; HbA1c, glycated haemoglobin; C, cholesterol; LDL, low-density lipoprotein; HDL, hgh-density lipoprotein; Lp(a), lipoprotein a; BP, blood pressure; CRP, C-reactive protein*Age, $$n = 96$$; BMI and waist circumference, $$n = 98$$; Lp(a), $$n = 50$$; 2-h glucose, $$n = 97$$**Age and BMI, $$n = 103$$; fat mass, $$n = 98$$; LDL-C and HDL-C, $$n = 102$$; Lp(a), $$n = 66$$ *We* generated the NMR metabolomics-based metabotypes using three different approaches. Firstly, the participants were clustered into two metabotypes based on scaled NMR metabolomics data directly. Secondly, we did a PCA on NMR metabolomics data. The first four principal components (PC) explained about $85\%$ of the variation (Supplemental Fig. 1), and we used these four PCs to cluster the participants into two metabotypes. The concentration of VLDL, LDL, IDL, and HDL particles and lipids, including cholesterol, were important contributors for the first four PCs (Fig. 1). Finally, we wanted to capture variation in the NMR metabolomics data independently of variation in sex, age, BMI, statin use, and smoking status. Hence, residuals from regression models with these variables as independent variables were clustered into two metabotypes. The separation of participants into the clinical metabotypes and the NMR metabotypes based on the three different clustering approaches are shown in Fig. 2, and the participant clusters are shown in Supplemental Fig. 2. As the different clustering approaches resulted in very similar metabotypes, we continued our analyses with the clusters from direct clustering of NMR data and from clustering of the first four PCs. The NMR-based metabotypes are hereafter called favorable NMR metabotype and unfavorable NMR metabotype. Fig. 1The variables with the largest contribution to the separation of the first four principal components. ApoA1, apolipoprotein A1, C, cholesterol, CE, cholesteryl esters, FC, free cholesterol, HDL, high-density lipoprotein, IDL, intermediate-density lipoprotein, L, large, LDL, low-density lipoprotein, M, medium, P, particle concentration, PC, principal component, PL, phospholipids, S, small, VLDL, very-low-density lipoprotein, XL, extra-largeFig. 2Separation of participants into the favorable and unfavorable clinical and NMR metabotypes. The NMR metabotypes were generated using three approaches: k-means clusters based on NMR metabolomics data, k-means clusters of the four first principal components, and k-means clusters of residuals from regression analyses adjusted for age, sex, body mass index, statin use, and smoking. PC, principal components The favorable NMR metabotype ($$n = 127$$) had lower total- and LDL-C and triglycerides and higher HDL-C than the unfavorable NMR metabotype ($$n = 76$$, Table 3). In addition, there were more men in the unfavorable than the favorable metabotype. The age, proportion of premenopausal women and women using hormonal contraceptives, BMI, waist circumference, and glucose-related variables were more similar between the NMR metabotypes than between the clinical metabotypes. Table 3Characteristics of the NMR metabotypesFavorable NMR metabotype ($$n = 127$$)*Unfavorable NMR metabotype ($$n = 76$$)**n (%) Men30 (23.6)40 (52.6) Statin users22 (17.3)7 (9.2) Anti-inflammatory drug users7 (5.5)0 [0] Premenopausal women54 [56]12 [33] Women using hormonal contraceptives29 [30]3 [8]Mean (SD) Age (years)49 [13]51 [11] BMI (kg/m2)31.8 (5.1)32.7 (4.9) Waist circumference (cm)105.0 (14.5)109.6 (12.8) HbA1c (mmol/mol)29 [15]32 [16] Total-C (mmol/L)4.6 (0.8)5.7 (0.9) LDL-C (mmol/L)3.0 (0.7)4.1 (0.9) Median (IQR) HDL-C (mmol/L)1.4 (0.5)1.1 (0.3) Triglycerides (mmol/L)1.06 (0.45)1.9 (0.99) Lp(a) (mg/L)309 [531]227 [341] Fasting glucose (mmol/L)5.1 (0.9)5.5 (0.9) Insulin (pmol/L)68 [59]96 [71] 2-h glucose (mmol/L)5.1 (2.2)5.8 (2.5) Systolic BP (mmHg)117 [18]123 [17] Diastolic BP (mmHg)70 [12]73 [14] CRP (mg/L)2.5 (3.7)3.3 (4.8)*Age, $$n = 125$$; BMI, waist circumference, 2 h glucose, systolic and diastolic blood pressure, $$n = 126$$; Lp(a), $$n = 67$$**Age, LDL-c, HDL-C, $$n = 74$$; BMI and 2-h glucose, $$n = 75$$; Lp(a), $$n = 49$$BMI, body mass index, HbA1c, glycated hemoglobin, C, cholesterol, LDL, low-density lipoprotein, HDL, high-density lipoprotein, Lp(a), lipoprotein a, BP, blood pressure, CRP, C-reactive protein
## Association between food intake and 2-h glucose in the whole sample
We analyzed the association between intake of all food groups (Table 1) and 2-h glucose after an OGTT, adjusted for sex, age, BMI, use of statins, smoking, and energy intake. The associations between intake of macronutrients (E%) and 2-h glucose were analyzed with the same model, but not adjusted for energy intake. There were no significant association between intake of any food group and 2-h glucose (Supplemental file 2). However, higher intake of mono- and disaccharides (E%) were associated with a lower 2-h glucose (Fig. 3, $$p \leq 0.02$$). This association remained after adjusting for multiple comparisons (FDR < $10\%$). All food intake 2-h glucose associations in the whole population are visualized in Supplemental Fig. 3 and Supplemental Fig. 4.Fig. 3Association between intake of mono and disaccharides (energy %) and 2-h glucose. Intake of mono and disaccharides (energy %) was associated with 2-h glucose ($$p \leq 0.02$$) after an oral glucose tolerance test in the whole sampleFig. 4Interaction between intake of food groups and metabotypes on the association with 2-h glucose. The forest plot to the left shows β-coefficients for the association between intake of food groups and 2-h glucose for the different metabotypes, adjusted for sex, age, BMI, use of statins, smoking, and energy intake. p*, p value of the metabotype-food group interaction term for the clinical metabotypes. p**, p value of the metabotype-food group interaction term for the NMR metabotypes. The scatter plots show the unadjusted correlations between 2-h glucose and three highlighted food groups for the clinical metabotypes (to the left) and the NMR metabotypes (to the right)
## Interactions between food group intake and metabotypes on the association with 2-h glucose
We investigated if there were any interactions between the metabotypes and intake of food groups on the associations with 2-h glucose, adjusted for sex, age, BMI, statin use, smoking, and energy intake. All regression coefficients, $95\%$ confidence intervals, p values, and FDR q values from these analyses can be found in Supplemental file 3. There was an interaction between the clinical metabotypes and intake of vegetables (Fig. 4, $$p \leq 0.01$$). Individuals with a low vegetable intake in the unfavorable clinical metabotype had higher postprandial blood glucose peaks compared to the favorable clinical metabotype. This suggests that the unfavorable clinical metabotype may improve their glucose tolerance by eating more vegetables. The same pattern was seen for the NMR metabotypes, but the separation of these metabotypes was less clear than for the clinical metabotypes. In the whole sample, the association between glucose tolerance and food intake depended on age for intake of fruit ($$p \leq 0.01$$) and intake of vegetables ($$p \leq 0.03$$, Supplemental file 4).
To verify the interaction between the clinical metabotypes and vegetable intake, we used objective biomarkers of vegetable intake and investigated if these interacted with the metabotypes on the association with 2-h glucose, adjusted for sex, age, BMI, statin use, smoking, and energy intake. We excluded one participant from the carotenoid analyses because of regular intake of several carotenoid containing dietary supplements. Of the six different carotenoids we analyzed in plasma, there was a significant interaction between the clinical metabotypes and plasma levels of lutein ($$p \leq 0.04$$) and zeaxanthin ($$p \leq 0.04$$). In addition, there was a significant interaction between the NMR metabotype and zeaxanthin ($$p \leq 0.02$$). Unadjusted correlation between the 2-h glucose and plasma lutein and zeaxanthin in the different metabotypes are shown in Fig. 5. All regression coefficients, $95\%$ confidence intervals and p values from the carotenoid analyses can be found in Supplemental file 5.Fig. 5Unadjusted correlations between 2-h glucose and lutein (top) and zeaxanthin (bottom) There were no other significant food group—metabotype interactions; nonetheless, there seemed to be differences between the clinical and NMR metabotypes related to some of the food groups. Although not significantly, dietary fat sources, including fish and shellfish, high-fat and fermented dairy products, and vegetable oils and oil products, seemed to have a stronger association with 2-h glucose in the unfavorable NMR metabotype than the other metabotypes. For example, intake of fish and shellfish was associated with 2-h glucose in the opposite directions for the NMR metabotypes (interaction, $$p \leq 0.17$$). A high intake was associated with lower 2-h glucose in the unfavorable and higher 2-h glucose in the unfavorable NMR metabotype. The associations between fish intake and 2-h glucose were more similar for the clinical metabotypes. Finally, higher intake of oil and oil products was associated with lower 2-h glucose levels in the unfavorable NMR metabotype, but not in the favorable NMR metabotype (interaction, $$p \leq 0.08$$). Overall, the data suggest that higher intake of foods high in polyunsaturated fatty acids (PUFA) may be associated with lower 2-h blood glucose in the unfavorable NMR metabotype, while there is no such association in the favorable NMR metabotype. In the whole sample, the association between glucose tolerance and food intake depended on sex for intake of high-fat dairy ($$p \leq 0.01$$) and intake of cis-PUFA ($$p \leq 0.04$$, Supplemental file 4).
## Interaction between intake of macronutrients and metabotypes on the association with 2-h glucose
There were no significant interactions between intake of macronutrients and metabotypes on the association with 2-h glucose (Supplemental file 6). Although not significantly, a higher intake of fiber was associated with a lower 2-h glucose for the unfavorable clinical metabotype compared to the favorable clinical metabotype (interaction, $$p \leq 0.14$$, Fig. 6). Finally, a higher intake of SFA was non-significantly associated with higher 2-h glucose in the unfavorable NMR metabotype, while the SFA-2-h glucose association was less pronounced in the other metabotypes. Fig. 6Interaction between intake of macronutrients and metabotypes on the association with 2-h glucose. The forest plot to the left shows β-coefficients for the association between intake of nutrients and 2-h glucose for the different metabotypes, adjusted for sex, age, BMI, use of statins, and smoking. p*, p value of the metabotype-nutrient interaction term for the clinical metabotypes. p**, p value of the metabotype-nutrient interaction term for the NMR metabotypes. The scatter plots show the unadjusted correlations between 2-h glucose and two highlighted nutrients for the clinical metabotypes (to the left) and the NMR metabotypes (to the right)
## Discussion
In this study, we grouped participants into metabotypes based on standard clinical cutoffs for glycemic variables and based on NMR metabolomics. For the clinical metabotypes, there was an interaction with intake of vegetables on the association with glucose tolerance. Although there were no other statistically significant interactions, the association between glucose tolerance and intake of fiber depended on the clinical metabotypes. Finally, the association between glucose tolerance and intake of saturated fatty acids and dietary fat sources, such as vegetable oils, depended non-significantly on the NMR metabotypes.
It seemed like the unfavorable metabotypes, regardless of how they were generated, would have a greater benefit on glucose tolerance of improving the diet than the favorable metabotypes. However, in all metabotypes, intake of food groups including nuts and seeds, lean fish, and low-fat dairy were associated with lower 2-h glucose (Fig. 4). This supports that advice to consume more of these food groups in food based dietary guidelines are beneficial, regardless of metabolic phenotype and disease risk. In contrast, intake of food groups such as vegetables and fish seemed to be more beneficial for the unfavorable than the favorable metabotypes. Future metabotype studies should investigate if a healthy diet is even more important to prevent lifestyle diseases in individuals with a deteriorated metabolic phenotype.
Higher intake of vegetables was associated with better glucose tolerance in the unfavorable clinical metabotype, while there was no such association in the favorable clinical metabotype. The same pattern was seen for intake of dietary fiber. There was a great difference in the average age between the favorable and the unfavorable clinical metabotype, 44 and 55 years, respectively. Moreover, the association between glucose tolerance and food intake depended on age for intake of fruit and vegetables. Hence, the age difference between the clinical metabotypes is important for the interaction between vegetable intake and the clinical metabotypes. Although we have adjusted for age in the food intake-metabotype interaction analyses, we cannot rule out residual confounding. A meta-analysis of prospective cohort studies that included participants free of T2DM at onset of the study showed a non-significant inverse association between intake of vegetables and risk of T2DM [5]. However, the range of vegetable intake in this meta-analysis was narrower than in our study. Similarly, a meta-analysis of prospective cohort studies showed that intake of dietary fiber was associated with a reduced T2DM risk only in certain geographic regions [24]. Our study suggest that the metabolic phenotype may modulate the association between vegetable intake and glucose tolerance. It is also possible that vegetable intake modulates the metabolic phenotype and thus the association with glucose tolerance. Hence, this interaction may explain conflicting results in studies examining associations between vegetable and fiber intake and T2DM risk [5]. Moreover, an 8-week whole grain diet intervention improved glucose tolerance in obese adults compared to a refined grain diet [25]. Hence, the differences between metabotypes in associations between glucose tolerance and vegetable and fiber intake are probably driven by the separation of the clinical metabotypes by glycemic variables.
The interaction between the clinical metabotypes and vegetable intake was confirmed by analyses of plasma levels of carotenoids, objective biomarkers of vegetable intake [26]. High plasma levels of both lutein and zeaxanthin were associated with better glucose tolerance in the unfavorable clinical metabotype, while there was no such association in the favorable clinical metabotype. The plasma level of zeaxanthin also had a significant interaction with the NMR metabotypes. This may reflect that there were similar patterns for the association between vegetable intake and glucose tolerance in the NMR metabotypes and the clinical metabotypes, although the interaction was not significant for the NMR metabotypes.
The association between food intake and glucose tolerance depended on the NMR metabotypes for several dietary fat sources and SFA, although these interactions were non-significant. A high intake of dietary sources of PUFA such as fish and vegetable oils and oil products were associated with improved glucose tolerance in the unfavorable NMR metabotype, but not in the favorable. Correspondingly, a high intake of SFA as well as dietary sources of SFA such as high-fat and fermented dairy products were non-significantly associated with a worsened glucose tolerance in the unfavorable NMR metabotype, but not the favorable. The distribution of male and female participants differed between the NMR metabotypes, and the association between glucose tolerance and food intake depended on sex for high-fat dairy and cis-PUFA. Although we adjusted for sex in the food intake-metabotype interaction analyses, and although the NMR metabotypes were very similar after removing variation associated with sex, there may still be residual confounding by sex. Differences in the concentration of lipoprotein particles and their lipid content were the main drivers of the separation of the favorable and unfavorable NMR metabotypes. This suggests that an improved dietary fat quality that would lower LDL-C also would improve glucose tolerance in the unfavorable NMR metabotype. One possible explanation would be that in people with elevated LDL-C, pancreatic β cells accumulate cholesterol due to uptake via the LDL receptor which is abundantly expressed in pancreatic β cells [27]. Accumulation of cholesterol in β cells causes a reduction of the cells’ glucose stimulated insulin secretion and prolonged exposure to LDL-C may lead to beta-cell death [28–30]. Hence, lowering of LDL-C will improve glucose tolerance. A sufficiently powered study is needed to confirm the non-significant interactions between metabotypes separated mainly by LDL-C and dietary fat sources with glucose tolerance.
Surprisingly, higher intake of mono- and disaccharides was associated with improved glucose tolerance in the whole study population. This may be because people with an unhealthy lifestyle tend to underreport intake of unhealthy foods, such as foods with a high content of simple sugars [31, 32]. However, this finding may also be spurious, as we did not find any other associations between intake of other groups of unhealthy foods and glucose tolerance in the whole study population.
In this study, we chose to split our study population into only two different metabotypes per strategy because splitting the data into even smaller groups would results in analyses with too low power. In addition, the two metabotypes generated per strategy differed in clinically relevant variables that made it possible to classify the metabotypes as favorable and unfavorable. However, a larger study population would have enabled the generation of more metabotypes, allowing comparison of more clearly separated groups that would have affected the analyses of metabotype-food intake interactions.
There is no consensus on how to define metabotypes; thus, the term “metabotype” is subjectively used and metabotypes are constructed to fit the aims of the individual studies [6]. Many studies have used a handful of selected metabolites related to the metabolic syndrome and cardiovascular disease to create metabotypes [6]. As an example, metabotypes based on the glucose response following an OGTT differed in BMI, body fat, triglycerides, hsCRP, insulin response, and β-cell function [33]. Moreover, clustering of participants using triglycerides, total cholesterol, HDL-cholesterol, and glucose identified three metabotypes that were given targeted dietary advice based on the biochemical characteristics of each cluster. The targeted advice largely agreed with personalized dietary advice based on individual characteristics, demonstrating that metabotypes are useful in precision nutrition [34]. Metabotypes based on a few selected variables as well as metabotypes based on omics-technologies have been used to study the relationship between metabotype and food intake on disease risk. In a metabotype characterized by a high proportion of T2DM, as well as high age, BMI and waist circumference, a low intake of fruit and a high intake of sugar-sweetened beverages was associated with T2DM. In the same study, the more healthy metabotype showed associations between meat intake and T2DM, demonstrating that different metabotypes may benefit from different dietary advice to achieve disease risk reduction [7]. Furthermore, NMR metabolomics has been used to determine metabotypes that responded to vitamin D supplementation by improving metabolic syndrome-related risk markers including CRP, insulin, and HOMA scores [35]. Finally, lipoprotein profiles from NMR metabolomics were used to study the response to fenofibrate in clusters of low, medium, and high degree of dyslipidemia. This clustering approach was better at separating those with a beneficial response to fenofibrate therapy than standard clinical methods [36].
The NMR platform that was used in this study quantifies 250 metabolites; however, the vast majority of these variables are related to lipoproteins, lipids, and fatty acids. Hence, the metabotypes based on NMR data are distinguished by a favorable and unfavorable lipid profile. The use of other untargeted and targeted metabolomics platforms covering different aspects of metabolism would have produced metabotypes characterized on other metabolites than lipids. Moreover, there were more men in the unfavorable NMR metabotype compared to the favorable metabotype. This could potentially have been a driver of both the differences in NMR metabotype characteristics, and the food intake associations. However, the variation in the NMR data introduced by sex was removed when the residuals were clustered, and the clustering of residuals approach generated very similar metabotypes as the two other clustering approaches.
It is possible that the use of LDL-C, in addition to other standard lipid variables, would generate metabotypes similar to the NMR metabotypes generated in this study. Hence, the use of NMR metabolomics may not provide us with more useful metabotypes compared to standard clinical variables. If there is a clinically relevant difference between the metabotypes, e.g., a difference in fasting glucose or LDL-C, it is not surprising that this difference is the main driver of the associations compared to fluctuations in a wider range of molecules with a less important role in determining disease risk. In other words, more research is needed to determine if the cost of creating metabotypes based on omics-technologies can be justified.
This exploratory study is limited by a low sample size that increases the probability of both positive and negative findings being due to chance. Furthermore, although we adjusted for sex and age in the analyses of interaction between metabotypes and food intake on glucose tolerance, there may still be residual confounding by sex and age. Specifically, sex and age differences between the metabotypes may contribute to bias related to body composition, dietary habits, and drug use. Moreover, we investigated interactions between food intake and 2-h glucose in an OGTT, while measuring glucose at more time points and analyzing the whole glucose response curve may have provided a better estimation of glucose tolerance. Finally, this study is observational and cannot infer causality, especially due to residual confounding and the possibility of reverse causality. The study is strengthened by robust metabotypes that remained very similar with the different clustering approaches that were applied, although we cannot exclude that this may be due to overfitting of the models. Furthermore, this study demonstrates that it is possible to generate metabotypes based on both a simple set and a more complex set of variables. However, it is not clear if the cost of doing omics-analyses in metabotyping can be justified by producing more informative metabotypes compared to metabotypes based on a few selected variables with a strong disease risk association. Finally, the association between food intake and glucose tolerance were dependent of metabotype, suggesting that a similar approach can be used to guide the design of metabotype-specific interventions in future studies in precision nutrition.
## Conclusions
The metabotypes with more unfavorable characteristics showed stronger associations between food intake and glucose tolerance than the more favorable metabotypes. Moreover, the variables used to create the metabotypes affected how the metabotypes interacted with dietary intake on the association with glucose tolerance. Metabotyping may be a useful tool in precision nutrition to find dietary interventions that will benefit specific groups of individuals.
## Supplementary Information
Additional file 1: Supplemental Figure 1.Variance explained by the first ten principal components. PC, principal component File format:.pdfAdditional file 2: Supplemental Figure 2. Separation of participants into metabotypes. A) Separation of the favorable and the unfavorable clinical metabotype based on fasting glucose (cut-off = 5.6 mmol/L), 2h OGTT glucose (cut-off = 6.5 mmol/L) and HbA1c (cut-off = 5.8 %). B) Separation of the favorable and unfavorable NMR metabotype generated by k-means clustering of scaled NMR metabolomics data directly, visualized by the first four PCs. C) Separation of the favorable and unfavorable NMR metabotype generated by k-means clustering of the first four PCs, visualized by the four PCs. D) Separation of the favorable and unfavorable NMR metabotype generated by k-means clustering of residuals from regression models of the NMR variables adjusted for sex, age, BMI, statin use and smoking status, visualized by the first four PCs. Note the similarity between panels B and C, as expected. OGTT, oral glucose tolerance test, PC, principal component. File format:.pdfAdditional file 3: Supplemental Figure 3. Associations between intake of food groups and 2h glucose in the whole population. β-coefficients, with $95\%$ confidence intervals, for the association between intake of food groups and 2h glucose in the whole population, adjusted for sex, age, BMI, use of statins, smoking and energy intake. Numbers to the right are p-values of the association. ASB, artificially sweetened beverages. File format:.pdfAdditional file 4: Supplemental Figure 4. Associations between intake of macronutrients and 2h glucose in the whole population. β-coefficients, with $95\%$ confidence intervals, for the association between intake of macronutrients (E%) and 2h glucose in the whole population, adjusted for sex, age, BMI, use of statins and smoking. Numbers to the right are p-values of the association. MUFA, monounsaturated fatty acids, PUFA, polyunsaturated fatty acids, SFA, saturated fatty acids File format:.pdfAdditional file 5: Supplemental file 1. List of NMR variables used in clustering analyses. File format:.xlsxAdditional file 6: Supplemental file 2. Association between 2h glucose and intake of food groups and nutrients in all participants. Regression coefficients, $95\%$ confidence intervals, p-values and FDR q-values for regression analyses. Association between 2h glucose and intake of food groups, adjusted for sex, age, BMI, statin use, smoking and energy intake. Association between 2h glucose and intake of nutrients, adjusted for sex, age, BMI, statin use and smoking. File format:.xlsxAdditional file 7: Supplemental file 3. Interactions between metabotypes and intake of food groups on the associations with 2h glucose. Regression coefficients, $95\%$ confidence intervals, p-values for regression analyses. FDR q-values for the metabotype-food group interaction effect. Model adjusted for sex, age, BMI, statin use, smoking and energy intake File format:.xlsxAdditional file 8: Supplemental file 4. Interaction between age and food intake on the association with 2h glucose and interaction between sex and food intake on the association with 2h glucose. Regression coefficients and p-values for regression analyses. File format:.xlsxAdditional file 9: Supplemental file 5. Interactions between metabotypes and carotenoid concentrations on the associations with 2h glucose. Regression coefficients, $95\%$ confidence intervals, p-values for regression analyses. Model adjusted for sex, age, BMI, statin use, smoking and energy intake. File format:.xlsxAdditional file 10: Supplemental file 6. Interactions between metabotypes and nutrient intake on the associations with 2h glucose. Regression coefficients, $95\%$ confidence intervals, p-values for regression analyses. FDR q-values for the metabotype-nutrient interaction effect. Model adjusted for sex, age, BMI, statin use and smoking. File format:.xlsx
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|
---
title: Does benefits-of-breastfeeding language or risks-of-formula-feeding language
promote more-positive attitudes toward breastfeeding among midwives and nurses?
authors:
- Ayumi Toda
- Keiko Nanishi
- Akira Shibanuma
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10007738
doi: 10.1186/s12884-023-05493-w
license: CC BY 4.0
---
# Does benefits-of-breastfeeding language or risks-of-formula-feeding language promote more-positive attitudes toward breastfeeding among midwives and nurses?
## Abstract
### Background
Midwives and nurses are crucial in breastfeeding support. Few studies have explored appropriate language for nursing education on breastfeeding. We assessed the impact of the language used on breastfeeding attitudes among midwives and nurses.
### Methods
A quasi-experimental study was conducted online in Japan among 174 midwives and nurses who had work experience in obstetrics or pediatrics. Participants were allocated to three groups to receive different text messages as the intervention (the benefit of breastfeeding for Group 1; the risk of formula feeding for Group 2; the importance of childcare for Group 3 as the comparison). The Japanese version of the Iowa Infant Feeding Attitude Scale (IIFAS-J) was used to assess breastfeeding attitudes before and after reading the texts. Also, participant reactions to the text were assessed by their responses to three statements. ANOVA, the chi-square test, and the t-test were used for outcome assessments.
### Results
The post-test IIFAS-J score was significantly higher than the pre-test score only for Group 1 ($p \leq 0.01$). The percentage of participants who agreed with the content of the text was $70.7\%$ in Group 1 and $48.3\%$ in Group 2. The percentage of participants who reported discomfort with the text was $34.5\%$ in Group 1 and $55.2\%$ in Group 2. No significant difference among groups existed regarding interest in the text. In all three groups, participants who agreed with the text had a higher post-test IIFAS-J score than those who disagreed with the text (6.85 points higher, $p \leq 0.01$ in Group 1; 7.19 points higher, $p \leq 0.01$ in Group 2; 8.00 points higher, $p \leq 0.02$ in Group 3). Discomfort with the text and interest in the text were associated with a significantly higher post-test IIFAS-J score in Group 1 and Group 2 but not in Group 3.
### Conclusions
“Benefits of breastfeeding” language, which conveys the information in a positive manner, appears to be more appropriate than “risks of infant formula” language for creating a positive attitude toward breastfeeding in nursing education.
### Trial registration
This study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000023322). Registered $\frac{05}{08}$/2016.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-023-05493-w.
## Introduction
Midwives and nurses play a crucial role in supporting breastfeeding [1–4], and their attitudes toward breastfeeding affect how they support breastfeeding. When midwives and nurses had neutral or negative attitudes toward breastfeeding, they often unnecessarily suggested using infant formula, leading to delayed initiation and premature cessation of breastfeeding [4]. On the other hand, mothers were more likely to initiate and continue breastfeeding when cared for by midwives and nurses with positive breastfeeding attitudes [1, 5]. Thus, midwives’ and nurses’ positive attitudes toward breastfeeding are essential in promoting breastfeeding.
Although the benefits of breastfeeding are often highlighted in health education, some health experts have criticized the presentation of breastfeeding as the intervention and formula feeding as the comparison. A systematic review of the presentation of infant feeding studies found that only $11\%$ of abstracts named formula feeding as a health risk exposure. The authors argued that skew in the communication of research findings might reduce knowledge of and support for breastfeeding among health professionals [6]. McNiel et al. argued that presenting infant formula use as a control practice when reporting the benefits of breastfeeding implicitly defined formula feeding as a normative standard. Therefore, to promote exclusive breastfeeding as a standard, they chose to express evidence on the benefits of breastfeeding as risks of infant formula [7]. In line with this practice, Stuebe argued that when breastfeeding is promoted as “breast is best,” formula feeding is implicitly suggested as “good” or “normal” [8].
There is concern regarding how people interpret breastfeeding recommendations when the recommendations are based on the risks of infant formula to their babies’ health. A study of 434 university students in the United States found no significant difference in breastfeeding intention between those who received advocacy texts with “risks of formula” language, which presented the risks of formula feeding over breastfeeding, and those who received texts with “benefits of breastfeeding” language, which highlighted the benefits of breastfeeding over formula feeding. Furthermore, respondents who read the text with risk language were more likely to rate it as less trustworthy, less accurate, and less helpful than were those who read the text that stressed the benefits of breastfeeding. The authors concluded that use of risk language might not be advantageous for health promotion and might even be counterproductive to the goals of breastfeeding advocates [9]. Other researchers expressed concerns regarding a breastfeeding campaign that focused on the risks of formula, because it might elicit shame in women [10]. Thus, message content and tone should be carefully considered [11].
In Japan, “breast is best” has been a standard message from health organizations since the Ministry of Health and Welfare launched a breastfeeding promotion campaign in 1975 [12]. Nursing textbooks usually explain the benefits of breastfeeding rather than the risks of formula feeding [13]. Although the manner in which scientific evidence on infant feeding is presented to mothers and others remains a subject of discussion, little is known about appropriate language for nursing education. Therefore, this study compared the effects of “benefits of breastfeeding” language (i.e., providing infant feeding information with breastfeeding as the intervention and formula feeding as the comparison) and “risks of formula” language (i.e., providing the same information with formula feeding as the intervention and breastfeeding as the comparison) on attitudes toward breastfeeding among Japanese midwives and nurses with work experience in obstetrics and pediatrics. We further assessed how midwives and nurses reacted to the information. Three research questions addressed were as follows:Do changes in breastfeeding attitudes differ between midwives and nurses who have read a “benefits of breastfeeding” text and those who have read a “risks of formula” text?Which version of the text produces agreement with the text, comfort with the text, and interest in the text among midwives and nurses?Are there actions to the text associated with attitudes toward breastfeeding after reading the text?
## Design and participants
This quasi-experimental study was conducted online between August 10 and October 14, 2016. Participants were recruited through a study notification published on 1) a website managed by Medicus Shuppan Publishers Co., Ltd., a large Japanese publisher in nursing and medical sciences, 2) a job search site for midwives and nurses, and 3) Facebook. Macromill, Inc., an online survey company, posted the study invitation on the abovementioned media, and eligibility was confirmed by screening questions when the participant responded. The inclusion criteria were possession of a national nurse license and previous work experience in obstetrics or pediatrics, including neonatal intensive care units and growing care units. As of 2008, applicants for a midwife license in Japan must have a national nurse license.
Participants were allocated to one of three groups: Group 1 received a “benefits of breastfeeding” text, Group 2 received a “risks of formula feeding” text, and Group 3 received a control text. Participants answered the questionnaire before and after reading the intervention text. The group allocation was not random; instead, participants were allocated by a computer system that balanced the number of participants in each group. Specifically, a new participant was assigned to the group with the smallest number of participants who had completed the pre-test questionnaire. After completing the pre-test questionnaire, the text of the allocated group was presented to the participants, after which they were invited to a post-test questionnaire. The whole process took approximately 30 minutes. Although participants were encouraged to read the text before proceeding to the post-test questionnaire, skipping the reading was possible and not monitored.
## Blinding
As the intervention, participants were asked to read the text assigned to their group. Therefore, participants were not blinded to group allocation and could likely determine the allocation from the content of the text. The authors were blinded to the group allocation until the outcomes were analyzed and could not influence the responses.
## Texts
The texts for Group 1 and Group 2 were created in Japanese by using materials developed in English [9]. Wallace and Taylor developed two texts to convey the same information on infant feeding: one using benefit language and the other using risk language. For example, the message “breastfed children are less likely to suffer from infectious illnesses and their symptoms” in the benefits text was converted to “formula-fed children are more likely to suffer from infectious illnesses and their symptoms” in the risks text. The present authors created the Japanese version of these texts with the permission of Wallace and Taylor, and the back-translation method was used for the translation. Specifically, the first author translated the original English version into Japanese, and a Japanese–English bilingual graduate student of public health who was blinded to the original version back-translated it into English. The authors compared the back-translated version and original English version. In the event of a discrepancy in meaning, the first author revised the Japanese translation. The process was repeated until the authors agreed that the Japanese translation was semantically equivalent to the original.
Before translation, the authors checked the relevance of the content of the texts. First, the authors assessed if the textual information was consistent with existing evidence in 2016. Systematic reviews and a policy statement published after the development of the original English version were examined [14–21]. No corrections were made to the texts, as all statements were consistent with evidence discussed in the reviews. After the Japanese translation was completed, the content of the texts was reviewed by a pediatrician and a breastfeeding researcher in Japan who were not part of the research team. They confirmed that the texts were relevant for midwives and nurses in Japan. The final versions of the back-translated texts are shown in Supplementary Material 1.
The control group (i.e., Group 3) received a brochure on healthy pregnancy and delivery developed by the Ministry of Health, Labour and Welfare [22]. The brochure was published online on the Ministry’s website for free use. The authors chose it as a control text because it did not mention infant feeding and was about the same length as the intervention texts.
## Attitude toward breastfeeding
Attitude toward breastfeeding was measured twice in the pre-test and post-test questionnaires by the Iowa Infant Feeding Scale-Japanese version (IIFAS-J) [23]—a Japanese version of the Iowa Infant Feeding Attitude Scale developed by de la Mora and Russell in the United States [24]. The IIFAS-J was tested for reliability and validity among 673 mothers, and one item was omitted from the original 17-item version [23]. The IIFAS-J thus consists of 16 items rated on a 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree). The total IIFAS-J score ranges from 16 to 80; lower scores indicate a more positive attitude toward formula feeding, while higher scores indicate a more positive attitude toward breastfeeding [23]. In this study, the *Cronbach alpha* values for pre-test and post-test surveys were 0.78 and 0.80, respectively, indicating that the scale was sufficiently reliable.
## Nurse reactions to the texts
The reaction of participants to the texts was assessed by their response to the three statements in the post-test questionnaire: “I can agree with the content of the text,” “The text makes me uncomfortable,” and “I’m interested in the text.” For all items, participants were asked to respond by using a 5-point Likert-type scale, and responses were dichotomized after checking the distribution of answers (Supplementary Table 1). The responses “strongly agree” and “agree” to the first statement were categorized as agreement with the text; all other responses were categorized as disagreement with the text. The responses “strongly agree,” “agree,” and “neither” to the second statement were categorized as comfort with the text; all other responses were categorized as discomfort with the text. The responses “strongly agree” and “agree” to the last statement were categorized as interest in the text; all other responses were categorized as lack of interest in the text.
## Background characteristics of participants
The pre-test questionnaire assessed the sociodemographic and employment characteristics of the participants. In addition to age, gender, total number of years of work experience as a nurse, possession of an advanced license in addition to the nursing license, education level, having a child (children), and recognition of the benefits of breastfeeding and risks of formula feeding were measured [25–27].
## Sample size estimation
A previous study of pregnant women reported a mean IIFAS-J score of 62 for those who intended to breastfeed exclusively. We aimed to increase the IIFAS-J score by 3 points by using benefit language to present the latest infant feeding information. We expected that use of risk language to present the same information would not have an equivalent effect. Specifically, we assumed that the control group would have a mean score of 62 at both the pre-test and post-test and designed the intervention for Group 1 to increase the mean score from 62 to 65 (SD 5). To detect a difference in pre-test score between Group 1 and the control with $80\%$ power and a two-sided alpha of $5\%$, we estimated that a minimum sample size of 44 for each group would be necessary (http://clincalc.com/Stats/SampleSize.aspx). To account for the possibility of drop-outs, we enrolled a total of 147 participants.
## Data analysis
Four assumptions were tested: 1) the texts using benefit language and risk language would increase IIFAS-J scores, 2) the improvement in IIFAS-J score would be greater for Group 1 (i.e., those who read the benefits text) than for the other groups, 3) benefit language would be received more favorably than risk language, and 4) participants who reacted favorably to a text would have a more positive attitude toward breastfeeding in post-test measurement than did those who read the risks text. Analysis of Variance (ANOVA) or the chi-square test of independence was used to assess between-group differences in nurse characteristics. The paired t-test was used to compare mean differences in pre-test and post-test IIFAS-J scores within each group. ANOVA was used to compare mean IIFAS-J scores among groups at baseline and after the intervention, and change in IIFAS-J score after the intervention. The chi-square test of independence was used to compare how participants in the three groups reacted to the texts. The t-test was used to assess the association between receiving the text favorably and post-test IIFAS-J scores. Stata version 13.1 was used to analyze all the data (Stata Corp. LLC, College Station, Texas, USA), and a p-value less than 0.05 was considered to indicate statistical significance.
## Participant flow
Figure 1 shows the participant flow diagram. Of the 3487 individuals who accessed the study’s website, 630 proceeded to receive detailed information about study participation and responded to the screening questions. Among them, 397 did not meet the inclusion criteria and were not invited to the study. Consequently, the 233 midwives and nurses considered eligible were allocated to one of the three groups after informed consent was obtained. Ultimately, 174 participants completed both the pre-test and post-test questionnaires and were included in the analysis. Fig. 1Participant flow diagram
## Characteristics of participants
Table 1 shows the characteristics of participants by group (that is, the allocation of interventions). Among the groups, the characteristics of the participants were not significantly different, except for experience in the workplace. More participants in Group 1 had experience working in obstetric wards, as compared with Group 2 and Group 3 (i.e., the control) ($$p \leq 0.02$$). More participants in Group 1 had experience working in pediatric wards ($p \leq 0.01$) and neonatal intensive care units/growing care units ($p \leq 0.01$), as compared with the other groups. All 174 participants reported hearing of the benefits of breastfeeding, and majority reported hearing of the risks of formula feeding. Table 1Characteristics of participantsGroup 1a ($$n = 58$$)Group 2b ($$n = 58$$)Group 3c ($$n = 58$$)P-valuedn (%)Mean [SD]n (%)Mean [SD]n (%)Mean [SD]Age35.6 [7.5]34.9 [7.2]34.3 [6.6]0.66Total number of years of work experience as a nurse12.0 [7.6]11.1 [6.9]10.0 [6.3]0.32Possession of advanced license in addition to nursing license Midwife license32 (55.2)33 (56.9)29 (50.0)0.74 Public Health Nurse license30 (51.7)31 (53.4)24 (41.4)0.37Educational experience0.76 University/Master’s degree34 (58.6)32 (55.2)30 (51.7) 3-year curriculum24 (41.4)26 (44.8)28 (48.3)*Have a* child/children?0.31 Yes39 (67.2)32 (55.2)32 (55.2)Heard of breastfeeding benefits? Yes58 (100.0)58 (100.0)58 (100.0)Heard of infant formula risks?0.67 Yes51 (87.9)48 (82.8)48 (82.8)aGroup 1 received a text highlighting the benefits of breastfeedingbGroup 2 received a text highlighting the risks of formula feedingcGroup 3 received a control textdAnalysis of variance for continuous variables; chi-square test for categorical variables
## Effect of texts on attitudes toward breastfeeding
The paired t-test showed that the mean (SD) IIFAS-J score improved significantly, from 63.5 (7.3) to 65.3 (8.0), in Group 1 (the difference between the pre-test and post-test was 1.8 points, $p \leq 0.01$). In contrast, the IIFAS-J scores did not improve in Group 2 or Group 3. ANOVA showed that change in mean IIFAS-J score after reading the text did not significantly differ among the three groups ($$p \leq 0.21$$) (Table 2).Table 2Pre-test and post-test IIFAS-J scoresGroup 1a ($$n = 58$$)Group 2b ($$n = 58$$)Group 3c ($$n = 58$$)P-valuedMean [SD]Mean [SD]Mean [SD]Pre-test63.5 [7.3]64.4 [7.5]63.3 [8.7]0.70Post-test65.3 [8.0]65.1 [7.8]64.1 [7.8]0.66Difference1.8 [3.9]0.7 [3.8]0.8 [3.9]0.21P-valuee< 0.010.160.13aGroup 1 received a “benefits of breastfeeding” textbGroup 2 received a “risks of infant feeding” textcGroup 3 received a control textdP-value for analysis of varianceeP-value for paired t-test within group
## Participants’ reactions to the texts
The texts elicited different reactions among the participants (Table 3). While $70.7\%$ of participants who received the benefits text (i.e., Group 1) agreed with the text, only $48.3\%$ of those who received the risks text (i.e., Group 2) agreed with the text. Similarly, while only $34.5\%$ of participants who received the benefits text (i.e., Group 1) reported discomfort with the text, $55.2\%$ of those who received the same information in the risks text (i.e., Group 2) reported discomfort with the text. In Group 1, $79.3\%$ of participants reported that the text was interesting, while $74.1\%$ of the participants in Group 2 reported that the text was interesting. In the control group (i.e., those receiving a text unrelated to infant feeding), the vast majority reported that they agreed with the text, some reported discomfort, and most found it interesting. When the three groups were compared, there were significant differences in agreement ($p \leq 0.01$) and discomfort ($p \leq 0.01$) with the text, but no significant difference in interest in the text. Table 3Reaction of participants to the study textsGroup 1a ($$n = 58$$)Group 2b ($$n = 58$$)Group 3c ($$n = 58$$)P-valuedn (%)n (%)n (%)< 0.01Agreement with text41 (70.7)28 (48.3)53 (91.4)< 0.01Uncomfortable with text20 (34.5)32 (55.2)8 (13.8)0.56Interested in text46 (79.3)43 (74.1)41 (70.7)aGroup 1 received a text highlighting the benefits of breastfeedingbGroup 2 received a text highlighting the risks of formula feedingcGroup 3 received a control textdChi-square test
## Difference in post-test IIFAS-J scores in relation to reaction to the text
Differences in post-test IIFAS-J scores were assessed within each group in relation to agreement with (Fig. 2a), discomfort with (Fig. 2b), and interest in (Fig. 2c) the text. In all three groups, participants who agreed with the text had higher post-test scores (Fig. 2a). Specifically, in Group 1, the post-test score was 6.85 points higher among participants who agreed with the text than among those who did not ($p \leq 0.01$). Similarly, in Group 2, the post-test score was 7.19 points higher among participants who agreed with the text than among those who did not ($p \leq 0.01$). In Group 3, the mean post-test score was higher among participants who agreed with the text than among those who did not (8.00 points, $p \leq 0.02$). Discomfort with the text was associated with lower post-test scores in Group 1 and Group 2 (Fig. 2b). In Group 1, the mean post-test score was 7.35 points lower for participants who reported discomfort with the text than for those who did not ($p \leq 0.01$). Likewise, in Group 2, the mean post-test score was 6.86 points lower for participants who reported discomfort with the text than for those who did not ($p \leq 0.01$). In Group 3, discomfort with the text was not significantly associated with post-test score ($$p \leq 0.79$$). Finally, interest in the text was associated with higher post-test IIFAS-J scores in Group 1 and Group 2 (Fig. 2c). In Group 1, the mean post-test score was 5.43 points higher for participants who were interested in the text than for those who were not ($p \leq 0.05$). In Group 2, the mean post-test score was 9.80 points higher for participants who were interested in the text than for those who were not ($p \leq 0.01$). In Group 3, interest in the text was not significantly associated with post-test score ($$p \leq 0.15$$).Fig. 2Comparison of post-test IIFAS-J scores between those who received the text favorably and those who did not within each group. a Mean scores of post-test IIFAS-J scores (compared those who agreed with the text and those who disagreed with the text) b Mean scores of post-test IIFAS-J scores (compared those who reported discomfort with the text and those who did not) c Mean scores of post-test IIFAS-J scores (compared those who were interested in the text and those who lacked interest)
## Discussion
The present findings suggest that “benefits of breastfeeding” language is better than “risks of infant formula” language for producing a positive attitude to breastfeeding in nursing education. Only the benefits text significantly improved the attitude to breastfeeding. Second, the benefits text was received more favorably than the risks text. Specifically, most participants who received the benefits text agreed with it, while most participants who received the risks text found it uncomfortable. Moreover, agreement and lack of discomfort with a text were associated with more-positive attitudes toward breastfeeding after reading it.
Comparison of pre-test and post-test IIFAS-J scores indicated that only the benefits text significantly improved midwives’ and nurses’ attitudes toward breastfeeding; the same information presented with risk language was not effective. This finding was consistent with a study of undergraduate students [9]. The authors rejected the assumption that risk language advocacy would be more effective in promoting breastfeeding intention and suggested that such language might create a backlash against its negative tone. Our study findings suggest that their ideas might be applicable to midwives and nurses with work experience in obstetrics, neonatology, and pediatrics. We found that risk language did not enhance midwives’ and nurses’ attitudes toward breastfeeding. Risk language was not only ineffective, it was received less favorably than benefit language. Midwives and nurses were less likely to agree and more likely to feel discomfort when infant feeding information was presented in risk language.
When change in IIFAS-J score was compared among the three groups, neither of the intervention groups (i.e., those receiving the benefits text and the risks text) showed more improvement than the group receiving the control text. Most of the participants reported that they had heard of the benefits of breastfeeding and the risks of infant formula before participating in the study. Nurses who are knowledgeable about breastfeeding often have a positive attitude toward breastfeeding [28]. The mean pre-test IIFAS-J scores of all the present groups were higher than the mean IIFAS-J score, 61.0, in a previous study of 781 pregnant women in their third trimester [23]. Therefore, midwives’ and nurses’ preliminary knowledge of infant feeding and their already positive attitude to breastfeeding might have obscured the effects of the present intervention texts.
Participants who favorably received the infant feeding text (i.e., were in agreement and felt no discomfort) had higher post-test IIFAS-J scores than did those who felt less favorably about the text in both intervention groups. This finding suggests that the reader’s feelings might impact the effectiveness of the intervention. Participants received the information more favorably when it was presented as the benefits of breastfeeding; thus, presenting infant feeding information that focuses on the benefits of breastfeeding may be more effective for promoting breastfeeding support among midwives and nurses. In addition, when any information is presented to midwives and nurses as a risk of infant formula use, it should be presented in a way that is agreeable and does not cause discomfort.
When interpreting our results, it should be noted that our study evaluated the language and not the content of the texts. Nursing education on breastfeeding must encompass more than the benefits of breastfeeding. Lactating mothers expect health professionals to provide sensitive responses to their emotional needs, in addition to relevant knowledge and practical advice [29, 30]. Thus, nursing education to support breastfeeding should encompass a wide range of supporting techniques, such as counselling skills that encourage empathy when working with lactating mothers [31]. Our study findings suggest that participants’ feelings about educational material, and not simply the information itself, might affect the effectiveness of such material. Future studies should explore how to best present infant feeding information to midwives and nurses as part of a comprehensive curriculum that encompasses all the necessary skills for breastfeeding support.
There are several other limitations of the study. The present findings might also be affected by selection bias because of the nature of online surveys [32] and the low response rate. As is the case for many online surveys, we know nothing about participant characteristics beyond the background data we collected. Therefore, the participants may have had characteristics that differed from those of midwives and obstetric and pediatric nurses in Japan. Also, participants were likely to be frequent users of internet services and social media and to be interested in infant feeding support. Next, participants were not forced to read the intervention texts; therefore, some participants might have skipped reading the intervention materials, which could have reduced the effectiveness of the intervention. Finally, the current study was conducted more than 6 years ago. After the data were collected, Japan’s Ministry of Health, Labor and Welfare revised its guidelines for health professionals on the feeding of infants and young children [33] and removed the term “breastfeeding promotion” from the text, in consideration of the feelings of mothers who choose to formula feed. Therefore, midwives and nurses may be more sensitive in using risk language than when we conducted the study.
Notwithstanding these limitations, the current study has value in assessing the language used in nursing education. We conclude that a text that presented the benefits of breastfeeding improved breastfeeding attitudes and that most midwives and nurses received it favorably. We found no advantage in using risk language to present infant feeding information. Future studies should attempt to identify the most appropriate way to convey infant feeding information as part of comprehensive nursing education to improve breastfeeding support.
## Conclusions
Only the benefits text significantly improved the attitude of midwives and nurses to breastfeeding. In addition, the benefits text was received more favorably than the risks text. Moreover, agreement and lack of discomfort with a text were associated with more-positive attitudes toward breastfeeding after reading it. Therefore, “benefits of breastfeeding” language, which conveys the information in a positive manner, appears to be more appropriate than “risks of infant formula” language for producing a positive attitude to breastfeeding in nursing education. If information is to be presented as a risk of formula feeding, it should be delivered in a way that is agreeable and less likely to cause discomfort.
## Supplementary Information
Additional file 1.Additional file 2: Supplementary Table 1. Responses to items regarding participant reactions to the texts.
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|
---
title: 'Association between antidepressant use and liver fibrosis in patients with
type 2 diabetes: a population based study'
authors:
- Lin Shi
- Fangyuan Jia
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10007740
doi: 10.1186/s13098-023-01016-x
license: CC BY 4.0
---
# Association between antidepressant use and liver fibrosis in patients with type 2 diabetes: a population based study
## Abstract
### Background
The prevalence of liver fibrosis among diabetic patients is increasing rapidly. Our study aims at exploring the relationship between antidepressant use and liver fibrosis in diabetic patients.
### Methods
We conducted this cross-sectional study through the cycle of National Health and Nutrition Examination Survey (NHANES) 2017–2018. The study population were consisted of patients with type 2 diabetes and reliable vibration-controlled transient elastography (VCTE) results. The presence of liver fibrosis and steatosis were assessed by the median values of liver stiffness measurement (LSM) and controlled attenuation parameter (CAP), respectively. Antidepressants included selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants (TCAs), serotonin and norepinephrine reuptake inhibitors (SNRIs) and serotonin antagonists and reuptake inhibitors (SARIs). Patients with evidence of viral hepatitis and significant alcohol consumption were excluded. Logistic regression analysis was performed to evaluate the association between antidepressant use and both steatosis and significant (≥ F3) liver fibrosis after adjustment for potential confounders.
### Results
Our study population consisted of 340 women and 414 men, of whom 87 women($61.3\%$) and 55($38.7\%$) men received antidepressants. The most commonly used antidepressants were SSNIs($48.6\%$), SNRIs($22.5\%$) and TCAs($12.7\%$), followed by SARIs($10.6\%$) and other antidepressants($5.6\%$). 165 participants had significant liver fibrosis by VCTE, with a weighted overall prevalence of $24\%$($95\%$ CI 19.2–29.5). In addition, 510 patients had evidence of hepatic steatosis by VCTE with a weighted overall prevalence of $75.4\%$($95\%$ CI 69.2–80.7). After adjusting confounders, no significant association was observed between antidepressant use and significant liver fibrosis or cirrhosis.
### Conclusions
In conclusion, in this cross-sectional study, we found that antidepressant drugs was not associated with liver fibrosis and cirrhosis in patients with type 2 diabetes in a nationwide population.
## Introduction
The global prevalence and incidence of diabetes and mental health problems are increasing rapidly, especially the high risk of depression symptoms for diabetic patients compared to those without diabetes [1, 2]. Comorbid diabetes and depression are associated with increased mortality, such as cardiovascular or kidney disease [3, 4]. Moreover, the coexistence of diabetes and depression has a greater impact on depression when combined with other diseases [2]. On the other hand, non-alcoholic fatty liver disease (NAFLD) is very common among diabetic patients. It was reported that 60–$70\%$ of patients with type 2 diabetes(T2DM) have non-alcoholic fatty liver disease (NAFLD), and nearly $15\%$ have evidence of advanced liver fibrosis [5, 6]. In addition, a study of a nationally representative sample of US adults showed that depression was independently associated with the occurrence of NAFLD [7]. Therefore, patients with T2DM are at a high risk of combinational NAFLD or liver fibrosis and depression, and the complex interplay of those diseases is not fully understood.
Depression disorders among patients with T2DM could be effectively treated by antidepressants, as has been reported in some studies [2, 8]. However, some recent studies have shown that antidepressant use might be a risk factor for the new onset of T2DM, especially tricyclic antidepressant use [9, 10]. Moreover, an increasing linear relationship between the duration of antidepressant use and T2DM risk was observed in a recent meta-analysis [9].In addition, NAFLD is common in T2DM, and patients with T2DM suffer from an increased risk of liver mortality. In patients with viral hepatitis, antidepressants were considered to play a protective role in liver fibrosis progression and reduce the risk of developing cirrhosis [11, 12].Considering the increased prevalence of depression disorders, antidepressant use and NAFLD among T2DM patients, understanding the relationship between antidepressant use and liver fibrosis in patients with type 2 diabetes is critical.
In fact, few studies focused on the relationship between antidepressant use and liver fibrosis in patients with T2DM. In our study, we extracted data from an unselected sample of adults with T2DM from the 2017–2018 cycle of the National Health and Nutrition Examination Survey (NHANES) to examine the association between antidepressant use and liver fibrosis.
## Study population
This study was conducted on the basis of the National Health and Nutrition Examination Survey (NHANES) 2017–2018. The NHANES is conducted in the United States by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). All the data in the NHANES were collected by unified trained professional personnel through household interviews or an examination conducted in a mobile examination center. The survey consists of cross-sectional interviews, examinations and laboratory data collected from a complex multistage, stratified, clustered probability sample representative of the US population. The Institutional Review Board of the CDC approved the survey protocol. All participants provided informed consent. We were exempt from IRB approval from our institution as the dataset used in the analysis was completely deidentified. The participants were adults aged 18 years or older with type 2 diabetes in 2017–2018. In our analysis, the exclusion criteria were as follows: [1] participants with positive serum hepatitis B surface antigen or positive serum hepatitis C antibody; [2] participants with significant alcohol consumption(≥ 20 g/day for men and ≥ 10 g/day for women); [3] participants without complete vibration controlled transient elastography(VCTE); and [4] participants with possible type 1 diabetes(defined as a diagnosis at age < 30 years and the use of insulin as the only anti-diabetic therapy) [13].
## Clinical and laboratory variables
A series of potential confounders in the association of antidepressant use and liver fibrosis were extracted from the NHANES database, including demographic, clinical and laboratory data. Demographic variables included age, sex, ethnicity(Hispanic, non-Hispanic white, non-Hispanic black, or other races), the ratio of family income to poverty threshold, smoking habits and alcohol habits. Body measurements such as body mass index (BMI) and blood pressure were included. Past medical history, including ever diagnosis and treatment of coronary heart disease, stroke and hypertension was also included. The diagnosis of diabetes was based on any of the following criteria: [1] A self-reported diagnosis of diabetes. [ 2] use of anti-diabetic drugs. [ 3] a hemoglobin A1c (HbA1c) level ≥ $6.5\%$ (48 mmol/mol) [13].
Laboratory variables included alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltranspeptidase (GGT), total bilirubin(TBIL), glycated hemoglobin(HbA1c), total cholesterol, high density lipoprotein (HDL) and cholesterol. Laboratory methods for measurements of these values were reported in detail elsewhere [13, 14].
## Antidepressant exposure and liver fibrosis assessment
During the in-home questionnaire, trained interviewers reviewed participants' pill bottles for prescription and nonprescription medications and supplements reported to have been taken in the previous month [15]. Selective serotonin reuptake inhibitors (SSRIs) included escitalopram, fluoxetine, citalopram, fluvoxamine, sertraline, paroxetine, and duloxetine. Tricyclic antidepressant (TCA) prescriptions included amoxapine, amitriptyline, protriptyline, nortriptyline, imipramine, desipramine, doxepin, and trimipramine. Serotonin and norepinephrine reuptake inhibitors (SNRIs) included duloxetine and venlafaxine. Serotonin antagonists and reuptake inhibitors (SARIs) included trazodone and buspirone. Other antidepressants included mirtazapine and bupropion. The interviews, data collection and data processing methods were performed under the NHANES protocol.
Vibration controlled transient elastography(VCTE) was used and validated in some previous studies concerning liver fibrosis assessment in participants with nonalcoholic fatty liver disease. The detailed methods and steps of VCTE have been reported elsewhere [16, 17]. In our present study, we only included participants with complete VCTE assessment, which has been reported elsewhere [13]. For our analysis, we defined liver stiffness measurement(LSM) values of 8.0 kPa [18, 19] or higher and 13.1 kPa [19] as significant fibrosis (≥ F2) and cirrhosis (≥ F4) according to previous studies. Moreover, participants who had controlled attenuation parameter(CAP) values of 274 dB/m or higher and 302 dB/m were considered to have steatosis (≥ S1) and severe steatosis (≥ S3) [13].
## Statistical analysis
The PASS software (version 11) was used to calculate sample size. As a cross-sectional study, the overall prevalence of depression among diabetic patients was approximately $20\%$ in previous studies [9]. Therefore, $20\%$ was set as the estimated prevalence of depression among diabetic patients. The allowable error of the overall estimated proportion was set as 0.03. The significance level was set at 0.05, and a two-sided interval was used. Based on PASS software, the sample size was calculated as 715, which is lower than that in our study [20].
All analyses were conducted by R software(4.1.1). Due to the complex design of the NHANES survey, we followed the recommendations of the NHANES and a two-year-cycle weights were appropriately used in our study for each analysis. Categorical variables are expressed as numbers and weighted proportions. Continuous variables are presented as weighted means ± standard error (SE).
All enrolled participants were separated into two groups: antidepressant use group and no-antidepressant use group. The characteristics between two groups were compared by sample weighted linear regression for continuous variables and the design-adjusted Rao-Scott chi-square test for categorical variables. To further examine the association between antidepressant exposure and liver fibrosis, we applied two weighted multivariable linear regression analyses. In Model 1,we only adjusted for covariates including age, sex and race. In Model 2, we further adjusted Model 1 plus BMI and laboratory variables including ALT, AST, GGT, triglycerides and HbA1c. The relationship between antidepressant use and liver steatosis was also examined using the two models. A two-tailed P value < 0.05 was considered statistically significant.
## Baseline characteristics of the study population
A total of 5533 individuals aged over 18 years with complete MEC visits during interviews in the2017-2018 NHANES cycle were identified as initial samples. Then we excluded participants without diabetes or type 1 diabetes and viral hepatitis and significant alcohol intake as previously described. Finally, we enrolled participants with complete VCTE data, leading to a population of 754 patients as our final study population. The flow chart of our study is shown in Fig. 1.Fig. 1The flow chart of our study Our study population comprised of 340 women and 414 men, of whom 87 women ($61.3\%$) and 55 ($38.7\%$) men received antidepressants. A significantly higher BMI was observed in the antidepressant use group (33.6 ± 7.4 vs. 31.8 ± 7.5). In our study, $14.5\%$ of patients were lean (BMI < 25 kg/m2), $29.7\%$ were overweight (BMI 25–29.9 kg/m2) and $55.8\%$ were obese (BMI ≥ 30 kg/m2). Women more frequently received antidepressants than men. The most commonly used antidepressants were SSNIs ($48.6\%$), SNRIs ($22.5\%$) and TCAs ($12.7\%$), followed by SARIs ($10.6\%$) and other antidepressants ($5.6\%$). A total of 165 participants had significant liver fibrosis by VCTE for an overall weighted prevalence of $24\%$ ($95\%$ CI 19.2–29.5). Moreover, 510 patients had evidence of liver steatosis by VCTE with an overall weighted prevalence of $75.4\%$ ($95\%$ CI 69.2–80.7).
We compared the clinical and laboratory variables between the antidepressant use group and the non-antidepressant use group in Table 1. There was no significant in age between the two groups, and non-Hispanic White patients tended to received more antidepressants. Table 1Features of the study population according to current antidepressant useNon-antidepressant useAntidepressant useP-valueN612142Age(years)61.9 ± 13.061.5 ± 13.40.806Male(%)359 ($58.7\%$)55 ($38.7\%$) < 0.001Race/ethnicity (%) < 0.001 Non-Hispanic white171 ($27.9\%$)69 ($48.6\%$) Non-Hispanic Black135 ($22.1\%$)34 ($23.9\%$) Mexican American102 ($16.7\%$)11 ($7.7\%$) other204 ($33.3\%$)28 ($19.7\%$)BMI(kg/m2)31.8 ± 7.533.6 ± 7.40.003Waist circumference (cm)107.9 ± 16.2111.4 ± 15.50.02Laboratory variables ALT(IU/L)24.5 ± 17.519.2 ± 9.9 < 0.001 AST(IU/L)22.6 ± 14.219.7 ± 8.00.009 Albumin(g/L)40.1 ± 3.138.7 ± 4.0 < 0.001 GGT(IU/L)39.9 ± 48.434.7 ± 46.40.015 HDL(mg/dL)48.7 ± 13.649.0 ± 14.60.965 LDL(mg/dl)2.87 ± 0.042.73 ± 0.060.05Total cholesterol (mg/dL)179.4 ± 45.0172.4 ± 45.80.019Triglycerides (mg/dL)135.0 ± 116.3174.6 ± 178.20.032HbA1c (%)7.2 ± 1.67.1 ± 1.40.271LSM(kPa)7.2 ± 5.97.5 ± 9.00.391CAP(dB/m)302.1 ± 55.9304.3 ± 55.70.752Fibrosis≧2139 ($22.7\%$)26 ($18.3\%$)0.253Fibrosis≧443 ($7.0\%$)8 ($5.6\%$)0.552Steatosis≧1411 ($67.2\%$)99 ($69.7\%$)0.557Steatosis≧3315 ($51.5\%$)73 ($51.4\%$)0.989BMI body mass index, ALT alanine transaminase, AST aspartate transaminase, GGT gamma-glutamyl transpeptidase, HDL high density lipoprotein, LDL low density lipoprotein, HbA1c glycosylated hemoglobin, LSM liver stiffness measurement, CAP controlled attenuation parameter; Some liver function markers, including ALT, AST and TBIL, were significantly lower in the antidepressant use group. However, no significant differences were observed in CAP and LSM between the two groups. Interestingly, lipid parameters including LDL, total cholesterol and triglycerides, were significantly lower in the antidepressant use group, whereas HDL cholesterol was not significantly different between the two groups.
## Liver fibrosis and steatosis
To examine the association between liver fibrosis and antidepressant use, we then conducted sample weighted multivariable logistic regression models (Table 2). We also identified factors related to steatosis by above models (Table 3).Table 2Association of antidepressant with risk of significant fibrosis or cirrhosis by weighted regressionVariablesSignificant fibrosis (≥ F2)Cirrhosis (F4)OR$95\%$CIP-valueOR$95\%$CIP-valueSex1.30.27–6.350.641.290.38–4.980.79Age1.010.99–1.040.341.010.98–1.050.68Race------Non-Hispanic whiteRef--Ref-Non-Hispanic Black0.630.53–0.910.031.440.99–1.990.25Mexican American1.820.87–3.800.111.550.75–3.200.23Other1.950.21–3.130.191.750.98–2.770.2BMI1.141.1–1.180.0011.171.08–1.260.01ALT0.990.94–1.040.990.870.79–1.960.21AST1.21.01–1.030.031.011.01–1.020.05GGT1.011.00–1.030.11.011.00–1.010.09HbA1c1.10.97–1.240.201.090.91–1.380.5Triglycerides1.021.01–1.040.040.990.99–1.000.99LDL1.080.73–1.60.680.980.96–1.010.19Antidepressant use0.530.2–1.580.310.490.07–2.410.51BMI body mass index, ALT alanine transaminase, AST aspartate transaminase, GGT gamma-glutamyl transpeptidase, LDL low density lipoprotein, HbA1c glycosylated hemoglobinTable 3Association of antidepressant with risk of any or severe steatosis by weighted regressionVariablesAny steatosis (≥ S1)*Severe steatosis* (S3)OR$95\%$CIP-valueOR$95\%$CIP-valueSex1.510.88–2.360.291.811.03–3.010.04Age1.020.99–1.050.251.010.99–1.150.59Race––––––Non-Hispanic whiteRef––Ref––Non-Hispanic Black0.510.07–0.890.040.490.17–2.890.1Mexican American1.540.78–3.050.211.870.65–3.00.33Other1.771.0–2.820.611.580.77–2.980.79BMI1.151.07–1.220.011.111.00–1.200.06ALT1.021.01–1.040.031.031.01–1.080.05AST0.960.89–1.00.120.990.99–1.030.61GGT1.00.99–1.010.771.00.99–1.000.38HbA1c1.211.05–1.490.031.270.99–1.510.32Triglycerides1.011.0–1.02 < 0.0011.011.0–1.02 < 0.001LDL0.870.71–1.070.180.810.71–1.110.19Antidepressant use0.790.41–1.840.390.870.39–1.550.59BMI body mass index, ALT alanine transaminase, AST aspartate transaminase, GGT gamma-glutamyl transpeptidase, LDL low density lipoprotein, HbA1c glycosylated hemoglobin; After adjusting for confounding factors, no significant association was observed between antidepressant use and significant liver fibrosis (OR: 0.53; $95\%$ CI: 0.2–1.58) or cirrhosis (OR: 0.49; $95\%$ CI: 0.07–2.41). In the analysis, we also identified that BMI was significantly associated with significant fibrosis (OR: 1.14; $95\%$ CI: 1.1–1.18) or cirrhosis (OR: 1.17; $95\%$ CI: 1.08–1.26). Higher triglyceride was also associated with fibrosis (OR: 1.02, $95\%$ CI: 1.01–1.04) but not with cirrhosis (OR: 0.99, $95\%$ CI: 0.99–1.00). Non-Hispanic black individuals were protected from liver fibrosis (OR: 0.63; $95\%$ CI: 0.53–0.91) but not in cirrhosis (OR: 1.44; $95\%$ CI: 0.99–1.99). Detailed regression results are displayed in Table 2.
Considering the severity of hepatic steatosis, BMI and HbA1c were independent risk factors or any steatosis (OR: 1.15, $95\%$ CI: 1.07–1.22; OR: 1.21, $95\%$ CI: 1.05–1.49, respectively) but were not associated with severe steatosis(OR: 1.11, $95\%$ CI: 1.00–1.12; OR: 1.27, $95\%$ CI: 0.99–1.51, respectively) (Table 3). Moreover, non-Hispanic black individuals were protected from steatosis (OR: 0.51; $95\%$ CI: 0.07–0.89) but were not associated with severe steatosis(OR: 0.49; $95\%$ CI: 0.17–2.89). Similarly, we found no significant association between antidepressant use and liver steatosis(OR: 0.87; $95\%$ CI: 0.39–1.55). Other regression results are shown in Table 3.
## Disscussion
In the present study, we enrolled 754 patients and found that after adjusting for confounding factors, antidepressant use was not statistically associated with liver fibrosis and steatosis among patients with T2DM. In our study, we included several main types of antidepressant medications, such as SSNIs, SNRIs, and TCAs. Considering the increasing incidence of depression disorders in T2DM patients, our study improves the understanding of the association between antidepressant drugs and liver fibrosis in patients with type 2 diabetes.
Patients with T2DM have long been plagued with NAFLD and mental disorders according to data from the famous INTERPRET-DD study [1]. In that international and multicenter analysis, the researchers found that $10.6\%$ of diabetic patients were diagnosed with current major depressive disorder, and $17\%$ of diabetic patients reported moderate/severe depressive symptomatology. Thus, mental disorders are prevalent among diabetic patients. On the other hand, NAFLD has also been a huge burden on public health and affects most individuals with T2DM. Moreover, the incidence of NAFLD-induced liver fibrosis is increasing, which may have a negative impact on the health of patients with T2DM, which may have different impacts in different sexes, and public health systems [21–23]. To date, we have no effective drug therapies for NAFLD-induced liver fibrosis.
To date, only a few drugs have been explored and validated in slowing the progression of liver fibrosis. It has been reported that statins may play an important role in slowing the progression of liver fibrosis in patients with T2DM because statins can reduce hepatic expression of tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6) and transforming growth factor-β (TGF-β) [13]. However a previous study also showed that statins were not associated with the progression of nonalcoholic fatty liver disease [24]. Aspirin was also found to have some effect on decreasing aspartate aminotransferase platelet ratio index(APRI) scores in patients with chronic liver disease. However, the association between antidepressant use and liver fibrosis in patients with T2DM has been unexplored until now.
In our present study, we included SSRIs, TCAs and SNRIs, which are the main types of prescriptions for patients with depressive disorder. Previous studies have reported that TCAs may play an antifibrotic role in liver disease, and may exert their antifibrotic effect in hepatic stellate cells through inhibition of the sphingomyelinase pathway [25]. Moreover, TCAs reduced NAFLD-induced hepatic steatosis, suggesting that TCAs may reduce NASH by regulating endoplasmic reticulum (ER) stress, lysosomal membrane permeabilization, and autophagy [26]. However, some recent evidence has suggested that TCAs are associated with increased weight gain and insulin resistance among patients with depression [27, 28]. The complex relationship between TCAs, T2DM and NAFLD should be noted. On the other hand, there is an increasing evidence that long-term SSRI use is associated with an increasing incidence of hepatic lipid accumulation [29]. The potential mechanisms are not fully understood. Some emerging evidence suggests that serotonin production, which can act via the 2A serotonin receptor (HTR2A) to upregulate the expression of lipogenic proteins and increase hepatic steatosis, in the periphery may be instrumental to the pathophysiology of NAFLD [30, 31]. SSRIs including fluoxetine have been shown to increase serotonin synthesis in previous studies [29]. In our present study, $18.8\%$ of diabetic patients received antidepressant drugs, which mainly included SSRIs, TCAs and SNRIs. We found that antidepressant drugs were not associated with the degree of hepatic fibrosis on liver biopsy, but also on VCTE.
Our study has several strengths and limitations. To our knowledge, our study was the first to assess the association between antidepressant use and VCTE-based liver fibrosis in patients with type 2 diabetes. Moreover, we had a large sample size and decided to include both sexes and all age groups. However, as an observational and cross-sectional study design, our study also has several limitations. First, as a cross-sectional analysis, temporal trends of antidepressant use and liver fibrosis cannot be extrapolated. Second, in the database, we do not have data concerning the duration of antidepressant use, and prospective studies should be conducted in the future to examine the association of liver fibrosis and antidepressant use.
## Conclusion
In conclusion, in this cross-sectional study, we found that antidepressant drugs were not associated with liver fibrosis or cirrhosis in patients with type 2 diabetes in a nationwide population. Further prospective studies or RCTs should be conducted to validate this finding.
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|
---
title: Isorhynchophylline inhibits inflammatory responses in endothelial cells and
macrophages through the NF-κB/NLRP3 signaling pathway
authors:
- Li-Hua Wang
- Zheng-Wei Gu
- Jie Li
- Wen-Qing Yang
- Yun-Lun Li
- Dong-Mei Qi
- Dan-Yang Wang
- Hai-Qiang Jiang
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10007741
doi: 10.1186/s12906-023-03902-3
license: CC BY 4.0
---
# Isorhynchophylline inhibits inflammatory responses in endothelial cells and macrophages through the NF-κB/NLRP3 signaling pathway
## Abstract
### Background
Atherosclerosis is a chronic inflammatory disease of arterial wall, which is closely related to inflammatory reaction. In this study, the anti-inflammatory effect of isorhynchophylline was studied by NF- κB / NLRP3 pathway.
### Methods
[1] ApoE−/− mice were fed with high-fat diet to establish atherosclerotic model, while C57 with the same genetic background was fed with common diet as control group. Body weight was recorded and blood lipids were detected. The expression of NLRP3, NF-κB, IL-18 and Caspase-1 in aorta was detected by Western-Blot and PCR, and plaque formation was detected by HE and oil red O staining. [ 2] Lipopolysaccharide interfered with Human Umbilical Vein Endothelial Cells (HUVECs) and RAW264.7 to form inflammatory model, and was treated with isorhynchophylline. The expression of NLRP3, NF-κB, IL-18 and Caspase-1 in aorta was detected by Western-Blot and PCR, and the ability of cell migration was detected by Transwell and scratch test.
### Results
[1] the expression of NLRP3, NF- κB, IL-18 and Caspase-1 in aorta of model group was higher than that of control group, and plaque formation was obvious. [ 2] the expressions of NLRP3, NF- κB, IL-18 and Caspase-1 in HUVECs and RAW264.7 model groups were higher than those in control group, while isorhynchophylline decreased their expression and enhanced cell migration ability.
### Conclusion
Isorhynchophylline can reduce the inflammatory reaction induced by lipopolysaccharide and promote the ability of cell migration.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12906-023-03902-3.
## Introduction
Atherosclerosis (AS), as the main pathological basis of cardiovascular disease, is a chronic and diffuse vascular endothelial inflammatory response. Typical triggers of inflammation are infection and tissue damage [1]. IL-18 −/− × ApoE −/− mice showed a $50\%$ increase in serum cholesterol, but a reduction in the size of atherosclerotic lesions, providing strong evidence for the inflammatory hypothesis of atherosclerotic thrombosis [2].
Arterial inflammation is triggered by damage to the endothelium, usually at arterial branch points or areas where blood flow is obstructed, resulting in endothelial cell activation and recruitment of inflammatory cells to the vessel wall. At the site of endothelial activation, the structure changes, and the exposure of proteoglycans promotes the retention of low-density lipoprotein particles in the intima, and foam cells are generated as macrophages gradually absorb the modified lipoproteins. Foam cells and lipids form the lipid core of the plaque, and macrophages in the lesion produce large amounts of metalloproteinases that promote plaque instability and rupture around the shoulder to form “vulnerable plaques” [3, 4].
Endothelial cells metabolize actively, regulate vascular tension, inflammatory response and generate new blood vessels, and maintain vascular homeostasis through synthesis and secretion and paracrine of metabolites [5, 6]. Long-term exposure to cardiovascular hazards or harmful circulation can disrupt endothelial defense mechanisms, causing endothelial cell damage. Endothelial cell injury is an early step in the beginning of AS, and endothelial cell permeability increases after injury, which is more conducive to the transmission of lipoprotein and inflammatory cells, thus promoting the development of AS [7, 8].
In the injured site of atherosclerotic vascular endothelium, macrophages recruit immune cells and participate in inflammatory response by producing pro-inflammatory factors and chemokines. Lipoprotein is oxidized and deposited in the arterial wall to promote macrophage foaming and participate in the formation of plaques. The expression of pro-inflammatory factors by macrophages in plaques can accelerate the progression of AS [3]. Macrophages are plastic, so macrophages have the ability to promote inflammation and inhibit inflammation [9]. Macrophages exist at different sites in the plaque and the affected macrophages polarize and activate the phenotype [10, 11]. The typical inflammatory macrophage phenotype is M1, which is usually induced by incubation with interferon-γ (IFN-γ) and toll-like receptor 4 (TLR4) ligand lipopolysaccharide. It is a mature differentiation of monocytes that phagocytize excess lipids to form foam cells, while producing large amounts of inflammatory mediators [11]. Several subpopulations of alternatively activated macrophage populations, called M2 macrophages. The M2 phenotype can be induced by incubation of macrophages with IL-4 and IL-13, which have anti-inflammatory effects [12]. Although the existing M2 phenotype is less sensitive to lipids, when the plaque continues to develop, the M2 phenotype will be transformed into m1-phenotype macrophages, promoting the continuous occurrence of inflammatory response [13]. During the progression of atherosclerosis, the migration of macrophages from atherosclerotic plaques decreases, thus maintaining the inflammatory state of plaques and leading to the progression of chronic and more complex lesions. Macrophages are usually removed from the site where inflammation is resolved by lymphatic migration to the lymph nodes [14], thereby reducing intravascular inflammation.
Vascular wall inflammation plays an important role in the process of AS lesions, and NLRP3 inflammasomes, as the core part of the inflammatory response, mediate the occurrence and development of AS [15]. NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome is composed of NLRP3, apoptosis-associated speck-like protein containing (ASC) and cysteine-containing Acid aspartate proteolytic enzyme 1 (cysteinyl aspartate specific proteinase-1, Caspase-1) composition. Activated NLRP3 inflammasome cleaves Caspase-1, releases mature interleukin-18 and other pro-inflammatory factors, triggering vascular inflammatory response [16]. Experiments have shown that the expression of IL-18 in the serum of Caspase-1 knockout mice is reduced, and the recruitment of monocytes is weakened [17]. The nuclear factor NF-κB pathway has long been recognized as a typical pro-inflammatory signaling pathway, mainly based on the role of NF-κB in the expression of pro-inflammatory genes, including cytokines, chemokines, and adhesion molecules. Similar studies have shown that the production of proinflammatory cytokines in human atherosclerotic plaques is also dependent on NF-κB [18]. Studies have shown that NF-κB-induced NLRP3 expression is sufficient for NLRP3 to mediate inflammasome formation, suggesting that blocking the functional crosstalk that occurs between NF-κB and NLRP3 may reduce inflammation [19].
Isorhynchophylline (IRN) is a common bioactive ingredient extracted from rubiaceae uncaria. It is a tetracyclic indole oxide alkaloid with the molecular formula C22H26N2O4 (384.47 g/mol), and has many biological effects such as anti-hypertension, anti-proliferation, anti-inflammation and neuroprotection [20–23]. For example, IRN has been reported to reduce LPS-induced production of inflammatory cytokines in mouse microglia [24]. Other study has found that IRN has anti-inflammatory and antioxidant effects on endotoxin-stimulated mouse alveolar macrophages [25]. However, whether IRN can play a potential anti-inflammatory role in endothelial cells and macrophages remains unknown.
The purpose of this study was to explore the inhibitory effect of isorhynchophylline on lipopolysaccharide-induced inflammatory responses of HUVECs and RAW264.7.
## Antibodies
NF-κB p65 (15101S, Cell Signaling Technology, USA), NLRP3 (8242S, Cell Signaling Technology, USA), Caspase-1 (3866S, Cell Signaling Technology, USA), IL-18 (12242S, Cell Signaling Technology, USA), β-actin (Abways Technology, Shanghai), HRP-linked Anti-Rabbit IgG (H + L) (7074S, Cell Signaling Technology, USA),Goat Anti-Mouse IgG (H + L) (SparkJade, China).
## Animals
Blank control group: female and male half, 12 mice; AS model group: female and male half, 12 mice (Animals were purchased from Charles River (Weitong Lihua) Laboratory Animal Technology Co. Ltd. (Beijing, China).). 8-week-old ApoE−/− mice were fed with high-fat diet with fat content of $21\%$ and cholesterol content of $0.25\%$ for 12 weeks, while 8-week-old C57BL/6 mice with the same genetic background were fed with normal diet as control group. During the feeding of mice, body weight, hair color, survival rate and other growth states of mice were recorded. After 12 hours of fasting and water deprivation, the mice were anesthetized by intraperitoneal injection of sodium pentobarbital (50 mg/kg) (China, Beijing Chemical Reagent Co., 061206), blood samples were collected from orbit for blood lipid analysis, and the mice were killed after neck removal. Normal saline was injected through the left ventricular puncture vascular system, and the aorta and other tissues were collected. The protocol of animal experiments was approved by the Ethics Committee of Shandong University of Traditional Chinese Medicine. The Protocol permission number is SDUTCM20201030002. Experimental Animal Production License No.: SCXK (Beijing) 2016–0011. Animal care and procedures comply with the ethical guidelines issued by the International Scientific Committee on Experimental Animals (ICLAS).
## Oil red O staining
The mouse aorta was dissected in cold PBS. $4\%$ paraformaldehyde was fixed at 4 °C for 16 h, rinsed with water for 10 minutes, and then rinsed with $60\%$ isopropanol. Dye the blood vessels with oil red O for 15 minutes, shake gently, rinse with $60\%$ isopropanol, and then rinse with water 3 times. The heart samples were collected and the cross section of the aortic root was made. The lesion site was identified by oil red O and the image was observed under microscope.
## HE staining
The aortic roots of each group were fixed with $10\%$ formalin, embedded in paraffin, sliced into 5 μm, stained with HE, and observed under stereo fluorescence microscope.
## Cell viability assay
Cell viability was detected by cell counting kit (CCK8, MedChemExpress, New Jersey, USA). Human umbilical vein endothelial cells were cultured in 96-well plate at the ratio of 5 × 103 cells per well. RAW264.7 was cultured in 96-well plate with 1 × 104 cells per well. After 24 hours, the culture medium was changed and the cells were stimulated with different concentrations of LPS. Then, the cells were cultured in 37 °C and $5\%$ CO2 humidified incubator for different times, and then cultured in CCK-8 for 1 h.
## Quantitative real-time PCR
RNA was extracted by using TRIzol Reagent (Invitrogen, CA, USA) according to the manufacturer’s protocol. cDNA was synthesized using SPARKscript IIRT Plus Kit (SparkJade, China). The qPCR analysis with specific primer pairs was performed with 2 × SYBR Green qPCR Mix (SparkJade, China). *The* gene expression level was calculated by 2-ΔΔCt method and normalized to β-actin mRNA expression level. The primer (Biosune, Shanghai, China) base sequence is shown in Table 1.Table 1Primer SequencesGeneForward Primer (5′-3′)Reverse Primer (5′-3′)Human IL-18ATTGACCAAGGAAATCGGCCTCGGTCCGGGGTGCATTATCTCTHuman Caspase-1TTGGAGACATCCCACAATGGTGAAAATCGAACCTTGCGGAHuman NF-kB p65AAGAAGAGTCCTTTCAGCGGACCTGCGGGAAGGCACAGCAATHuman NLRP3GAGCCGAAGTGGGGTTCAGACTTCAATGCTGTCTTCCTGGCHuman b-actinCATGTACGTTGCTATCCAGGCCTCCTTAATGTCACGCACGATMouse IL-18TCAAAGTGCCAGTGAACCCCGGTCACAGCCAGTCCTCTTACMouse Caspase-1CGTACACGTCTTGCCCTCATAACTTGAGCTCCAACCCTCGMouse NF-kB p65ATCGCCACCGGATTGAAGAGCGGGGTTCAGTTGGTCCATTMouse NLRP3TCTGCACCCGGACTGTAAACCATTGTTGCCCAGGTTCAGCMouse b-actinGGCTGTATTCCCCTCCATCGCCAGTTGGTAACAATGCCATGT
## Western blot analysis
Cells or tissues were homogenized in cold RIPA lysis buffer supplemented with phenylmethanesulfonyl fluoride (PMSF). Protein concentrations were measured using a BCA protein assay kit (Dalian Meilun Biotechnology, Dalian, China). Protein samples were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to polyvinylidene difluoride (PVDF) membranes (Merkck, Germany) by electroblotting. After sealing the film with $5\%$ skim milk for 1 hour, the membranes were incubated with primary antibodies (1:1000) and β-actin (1:5000) overnight at 4 °C, and then incubated with HRP-linked Anti-Rabbit IgG (H + L) (Cell Signaling Technology, USA) (1:3000) and Goat Anti-Mouse IgG (H + L) (SparkJade, China) (1:5000). After 1 h, park ECL Super (SparkJade, China) was used to detect the bands, and the average densitometric analysis was conducted using ImageJ software.
## Cell culture and drug treatment
Human umbilical vein endothelial cells (HUVECs, OTWO Biotech, HTX2104) were cultured in ECM containing 25 mL fetal bovine serum (FBS), 5 mL endothelial cell growth supplement, and 5 mL antibiotic solution. Cells were maintained at 37 °C and $5\%$ CO2 in a humidified incubator. The control group were cultured only with DMEM, the model group were incubated with DMEM+ 10 μg/mL of LPS (SIGMA). The treat group was cultured with DMEM+ 10 μg/mL of LPS and 12 mg/L of IRN (Shanghai Yuanye Bio-Technology Co. Ltd., 6859-01-4). All cells were incubated for an additional 24 h before analysis.
RAW264.7 (Meilunbio, PWE-MU004–2) were cultured in DMEM containing $10\%$ FBS and $1\%$ antibiotic solution. Cells were maintained at 37 °C and $5\%$ CO2 in a humidified incubator. The control group were cultured only with DMEM, the model group were incubated with DMEM+ 10 μg/mL of LPS. The treat group was cultured with DMEM+ 10 μg/mL of LPS and 12 mg/L of IRN. All cells were incubated for an additional 24 h before analysis.
## Scratch assay and transwell assay
Cells were grown in a humidified incubator at 37 °C and $5\%$ CO2 for 24 h. Ricks were generated in the cell monolayer using a 10 μL straw tip. Imaging was performed at $\frac{0}{3}$/$\frac{6}{9}$/12 h. Cell migration ability was determined by the transwell assay.
## Statistical analysis
All experimental data were expressed as means ± standard error of mean (SEM). Two-group comparison was performed using a t-test for independent samples. Raw data were analyzed with SPSS 25.0 software and images were processed with GraphPad Prism 7. P-Values< 0.05 were considered statistically significant. Each experiment was repeated three times.
## Inflammatory genes are highly expressed in animal models
In order to verify the abnormal expression of inflammatory factors in AS, we constructed the AS mouse model. We regularly documented the weight every 4 weeks (Fig. 1A). At 4 weeks the model group was about 1.08 times as much as the normal group, at 8 weeks the model group was about 1.13 times as much as the normal group and at 12 weeks the model group was about 1.17 times as much as the normal group. After 12 weeks of HFD, plaque formation increased significantly in model group, accompanied by increased levels of TG, TC, LDL-C and HDL-C (Fig. 1B).TG content in the model group was three times that in the normal group. The content of TC in model group was 12 times of that in normal group. The level of LDL-C in the model group was nine times higher than that in the normal group and the HDL-C content of the model group was 7 times higher than that of the normal group. Aortic oil red O staining and HE staining showed that plaque formation and lipid accumulation were significantly increased in the model group (Fig. 1C-D). The protein expression levels of NLRP3, NF-кB, Caspase-1 and inflammatory factor IL-18 in the model group were all higher than those in the control group (Fig. 1E-F). Besides, the expression of NLRP3, NF-кB, Caspase-1 and the inflammatory factor IL-18 were measured in the aorta. PCR results showed that the expression of NLRP3 and NF-кB was 2.79 times and 2.12 times that of the control group. The Caspase-1 model group was 1.73 times higher than the control group and the IL-18 model group was 1.52 times higher than the control group (Fig. 1G).Fig. 1Evaluation the model of AS induced by high fat diet. A Body weight of mice at 1, 4, 8 and 12 weeks. * $P \leq 0.05$, ** $P \leq 0.01.$ B Plasma total cholesterol (TC), triglyceride (TG), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), ox-LDL levels ($$n = 12$$). * $P \leq 0.05$, ** $P \leq 0.01.$ C HE staining of aortic root. Scale bar, 200 μm. The picture on the right is four times larger than the one on the left. D Frozen sections of aortic root were stained with Oil Red O. Scale bar, 200 μm. The picture on the right is four times larger than the one on the left. E-F The expression of NLRP3, NF-кB, Caspase-1 and IL-18 were detected by Western Blot ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01.$ G The expression levels of NLRP3, NF-кB, Caspase-1 and IL-18 were detected by RT-PCR ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01$
## IRN suppressed inflammation by inhibiting the NF-кB /NLRP3 pathway in HUVECs
To investigate the anti-inflammatory effect of IRN and examine whether IRN inhibit NF-кB /NLRP3 activity in atherosclerosis, we detected the expression of inflammation related factors in LPS-induced HUVECs. Western blot results showed that the expression of NLRP3, NF-кB, Caspase-1 and inflammatory factor IL-18 in LPS-induced model cells was higher than that in control cells, and IRN significantly inhibited the expression of NLRP3, NF-кB, Caspase-1 and inflammatory factor IL-18 in LPS-induced model cells (Fig. 2A-B). PCR results showed that the expression of NLRP3 model group was 4.27 times, NF-кB model group was 4.49 times, Caspase-1 model group was 3.82 times, IL-18 model group was 2.87 times than that of control group. After administration, the expressions of NLRP3, NF-кB, Caspase-1 and IL-18 decreased (Fig. 2C).Fig. 2Effects of IRN on inflammatory response of NF-кB /NLRP3 signaling pathway in HUVECs. A-B The expression of NLRP3, NF-кB, Caspase-1 and IL-18 in the pathway were detected by Western Blot ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01.$ C The expression levels of NLRP3, NF-ΚB, Caspase-1 and IL-18 were detected by RT-PCR ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01$
## IRN suppressed inflammation by inhibiting the NF-кB /NLRP3 pathway in RAW264.7
To investigate the anti-inflammatory effect of IRN and examine whether it inhibits NF-кB /NLRP3 activity in atherosclerosis, we tested the expression of inflammation related factors in RAW264.7 induced by LPS. Western blot results showed that the expression of NLRP3, NF-кB, Caspase-1 and inflammatory factor IL-18 in LPS-induced model cells was higher than that in control cells, and IRN significantly inhibited the expression of NLRP3, NF-кB, Caspase-1 and inflammatory factor IL-18 in LPS-induced model cells (Fig. 3A-B). PCR results showed that the expression of NLRP3, NF-кB, Caspase-1 and IL-18 was 2.95 times higher than that of the control group, 2.54 times higher than that of the control group, 1.71 times higher than that of the control group, 2.72 times higher than that of the control group. After administration, the expressions of NLRP3, NF-кB, Caspase-1 and IL-18 decreased (Fig. 3C).Fig. 3Effects of IRN on inflammatory response of NF-кB /NLRP3 signaling pathway in RAW264.7. A-B The expression of NLRP3, NF-кB, Caspase-1 and IL-18 in the pathway were detected by Western Blot ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01.$ C The expression levels of NLRP3, NF-кB, Caspase-1 and IL-18 were detected by RT-PCR ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01$
## The effect of IRN on migration of HUVECs was evaluated by scratch test and transwell test
Compared with 0 h, at 3, 6, 9 and 12 h, all groups had different numbers of cells crossing the blue line. The total number of cells crossing the blue line in the model group at 3, 6, 9 and 12 hours was lower than that in the control group (Fig. 4A-B). However, compared with the model group, the number of HUVECs processed with IRN increased at different time points (Fig. 4A-B). The migration ability of HUVECs was determined by Transwell method, and the results were consistent with those of scratch method. After 6 hours of culture, the migration ability of endothelial cells in the endotoxin treatment group was significantly lower than that in the control group. In addition, the number of HUVECs in the treatment group was significantly higher than that in the model group (Fig. 4C-D). As mentioned above, from the overall trend, the migration ability of HUVECs is obviously impaired by LPS, while IRN can inhibit the impaired of LPS.Fig. 4Migration capacity of HUVECs treated by IRN. A Representative micrographs of scratch analysis at different times in the experiment. Scale bar, 1 mm. B Wound closure represented as migration ability in three groups ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01.$ C HUVECs migration was determined by transwell assay. Scale bar, 100 μm. D *Statistical data* of migration cells was shown ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01$
## The effect of IRN on migration of RAW264.7 was evaluated by scratch test and transwell test
Compared with 0 h, at 3, 6, 9 and 12 h, all groups had different numbers of cells crossing the blue line. The total number of cells crossed the blue line at 3, 6, 9 and 12 h in the model group was lower than that in the control group (Fig. 5A-B). However, compared with the model group, the number of RAW264.7 treated with IRN increased at different time points (Fig. 5A-B). The migration ability of RAW264.7 was determined by Transwell method, and the results were in good agreement with those of scratch test. After 6 hours of culture, the migration ability of RAW264.7 treated with endotoxin was significantly lower than that of the control group. In addition, the number of RAW264.7 in the treatment group was significantly higher than that in the model group (Fig. 5C-D). As mentioned above, from the overall trend, the migration ability of RAW264.7 is obviously impaired by LPS, while IRN can inhibit the impaired of LPS.Fig. 5Migration capacity of RAW264.7 treated by IRN. A Representative micrographs of scratch analysis at different times in the experiment. Scale bar, 1 mm. B Wound closure represented as migration ability in three groups ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01.$ C RAW264.7 migration was determined by transwell assay. Scale bar, 100 μm. D *Statistical data* of migration cells was shown ($$n = 3$$). * $P \leq 0.05$, ** $P \leq 0.01$
## Discussion
AS is a chronic vascular wall inflammatory disease [4], and inflammation has been shown to play an important role in the occurrence and development of atherosclerotic plaques [26]. The inflammatory reaction of arterial wall runs through the whole process of initiation, progression and plaque rupture and thrombosis of AS [27]. Dyslipidemia, especially hypercholesterolemia, which is mainly manifested as increased low-density lipoprotein (LDL-C) and total cholesterol (TC), is one of the main risk factors for atherosclerotic vascular disease. Low density lipoprotein (LDL-C) and high-density lipoprotein cholesterol (HDL-C) are currently the two most studied lipoproteins in atherosclerosis, and triglyceride (TG) levels also play a crucial role [28, 29]. After the vessel wall is damaged, endothelial cells, macrophages, plaques, etc. will release pro-inflammatory factors to maintain and enhance local inflammation and the development of atherosclerotic lesions [30]. In this study, we successfully established an AS mouse model by high-fat chow feeding, and ApoE−/− mice had elevated serum levels of TG, TC, and LDL-C, but also elevated HDL-C (a protective lipoprotein). This may be due to ApoE being knocked out, which is detrimental to cholesterol excretion and allows a compensatory increase in HDL-C [31]. There was also a significant increase in aortic arch plaques in the model group (Fig. 1). The expressions of NLRP3, NF-κB, Caspas-1 and IL-18 were significantly increased in ApoE−/− mice fed with high fat diet (Fig. 1). In conclusion, the above animal experimental results suggest that there is an inflammatory response in AS, which may be related to NF-κB/NLRP3 pathway.
NLRP3 is the most representative inflammasome involved in the inflammatory response of AS [32]. NLRP3 is activated by binding to the NF-κBVIAA1.3-KBP fragment located at the upstream transcription initiation site of the human NLRP3 gene [19]. Studies have shown that adenosine triphosphate, reactive oxygen species, cholesterol crystals, oxidized low density lipoprotein, LPS and other cholesterol crystal factors can activate NLRP3 inflammasome [15, 33]. Lipopolysaccharide (LPS) is the main outer membrane component of Gram-negative bacteria, and it has been suggested that LPS from bacteria may be the source of the inflammatory response observed in atherosclerosis [34, 35]. In vivo experiments found that inflammatory factors were abnormally expressed in the aorta of AS model mice, so we verified in vitro whether IRN can reduce inflammation. In this study, we demonstrated that LPS induces high expression of NLRP3, NF-κB, Caspas-1, and IL-18 in HUVECs cells and RAW264.7 cells, suggesting that LPS can induce activation of NF-κB/NLRP3 inflammatory pathway (Figs. 2 and 3). LPS significantly inhibited cell migration, indicating that LPS reduced cell activity (Figs. 4 and 5).
IRN has been found to have therapeutic effects on cardiovascular and central nervous system diseases such as depression, Parkinson’s disease, Alzheimer’s disease and hypertension through mechanisms including antioxidant, anti-inflammatory and neuroregulatory activities [36, 37]. In this study, IRN treatment inhibited the activation of NF-κB/NLRP3 inflammatory pathway induced by LPS in HUVECs cells and RAW264.7 cells, and down-regulated the expression of NF-κB, NLRP3, Caspas-1 and IL-18 (Figs. 2 and 3), suggesting that IRN has an anti-inflammatory effect on NF-κB/NLRP3 pathway. In migration and scratch experiments, IRN promoted the migration ability of HUVECs (Fig. 4) and RAW264.7 (Fig. 5), indicating that IRN could protect cell activity. The results showed that IRN can protect the activity of HUVECs, thus reducing the damage of HUVECs caused by lipid metabolism disorder, inhibiting the inflammatory response induced by damage of HUVECs, and preventing the generation of atherosclerosis from the perspective of inflammation initiation. On the other hand, IRN can also protect the activity of RAW264.7, reduce the sensitivity of RAW264.7 to lipids and reduce the phagocytosis of lipids, thus reducing the formation of foam cells and ultimately reducing the generation of plaques.
In conclusion, IRN inhibits the inflammatory response of endothelial cells and macrophages induced by LPS, and its anti-inflammatory effect is related to the regulation of NF-κB /NLRP3 pathway. In addition, IRN can enhance cell viability. The results of this study provide sufficient scientific basis for the study of effective methods to inhibit the inflammatory response of AS.
## Supplementary Information
Additional file 1. Additional file 2. Additional file 3. Additional file 4. Additional file 5. Additional file 6. Additional file 7.
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|
---
title: 'Systematic review of cholangiocarcinoma in Africa: epidemiology, management,
and clinical outcomes'
authors:
- Akwi W. Asombang
- Nathaniel Chishinga
- Mouhand F. Mohamed
- Alick Nkhoma
- Jackson Chipaila
- Bright Nsokolo
- Martha Manda-Mapalo
- Joao Filipe G. Montiero
- Lewis Banda
- Kulwinder S. Dua
journal: BMC Gastroenterology
year: 2023
pmcid: PMC10007746
doi: 10.1186/s12876-023-02687-6
license: CC BY 4.0
---
# Systematic review of cholangiocarcinoma in Africa: epidemiology, management, and clinical outcomes
## Abstract
### Background
The prevalence, management, and clinical outcomes of cholangiocarcinoma in Africa are unknown. The aim is to conduct a comprehensive systematic review on the epidemiology, management, and outcomes of cholangiocarcinoma in Africa.
### Methods
We searched PubMed, EMBASE, Web of Science and CINHAL from inception up to November 2019 for studies on cholangiocarcinoma in Africa. The results reported follow PRISMA guidelines. Quality of studies and risk of bias were adapted from a standard quality assessment tool. Descriptive data were expressed as numbers with proportions and Chi-squared test was used to compare proportions. P values < 0.05 were considered significant.
### Results
A total of 201 citations were identified from the four databases. After excluding duplicates, 133 full texts were reviewed for eligibility, and 11 studies were included. The 11 studies are reported from 4 countries only: 8 are from North Africa (Egypt 6 and Tunisia 2), and 3 in Sub-Saharan Africa (2 in South Africa, 1 in Nigeria). Ten studies reported management and outcomes, while one study reported epidemiology and risk factors. Median age for cholangiocarcinoma ranged between 52 and 61 years. Despite the proportion with cholangiocarcinoma being higher among males than females in Egypt, this gender disparity could not be demonstrated in other African countries. Chemotherapy is mainly used for palliative care. Surgical interventions are curative and prevent cancer progression. Statistical analyses were performed with Stata 15.1.
### Conclusion
The known global major risk factors such as primary sclerosing cholangitis, *Clonorchis sinensis* and Opisthorchis viverrini infestation are rare. Chemotherapy treatment was mainly used for palliative treatment and was reported in three studies. Surgical intervention was described in at least 6 studies as a curative modality of treatment. Diagnostic capabilities such as radiographic imaging and endoscopic are lacking across the continent which most likely plays a role in accurate diagnosis.
## Background
Cholangiocarcinoma (CCA) is a rare and aggressive cancer that arises from the epithelium of the intrahepatic or the extrahepatic bile ducts. CCA can also arise from hepatic progenitor cells [1]. Globally, the incidence of cholangiocarcinoma varies from as high as 113 per 100,000 in Northern Thailand, $\frac{5.7}{100}$,000 in Southern Thailand, $\frac{2.2}{100}$,000 in UK, $\frac{1.1}{100}$,000 in USA and $\frac{0.3}{100}$,000 in Israel [2]. In both males and females, the incidence rises at age 60 to 70 years, rarely diagnosed before age 40 years, with a higher incidence and mortality in men compared to women [2–4]. Little is known of CCA in Africa. Patients with CCA can present with jaundice pruritus, acholic stool and steatorrhea depending on tumor location and stage of presentation, but they can also present with abdominal pain and weight loss. Some patients have their CCAs detected as incidentalomas during cross sectional imaging for abdominal symptoms, or during hepatoma screening in those with underlying cirrhosis. Screening programs exist for detection of gall bladder or biliary cholangiocarcinoma in patients with underlying primary sclerosing cholangitis (PSC) using MRCP and CA19-9 [5]. The management of CCA is dependent on anatomical location of tumor, staging and histology [6].
Anatomically CCA are classified as: *Intrahepatic cholangiocarcinoma* (iCCA) if the tumor is located proximal to the secondary branches of the right and left hepatic ducts, perihilar cholangiocarcinoma (pCCA) if located between the secondary branches of the right and left hepatic ducts and the common hepatic duct proximal to the origin of the cystic duct, and distal cholangiocarcinoma (dCCA) if involving the common bile duct but not the ampulla of Vater [2, 7]. Surgical resection is curative in early iCCA and if R0 resection is achieved, can give up to 36 months disease specific survival. There is a risk of recurrence in up to $62.2\%$ of patients [8]. Surgical resection is also curative in dCCA early stage disease for R0 resection which entails a Whipple’s procedure, although the risk of recurrence is high with 5-year survival of $27\%$ [9]. Surgical resection is the treatment of choice for early stage Bismuth-Corlette pCCA, although liver transplantation is emerging as the preferred treatment option with up to $76\%$ 5-year survival with neoadjuvant chemoradiotherapy [10]. Neoadjuvant and adjuvant chemotherapy does improve survival in all types of CCA. Hyperbilirubinemia due to biliary obstruction results in a pro-inflammatory state that negatively impacts the post-operative outcomes [11]. Hyperbilirubinemia due to biliary obstruction results in a pro-inflammatory state that negatively impacts the post-operative outcomes [11]. Endoscopic Retrograde Cholangiopancreatography (ERCP) and percutaneous transhepatic biliary drainage (PTBD) are the procedures most commonly used for biliary drainage; in both the curative (preoperative) and palliative setting. PTBD is considered to be more advantageous in the preoperative setting, for better priming for surgery, and there is a lower risk of postprocedural complications such as cholangitis. In the palliative setting, either technique can be used depending on the expertise of the operators, however in cases where the bilirubin is very high, the stenosis is lengthy, or in the presence of cholangitis, ERCP failure, or altered biliary anatomy—PTBD is recommended over ERCP [12].
Many patients present with unresectable or metastatic disease; as such the median survival rate is as low as 3 to 6 months in the USA [13].
Risk factors for developing CCA include occupation, which can be due to chemical exposure, chronic biliary inflammation, chronic hepatic inflammation, and cirrhosis of any cause and congenital and acquired causes of cholestasis leading to biliary inflammation. Nonetheless, risk factors are not identifiable in $50\%$ of cases [2]. CCA has been documented in workers at printing companies in Japan exposed to high concentrations of 1,2-dichloropropane (1,2-DCP) and dichloromethane [14]. CCA has been shown to develop decades after administration of the radiologic contrast medium thorotrast, that was used for cerebral angiography in the 1950s [15]. Autoimmune conditions, especially PSC have been associated with increased risk of cholangiocarcinoma in elderly patients in the USA [16]. Heavy infestation by liver flukes *Clonorchis sinensis* and Opisthorchis viverrini due to eating raw fish is a known cause of CCA in Asian countries where these flukes are endemic, including Korea, China, Taiwan, and Vietnam [17]. Other liver flukes *Fasciola hepatica* and Fasciola gigantica, cause fascioliasis in sheep and cattle, and can infest other herbivores, are a known zoonotic cause of human fasciolasis from eating water cress and drinking water with Lymnaeidae snails, that are intermediate hosts, can cause chronic cholestasis [18]. Unlike the *Clonorchis sinensis* and Opisthorchis viverrini, there is no direct causal like between *Fasciola hepatica* and Fasciola gigantica, to CCA [18]. Chronic intrahepatic cholestasis due to pigment stones can occur to due chronic hemolysis associated with hemolytic conditions such as sickle cell disease, thalassemia and red blood cell enzyme disorders such as G6PD deficiency. Although hepatolithiasis is a known risk factor for CCA, a direct link between hemolytic anemia and CCA has not been established [19]. Bacterial infections with *Helicobacter species* causing chronic cholangitis and cholecystitis have been implicated in the etiological role in biliary cancers [20] Obesity has been associated with the increased risk of cholangiocarcinoma [21]. The use of GLP-1 analogues, used in treatment of diabetes is associated with an increased risk of bile duct and gallbladder disease requiring hospital admission including cholelithiasis, cholecystitis, cholangitis. Over the long term as inflammation and cholestasis are associated with CCA, there is a possibility that use of these medications could pose a risk to development of CCA [22]. In a recent review of risk factors for CCA in Eastern and Western countries, choledochal cysts, cirrhosis, choledocholithiasis, hepatitis B virus infection have been found to be associated with CCA [2].
To date, no systematic review or meta-analysis of studies on CCA in Africa has been conducted. We address this gap by conducting a comprehensive systematic review on the epidemiology, management, and outcomes of CCA in Africa.
## Search strategy and study identification
We searched PubMed, EMBASE, Web of Science, and CINAHL from inception to November 2019 for primary publications. Searches were limited to human studies conducted in Africa and to English-language publications. The following search terms were used in the four databases: (Cholangiocar* OR cholangio cancer OR cholangio tumo*) AND (epidemiology OR incidence OR prevalence OR risk factor* OR treatment OR management OR outcome*) AND (Africa OR East* Africa OR West Africa OR Southern Africa OR North* Africa OR Algeria* OR Angola* OR Benin OR Botswana OR “Burkina Faso” OR Burundi OR Cameroon* OR "Cabo Verde" OR “Cape Verde” OR “Central African Republic” OR Chad OR Comoros OR Congo OR “Cote d`Ivoire” OR Djibouti OR Egypt* OR “Democratic Republic of Congo” OR “Equatorial Guinea” OR Eritrea* OR “Eswatini” OR Swaziland OR Ethiopia* OR Gabon* OR Gambia* OR Ghana* OR Guinea OR “Guinea Bissau” OR Kenya* OR Lesotho OR Liberia* OR Libya* OR Madagascar OR Malawi* OR Mali OR Mauritania* OR Mauritius OR Morocco* OR Mozambique OR Namibia* OR Niger OR Nigeria* OR Rwanda* OR “Sao Tome and Principe” OR Senegal* OR Seychelle* OR “Sierra Leone” OR Somali* OR “South African” OR “South Africa” OR “South African” OR Sudan* OR “North Sudan” OR “South Sudan” OR Tanzania* OR Togo* OR Tunisia* OR Uganda* OR Zambia* OR Zimbabwe*). Citation lists of retrieved articles were manually screened to ensure sensitivity of the search strategy.
## Study selection
Studies were included if they (i) *Provided a* quantitative measure of disease occurrence (prevalence, incidence) or mortality (survival, mortality rate), and (ii) *Had a* quantitative association between risk factors and CCA. Studies were excluded if any of these criteria were not met. Titles and abstracts of all articles identified were screened independently by the authors. Full texts were reviewed by the authors, and consensus reached on potential eligibility.
## Data extraction
The following information was extracted from each eligible study from the database search: first author; year of publication; country of study; study population; study design; patient characteristics; management and outcomes.
## Quality assessment
A modified quality assessment of the final papers included in this systematic review was adapted from the quality assessment tool for systematic reviews of observational studies (QATSO) [22].
Items included in the QATSO tool were: [1] External validity (representativeness of sampling procedures used in each study); [2] Response rate, which we modified to include three categories (> $80\%$, $60\%$-$80\%$, < $60\%$ or not reported); [3] Validity of measurement methods and bias in the measurement of the outcomes; and [4] Control for important confounders. The final score was the mean across the items. Studies achieving a final mean score of $60\%$ or above were considered adequate in this review [23].
## Statistical analysis
Statistical analyses were performed with Stata 15.1 (Stata Corporation, College Station, TX). Descriptive data were expressed as numbers with proportions. We used the χ2 test to compare proportions. P values < 0.05 were considered significant.
## Results of the search strategy
A total of 201 citations were identified from the four databases. After excluding duplicates ($$n = 33$$), non-human citations ($$n = 20$$), non-English citations ($$n = 3$$), and articles that could not be accessed ($$n = 12$$), 133 full texts were reviewed for eligibility, and 11 studies were included (Fig. 1).Fig. 1Flow diagram of included and excluded studies
## Description of the studies and CCA patients
Of the 11 studies, one reported on the epidemiology and risk factors for CCA and was conducted in Nigeria, [24] and 10 studies reported on management and outcomes of CCA. These 10 studies also reported on patient characteristics that were included in the epidemiology and risk factor analysis below. Of these 10 studies, two studies were conducted in Tunisia, [25, 26] two in South Africa [27, 28], and six in Egypt (Table 1) [29–34].Table 1Summary of studies from systematic reviewNumber of studiesNigeriaSouth AfricaTunisiaEgyptPer country1226Reporting patient characteristics1124Reporting on chemotherapy––21Reporting transhepatic self-expanding metal stents–2––Reporting on preoperative biliary drainage–––1Reporting surgery––6 The 11 studies had a combined total of 1,125 patients with CCA. The study from Nigeria had 13 patients [24]. For the two studies from Tunisia, one had 10 patients [25] and the other had 28 patients [26]. For the two studies from South Africa, one had 36 patients [27]. And the other had 50 patients [28]. For the six studies from Egypt, one had 100 patients [29], two studies reported on the same 243 patients, [30, 34] one study had 440 patients [31], another had 46 patients [32], and lastly one study had 159 patients [33].
## Study quality
A qualitative assessment of these 11 studies achieved a final score of $77.3\%$ and thus had adequate quality (Table 2).Table 2Quality assessment of the 11 studies included in the systematic reviewQuality variableQuality variable categoriesNumber of studies (%)External validityNon-probability$\frac{11}{11}$ ($100\%$)Probability0Response rate or proportion of patients managed for CCAAbove $80\%$$\frac{11}{11}$ ($100\%$)Between 60 and $80\%$0Not reported0Validity of measurement methods and bias in the measurement of cholangiocarcinomaHistology$\frac{7}{11}$ ($63.6\%$))Enhanced CT and/or MRCP1Not reported3Control for important confoundersYes$\frac{5}{11}$ ($45.6\%$)Not reported6Final mean % score (mean across the items)$77.3\%$
## Epidemiology and risk factors
The risk factors of CCA in Africa remain unclear, most occurring in absence of known risk factors. In a retrospective study done by Babatunde et al., in Nigeria, 13 histologically confirmed specimen of CCA were identified, of which 7 were from males ($53.8\%$) [24]. The other studies that reported on gender in the patient characteristics also showed that more males than females had CCA [25, 27, 29, 30, 33, 34].
The studies from Egypt showed that among patients with CCA, the proportion of males was statistically significantly higher than females. As shown by the wide $95\%$ confidence intervals, the studies from Nigeria, South Africa, and Tunisia had small sample sizes of patients with CCA, and therefore could not demonstrate the significant gender disparity that exists among patients with CCA (Fig. 2).Fig. 2Proportion of males and females among patients with cholangiocarcinoma In studies that reported on age, the median age for CCA was between 52.5 and 61 years [25–27, 30, 33].
## Chemotherapy
Chemotherapy treatment was mainly used for palliative treatment and was reported in three studies [25, 26, 32]. The chemotherapy was also administered as either neoadjuvant or adjuvant therapy [26]. In most cases, the first line chemotherapy used was gemcitabine based [25, 26]. The combined chemotherapies used include gemcitabine-oxaliplatin (GEMOX), gemcitabine-cisplatin (GEMCIS), and folfirinox (5-fluorouracil, leucovorin, irinotecan, and oxaliplatin) regimes. The second line chemotherapy used were capecitabine, irinotecan and paclitaxel. Adjuvant chemotherapy was mainly gemcitabine based [26].
## Percutaneous transhepatic self-expanding metal stents (SEMS)
Only two studies reported the use of percutaneous transhepatic SEMS for palliation of malignant biliary obstruction [27, 28] In these patients no surgery was planned as the tumor was beyond the surgical margins. Lawson et.al reported that $40\%$ of the SEMS was placed in a single stage procedure while the rest was a two-stage procedure. The biliary obstruction relief and SEMS placement procedure was successful in all the patients. The type of SEMS used was a Boston Scientific 69 mm × 10 mm Wall stent [28].
## Preoperative biliary drainage (PBD)
This is a preoperative procedure done to relieve the patient from the jaundice and improve the patient’s general and liver functional state before any definitive surgery. The type of PBD reported in a single study includes percutaneous transhepatic biliary drainage (PTBD), and endoscopic retrograde cholangiopancreatography (ERCP) with stent placement [29].
## Surgery
This is the only known curative treatment for cholangiocarcinoma. There were six studies in which patients underwent surgery for curative intent [29–34]. In each of these studies the choice of the surgical procedure done was dependent on the patients’ general status and tumor extension. Major hepatectomy with or without caudate lobectomy was reported in two studies [32, 33]. In all these studies, extrahepatic biliary resection and lymphadenectomy of locoregional lymph nodes was done. For patients with extrahepatic CCA which was localized, local resection of the bile duct with or without minor hepatectomy was performed [32]. Hepaticojejunostomy with or without a stent was done for biliary enteric anastomosis [33]. In one case–control study that compared patients that had both CCA and cirrhosis, with those that had CCA but without cirrhosis, the incidence of early postoperative liver cell failure was significantly higher in the cirrhotic group. Also, cirrhosis was associated with significantly lower overall survival [34]. However, not all surgery was for curative intent, in some cases palliative bypass surgery was done to relieve patient from jaundice secondary to malignant obstruction [27].
## Assessment of outcomes and overall survival
The best median patient survival time reported was 36 months in patients who underwent surgery with curative intent. This was in patients that underwent major hepatectomy with caudate lobectomy [33]. Patients who underwent palliative bypass surgery had a reported survival rate of $54.5\%$ in one year and the longest survival period of 28 months [27]. Two other studies with sample sizes of 10 and 28 CCA patients reported a median survival period of 10 months and 12 months, respectively [25, 26]. A single center experience study with a study population of 100 patients showed a higher median survival period of 19.7 months in patient who did not undergo pre-operative biliary drainage compared to those who underwent it prior to hepatectomy [29].
Palliative chemotherapy was used in patients whose CCA was beyond the surgical margins. Two studies on palliative chemotherapy with a combined study population of 68 patients had the same median survival period of 9 months [25, 26]. Also, two studies on palliative treatment of malignant biliary obstruction using a percutaneous transhepatic self-expanding metal stents had a median survival period of seven months and a $20\%$ survival rate in six months [27, 28].
## Discussion
This is the first systematic review to assess risk factors, epidemiology, and management of cholangiocarcinoma in Africa. We have not identified a comprehensive study on the incidence of CCA across the whole continent of Africa. An Egyptian study has identified male gender (1.7:1), farming and rural residency, cirrhosis, hepatitis C infection ($54\%$), Schistosomiasis ($66.5\%$), chronic typhoid infection ($52\%$) and gallstone disease as possible risk factors for hilar cholangiocarcinoma [35]. However, in a retrospective study done by Babatunde et al. in Nigeria, 37 patients had biliary tract carcinomas (representing $0.18\%$ of all cancers in Ibadan), with more female than male patients (26 versus 11) [24]. Twenty females and four males had gallbladder carcinoma, while 6 females and 7 males had cholangiocarcinoma ($$p \leq 0.02$$). Gallstones ($33\%$) and dysplasia ($42\%$) were also risk factors for developing biliary type cancers [24].
CCAs express different types of mucin as a marker of differentiation and probable metastatic potential. MUC1, MUC1 core, MUC2, MUC3, MUC4, MUC5AC, and MUC6, were studied [36]. Extensive MUC3 expression was significantly associated with well-differentiated tumors, while there was an approaching significance between the extensive expression of MUC1 and metastasis in CCA [36]. Depot‐medroxyprogesterone acetate (DMPA) is not a risk factor for the development of either hepatocellular carcinoma or cholangiocarcinoma according to a study conducted in Thailand and Kenya [37]. PSC, a major risk factor in the west, is rare especially in sub-Saharan Africa as is ulcerative colitis. A study on PSC has been conducted in South Africa, with a total of 69 patients attending Charlotte Maxeke Johannesburg Academic Hospital of which 22 were black [38]. The risk of CCA among this population in Johannesburg has not been stated. 3 out of 4 Afro Caribbean women on the UC database at St Bartholomew’s Hospital in London developed of PSC [39]. Segal reported a series of the first 46 patients treated at Baragwanath Hospital with ulcerative colitis which is a known risk factor for PSC [40].
We have not seen any published evidence of risk of PSC and CCA in Africa. Fish borne zoonotic liver flukes *Clonorchis sinensis* and Opisthorchis viverrini are not a problem in Africa. There is a case report of infestation among Egyptian family who had the practice of consumption of imported fish from the Far East [41]. Morsy et al. describe liver flukes in an Egyptian family, however this is a case report and describes an infestation among the Egyptian family who had the practice of consumption of imported fish from the Far East and not a larger Africa population [41]. Whereas, this is not a risk factor among native Africans, it will be an increasing risk with Chinese migration onto the continent of Africa. It is estimated up to 12.5 million Chinese are infected by *Clonorchis sinensis* [42].
Other liver flukes causing fasciolasis affect cows and sheep in almost all countries in Africa [43]. Although there is a similar lifecycle and pathogenesis with *Clonorchis sinensis* and Opisthorchis viverrini, there is not definite causal effect for CCA. Gall stones have been identified as a risk factor for CCA. However, no direct cause link has been attributed to hemolytic anemias which causes pigment stones. Recent and innovative studies have explored the role of organoids as models for studying cholangiocarcinoma [45]. Sato et al. describe the use of 3D cell culture models to create an environment that contributes towards the understanding cholangiopathies including cholangiocarcinoma [45]. Wang et al. also delineate the impact of the microenvironment in liver carcinogenesis [46]. Exploring the role of microenvironment and developing models for the study of cholangiocarcinoma are channels to explore in understanding the epidemiology and risk factors [46].
Eleven studies report on the management of CCA in Africa, 6 in Egypt, 2 in South Africa, 2 in Tunisia and 1 in Senegal. Although liver transplantation is emerging as treatment of choice in localized hilar CCA (hCCA), this has not been reported in Africa. Six Egyptian and 2 Tunisian studies have reported on surgical resection of CCA with curative intent. In Egypt, Wahab reported that major hepatectomy with excision of the extrahepatic bile duct system and caudate lobe resection may be recommended for the surgical treatment of central cholangiocarcinoma in selected cases [32].
In a study published in 2012 Wahab concluded that caudate lobe resection in combination with major hepatectomy did not affect operative or postoperative morbidity and mortality but led to higher rates of margin-negative resections and significantly improved survival [33].
El- Hanafy et al. found that preoperative biliary drainage by PTC and ERCP in selected patients with cholangitis and long-standing jaundice increased morbidity, transfusion requirements and hospital stay [29]. However biliary drainage was associated with better outcomes in patients with malnutrition and renal impairment prior to liver resection in hCCA. But in these patients, there was higher complication risk including bile leak and collections, increased transfusion requirement, wound infection and pneumonia. In another study El Wahab et al. treated 243 patients with hCCA with resection of which 173 were with curative intent [30].
There was a $14\%$ five-year survival. Factors influencing survival were young age at diagnosis, resection with caudate lobe resection, well differentiated tumor, negative resection margins, negative nodal metastases, and absence of cirrhosis. A bilirubin of less than 10 mg/dL and HCV negative status in a non-cirrhotic liver predicted a better prognosis in resection on hCCA [44].
Of the two Tunisian studies, the treatments were multimodal with different tumor locations. Romdhane et al., [ 25] treated 17 patients: $41\%$ gall bladder, $35.5\%$ pHCCA, $23.5\%$ dCCA. Five patients were treated with curative intent, of which 3 had adjuvant chemotherapy (the subtype of CCA is not described), with rest treated with chemotherapy. Median survival for surgical resection was 10 months and 9 months for the chemotherapy group. Labidi S et al. also reports of treatment outcomes of 51 patients in Tunisia: $45\%$ gall bladder, $22\%$ hCCA, $20\%$ iCCA $14.5\%$ dCCA [26]. Of these, 9 were treated with curative resection 5 of whom also had adjuvant chemotherapy (subtype unclear). Again, the outcome was 12 months median survival for surgical resection with curative intent group, and 9 months in the chemotherapy only group.
Two South African studies report on palliative management of obstructing hCCA [27, 28], Clarke DL et al. report on a total of 36 deeply jaundiced patients with hilar obstruction [27]. Twenty-two had surgical biliary bypass, and 14 had PTC, the surgical group had higher morbidity, but both had good symptomatic relief of jaundice. There was no significant benefit of survival in the 2 groups, concluding that PTC would be treatment of choice in this group of patients. Lawson AJ et al. evaluated the use of PTC self-expanding metal stents to palliate malignant biliary obstruction as an alternative to surgical bypass or when ERCP is not feasible [28]. This study involved 50 patients. Although the mortality rate was high in this very high-risk group of patients, PTC placed SEMS achieved satisfactory palliation [28].
While the risk factors exist across the continent, it is unlikely that these data can be extrapolated across the whole of Africa. The changing demographics on the African continent with changing migration from the Far East should increase the awareness of *Clonorchis sinensis* and Opisthorchis viverrini as risk factors for CCA. The prevalence of PSC in *Africa is* unknown, as it is the only condition that warrants surveillance elsewhere in the world. Research is also required to determine if Fasciola.hepatica and Fasciola.gigantica are indeed not a risk factor for CCA, as they have a similar life cycle and cause cholestatic disease as do *Clonorchis sinensis* and Opisthorchis viverrini.
CCA remains a late presenting disease in Africa. The best median survival outcome of 36 months were for patients who had undergone hepatectomy with caudate lobectomy. Outcomes were improved when patients did not undergo preoperative biliary drainage. Where surgical resection with curative intent was planned, the data does not show that was based on modern radiological staging techniques and preoperative histology, and this may explain the poor 5-year survival. In a single high-volume center in Egypt, curative surgery results in $14\%$ 5-year survival. At this center, factors positively influencing survival were identified as: caudate lobe resection, bilirubin of less than 10 mg/dL, absence of cirrhosis and young age. There are no obvious risk factors on which to formulate surveillance programs in Africa. PTC based SEMS can be used to palliate jaundice in obstructing tumors as can ERCP stenting of dCCA. This would require investment in expertise and equipment in those African countries lacking such equipment. Chemotherapy was used for palliative intent in most studies, except a select group from the Tunisian studies when it was neoadjuvant to curative resection [25, 26].
The earlier studies were Gemcitabine based, either with Oxaliplatin (GEMOX) or Cisplatin (GEMCIS). The Folfirinox (5-fluorouracil, leucovorin, irinotecan, and oxaliplatin) regime has also been used. The numbers in these study groups were small and there was not significant survival advantage in those who received chemotherapy and neoadjuvant chemotherapy.
Our Study limitations include the few numbers of included studies; inability to rule duplicate publication as similar author’s group published a few of the included studies and the data reviewed is reported from 4 countries only. Given the small number of studies, our review highlights the need for more research to understand epidemiology and aid with development of management strategies.
## Conclusion
Our systematic review contributes towards addressing the questions of epidemiology, etiology, risk factors, clinically outcomes and management of cholangiocarcinoma in Africa. Our current review provides some foundational published work needed to guide future studies and support proposed research. Eleven studies from four countries are not representative of the African continent hence the importance of this publication. There is limited data from the African continent differentiating pathogenesis of intraphepatic versus extrahepatic cholangiocarcinoma. The clinical presentation of patients in *Africa is* similar as in patients in other continents with extrahepatic cholangiocarcinoma, with the biliary obstruction as the most likely presentation. Further studies are required to explore the etiology, pathogenesis, management, and clinical outcomes of intra versus extrahepatic cholangiocarcinoma in Africa.
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|
---
title: Is atelectasis related to the development of postoperative pneumonia? a retrospective
single center study
authors:
- Eunji Ko
- Kyung Yeon Yoo
- Choon Hak Lim
- Seungwoo Jun
- Kaehong Lee
- Yun Hee Kim
journal: BMC Anesthesiology
year: 2023
pmcid: PMC10007747
doi: 10.1186/s12871-023-02020-4
license: CC BY 4.0
---
# Is atelectasis related to the development of postoperative pneumonia? a retrospective single center study
## Abstract
### Background
Atelectasis may play a substantial role in the development of pneumonia. However, pneumonia has never been evaluated as an outcome of atelectasis in surgical patients. We aimed to determine whether atelectasis is related to an increased risk of postoperative pneumonia, intensive care unit (ICU) admission and hospital length of stay (LOS).
### Methods
The electronic medical records of adult patients who underwent elective non-cardiothoracic surgery under general anesthesia between October 2019 and August 2020 were reviewed. They were divided into two groups: one who developed postoperative atelectasis (atelectasis group) and the other who did not (non-atelectasis group). The primary outcome was the incidence of pneumonia within 30 days after the surgery. The secondary outcomes were ICU admission rate and postoperative LOS.
### Results
Patients in the atelectasis group were more likely to have risk factors for postoperative pneumonia including age, body mass index, a history of hypertension or diabetes mellitus and duration of surgery, compared with those in the non-atelectasis. Among 1,941 patients, 63 ($3.2\%$) developed postoperative pneumonia; $5.1\%$ in the atelectasis group and $2.8\%$ in the non-atelectasis ($$P \leq 0.025$$). In multivariable analysis, atelectasis was associated with an increased risk of pneumonia (adjusted odds ratio, 2.33; $95\%$ CI: 1.24 – 4.38; $$P \leq 0.008$$). Median postoperative LOS was significantly longer in the atelectasis group (7 [interquartile range: 5–10 days]) than in the non-atelectasis (6 [3–8] days) ($P \leq 0.001$). Adjusted median duration was also 2.19 days longer in the atelectasis group (β, 2.19; $95\%$ CI: 0.821 – 2.834; $P \leq 0.001$). ICU admission rate was higher in the atelectasis group ($12.1\%$ vs. $6.5\%$; $P \leq 0.001$), but it did not differ between the groups after adjustment for confounders (adjusted odds ratio, 1.52; $95\%$ CI: 0.88 – 2.62; $$P \leq 0.134$$).
### Conclusion
Among patients undergoing elective non-cardiothoracic surgery, patients with postoperative atelectasis were associated with a 2.33-fold higher incidence of pneumonia and a longer LOS than those without atelectasis. This finding alerts the need for careful management of perioperative atelectasis to prevent or reduce the adverse events including pneumonia and the burden of hospitalizations.
### Trial registration
None.
## Introduction
Surgery and general anesthesia are likely to impair the respiratory function, thus increasing the incidence of postoperative pulmonary complications (PPCs) [1]. PPCs, particularly atelectasis and pneumonia, are major causes of postoperative morbidity and mortality [2–4], along with prolonged hospital length of stay (LOS) and increased resource utilization and healthcare expenditure [1]. Atelectasis may occur shortly after preoxygenation and induction of general anesthesia, leading to an intrapulmonary shunt of blood through non-ventilated lung tissue and ventilation/perfusion mismatch. It may in turn compromise gas exchange in most patients ($90\%$) undergoing surgery under general anesthesia. Although atelectasis generally affects a small portion of dependent lung regions, it may be more pronounced in patients who are obese, undergoing thoracic or open abdominal surgery, or in the Trendelenburg or lateral decubitus position [5, 6]. It may then contribute to the development of infectious complications including pneumonia [7].
In an experimental study, amelioration of atelectasis with exogenous surfactants or open lung ventilation attenuated growth and translocation of bacteria administered into the trachea, and diminished the risk of pneumonia in a piglet model [8]. These findings are in line with those in a previous study, in which mechanical ventilation with zero positive end-expiratory pressure (PEEP) aggravated lung bacterial burden and deteriorated the histological aspects of pneumonia following the intrabronchial bacterial instillation, along with higher degree of atelectasis, when compared with spontaneously breathing controls, in rabbits [9]. However, the clinical evidence in human patients has been contradictory.
Protective intraoperative mechanical ventilation (low tidal volumes with appropriate levels of PEEP) as compared with conventional ventilation (high tidal volumes with no PEEP) reduced the occurrence of PPCs. In patients undergoing major abdominal surgery, low tidal volumes and low levels of PEEP with alveolar recruitment maneuvers also reduced the risk of major pulmonary complications and health care utilization [4]. On the contrary, when the low tidal volumes were used during surgery, a higher level of PEEP with alveolar recruitment maneuvers did not reduce the incidence of PPCs [10–12], nullifying the protective ventilatory strategy. In fact, previous studies on PPCs have never analyzed postoperative pneumonia as a primary outcome of atelectasis, and only collectively evaluated atelectasis and pneumonia as a composite of PPCs.
Admission of patients to the intensive care unit (ICU) who are at high risk of subsequently requiring physiological support is considered a standard of care in many healthcare systems. However, ICU beds are costly and of limited resource [13]. In addition, the length of time spent hospitalized well represents the degree of hospital resources utilized, such as bed occupancy, staffing, and equipment [14]. To date, no studies have evaluated the relationship between postoperative atelectasis and patient outcomes including ICU admission and LOS. The present study was aimed to determine whether the presence of atelectasis is related to an increased incidence of postoperative pneumonia, ICU admissions, and LOS in patients who underwent elective non-cardiothoracic surgery under general anesthesia. The primary outcome was the development of pneumonia. The secondary outcomes were ICU admission, and postoperative LOS.
## Study design and selection of participants
This retrospective study was approved by the Institutional Review Board of Korea University Anam Hospital (IRB No. 2020AN0507), which waived the need for informed consent, because it was retrospective and without any risk to patients. Electronic medical records were reviewed to determine the incidence of postoperative pneumonia, ICU admission rate and postoperative LOS in patients with atelectasis. All methods were carried out in accordance with current regulations and guidelines.
Patients aged 18 years or older who underwent elective non-cardiothoracic surgery under general anesthesia at Korea University Anam Hospital between October 1, 2019, and August 31, 2020, were screened. Among them, patients who had chest radiographs (CXRs) taken within the first seven postoperative days were included. Patients were then excluded from the analysis if they 1) had preoperative fever or uncertainty about infection of other organs, 2) had atelectasis on CXR taken preoperatively, 3) had preoperative neuromuscular disease, 4) had chest tubes, endotracheal tube or tracheostomy before or after the surgery, and 5) had two or more operations within 4 weeks after surgery. We did not include patients who underwent cardiothoracic surgery because of the higher incidence of postoperative pneumonia compared to non-cardiothoracic surgery [15] in this study.
## Variables
Demographic and clinical variables obtained included age, sex, body mass index (BMI), comorbidities (asthma, chronic obstructive lung disease [COPD], hypertension, heart failure, diabetes mellitus, and anemia), current smoking status, selected laboratory results (i.e. albumin), American Society of Anesthesiologists (ASA) physical status, pre- and postoperative CXRs, type of surgery, duration of anesthesia, type of reversal agent for neuromuscular blockade, postoperative occurrence of atelectasis and pneumonia, ICU admission, and postoperative LOS. The subjects were divided into two groups, one with postoperative atelectasis (atelectasis group) and the other without (non-atelectasis group), to explore the effects of atelectasis on the outcomes.
## Outcomes
The incidence of pneumonia that occurred within the first 30 days following surgery was evaluated as the primary outcome, as recommended as a follow-up period for the occurrence of adverse events in perioperative medicine [16]. The secondary outcomes were ICU admission rate and postoperative LOS. Atelectasis was defined as lung opacification with a shift of the mediastinum, hilum, or hemidiaphragm toward the affected area, and compensatory overinflation in the adjacent non-atelectatic lung on CXR obtained within the first 7 postoperative days. Radiological diagnoses were reported by the attending radiologists independent of the study. Preoperative CXR is a routine evaluation in every department while postoperative CXR is routinely obtained in a few departments or as required when a patient have fever, or develops respiratory symptoms, including dyspnea, shortness of breath, cough and sputum, or decrease of oxygen saturation in our hospital.
Postoperative pneumonia was diagnosed by respiratory physicians as consulted by the clinicians in charge of the patients based on the US Centers for Disease Control definition of pneumonia with two or more serial CXR showing at least one of the following findings: (i) new or progressive and persistent infiltrates, (ii) consolidation, and (iii) cavitation (one radiograph is sufficient for patients with no underlying pulmonary or cardiac disease). They also should have at least one of the following signs: (i) fever (> 38℃) with no other recognized cause; (ii) leukopenia (white cell count < 4 × 109 /L) or leukocytosis (white cell count > 12 × 109 /L), (iii) altered mental status with no other recognized cause for adults > 70 years old; and at least two of the following: (i) new onset of purulent sputum or change in character of sputum, or increased respiratory secretions, or increased suctioning requirements, (ii) new onset or worsening cough, dyspnea, or tachypnea, (iii) rales or bronchial breath sounds, and (iv) worsening gas exchange (hypoxemia, increased oxygen requirement, increased ventilator demand) [16, 17].
## Anesthetic management
Patients are premedicated with midazolam (0.1 mg/kg, p.o.) 60 min before induction of anesthesia. After full preoxygenation, anesthesia is induced with propofol and remifentanil in the inhalation anesthesia as well as in the total intravenous anesthesia. After administration of rocuronium 0.6 – 1.0 mg/kg i.v., the trachea is intubated and the lungs are mechanically ventilated with air and oxygen at 0.5 fraction of inspired oxygen with or without the use of PEEP (5 cmH2O) in all subjects. Anesthesia is maintained using sevoflurane or desflurane combined with remifentanil in the inhalation anesthesia and using propofol and remifentanil in the total intravenous anesthesia. Upon completion of the surgery, the anesthetic is discontinued, and residual neuromuscular block is antagonized with pyridostigmine and glycopyrrolate or sugammadex. Postoperative interventions such as chest physiotherapy, bronchodilators and antibiotics, which may have an impact on the development of atelectasis and pneumonia are not provided as standard of care for surgical patients in our hospital.
## Sample size and statistical analysis
The incidence of postoperative pneumonia known to date is as low as 1 ~ $2\%$ [18, 19] and as high as $28.9\%$ [20]. In a pilot study of patients who underwent non-cardiothoracic surgery during 5 days in the first week of April 2020, 11 cases ($20.0\%$) of atelectasis and 2 cases ($3.6\%$) of pneumonia were observed in 55 patients, among 174 patients screened. Assuming that $3.6\%$ of the 55 patients would develop postoperative pneumonia, at least a sample of 160 patients was required to detect a correlation between two independent variables with different incidence rates ($20.0\%$ vs. $3.6\%$), with a type 1 error of 0.05 and a power of $90\%$ [21]. Since regression analysis was performed for 16 types of covariates affecting the incidence of pneumonia, we calculated a sample size of 2,560 subjects. In the pilot study, 55 out of 174 subjects screened satisfied the inclusion criteria; therefore, we planned to collect data from approximately 8,000 patients.
Data are expressed as total numbers (percentage) for categorical variables and as mean ± standard deviation or median (interquartile range [IQR]) as appropriate for continuous variables. Continuous variables were compared using univariate logistic regression, and categorical variables were compared using the chi-squared test or Fisher’s exact test as appropriate. Initially, covariates associated with the response variables (incidence of pneumonia and ICU admission rate) were screened. Covariates with a P-value < 0.2 as determined by univariate regression were included in the multivariate logistic regression analysis using stepwise backward selection on a criterion of P-value < 0.05. Binary regression analysis was performed for pneumonia and ICU admission, where the dependent variables were dichotomous, and linear multiple regression analysis was performed for postoperative LOS, where the dependent variable was continuous. Statistical significance was defined as P-value < 0.05. Data were analyzed using IBM SPSS Statistics version 25 (IBM, Chicago, IL, USA).
## Results
Patients who underwent elective non-cardiothoracic surgery under general anesthesia (aged ≥ 18 years) were screened, and 2,646 out of 7,847 patients met the eligibility criteria. Among them, 705 patients were then excluded because 273 had preoperative fever or signs of infection, 50 had preoperative atelectasis, 4 had neuromuscular diseases, 37 had chest tube before or after surgery, 151 had tracheostomy or intratracheal tube before or after surgery, and 190 underwent multiple surgeries within 4 weeks after the previous surgery. Therefore, a total of 1,941 patients were finally analyzed; 373 and 1,568 in the atelectasis and the non-atelectasis groups, respectively (Fig. 1).Fig. 1Patient screening and exclusion process The demographic and clinical characteristics of the enrolled patients are summarized in Table 1. Patients aged 60.2 [IQR, 51.0—72.0] years with $40.1\%$ of women and high comorbidities. The atelectasis group aged more (63.3 ± 13.3 vs. 59.5 ± 16.3 years, $P \leq 0.001$), had higher BMI (25.6 ± 4.3 vs. 24.4 ± 4.4 kg/m2, $P \leq 0.001$], and was associated with more frequent preoperative diagnoses of hypertension (54.2 vs. $43.8\%$, $P \leq 0.001$) and diabetes mellitus (29.5 vs. $21.2\%$, $$P \leq 0.001$$) than the non-atelectasis. The atelectasis group was more likely to undergo upper abdominal surgery ($P \leq 0.001$) and longer surgical procedures (234.3 ± 113.2 vs. 186.6 ± 102.0 min; $P \leq 0.001$) and more frequently used sugammadex (70.5 vs. $51.5\%$, $P \leq 0.001$). Of 1,941 patients, postoperative CXRs were obtained routinely in $58.4\%$, due to postoperative fever in $29.3\%$, due to respiratory symptoms in $8.7\%$, and etc. ( e.g. pre/post hemodialysis, upper gastrointestinal series) in $3.6\%$.Table 1Demographics and clinical characteristics of patients with or without atelectasisCharacteristicsOveralln = 1,941Atelectasis groupn = 373Non-atelectasis groupn = 1,568P-valueDemographics Age, yr60.2 ± 15.863.3 ± 13.359.5 ± 16.3 < 0.001 Sex (% male)778 (40.1)158 (42.4)620 (39.5)0.318 BMI, kg/m224.6 ± 4.425.6 ± 4.324.4 ± 4.4 < 0.001Medical comorbidities Asthma54 (2.8)9 (2.4)45 (2.9)0.630 COPD32 (1.6)7 (1.9)25 (1.6)0.700 Hypertension888 (45.7)202 (54.2)686 (43.8) < 0.001 Heart failure72 (3.7)16 (4.3)56 (3.6)0.510 Diabetes mellitus442 (22.8)110 (29.5)332 (21.2)0.001 Anemia (hemoglobin < 10 g/dL)168 (8.7)36 (9.7)132 (8.4)0.447 Hypoalbuminemia (serum albumin < 3.5 g/dL)182 (9.4)34 (9.1)148 (9.4)0.847 Smoking history340 (17.5)77 (20.6)263 (16.8)0.077 Preoperative CXR abnormality189 (9.7)35 (9.4)154 (9.8)0.798ASA physical status0.070 I83 (4.3)6 (1.6)77 (4.9) II1364 (70.3)265 (71.0)1099 (70.1) III450 (23.2)93 (24.9)357 (22.8) IV44 (2.3)9 (2.4)35 (2.2)Type of surgery < 0.001 Brain surgery55 (2.8)12 (3.2)43 (2.7) Back and spine surgery308 (15.9)44 (11.8)264 (16.8) Upper abdominal surgery330 (17.0)106 (28.4)224 (14.3) Lower abdominal surgery744 (38.3)170 (45.6)574 (36.6) Orthopedic surgery416 (21.4)33 (8.8)383 (24.4) Other surgeries88 (4.5)8 (2.1)80 (5.1)Anesthesia time, min195.8 ± 105.9234.3 ± 113.2186.6 ± 102.0 < 0.001Type of reversal agents < 0.001 Pyridostigmine848 (43.7)108 (29.0)740 (47.2) Sugammadex1071 (55.2)263 (70.5)808 (51.5) None22 (1.1)2 (0.5)20 (1.3)Postoperative pneumonia63 (3.2)19 (5.1)44 (2.8)0.025ICU admission147 (7.6)45 (12.1)102 (6.5) < 0.001Postoperative LOS, days6 [4-8]7 [5-10]6 [3-8] < 0.00130-day mortality5 (0.3)0 (0.0)5 (0.3)0.275Values are presented as mean ± standard deviation or median [interquartile range], for continuous variables and as total numbers (percentage) for categorical variables. Continuous variables were compared using univariate logistic regression, and categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriateBMI Body mass index, COPD Chronic obstructive pulmonary disease, CXR Chest radiography, ASA American society of anesthesiologists, LOS Length of hospital stay, ICU Intensive care unit Pneumonia occurred in 63 cases ($3.2\%$) within the first 30 days after the surgery: 19 of 373 patients in the atelectasis group ($5.1\%$) and 44 of 1,568 patients in the non-atelectasis ($2.8\%$) ($$P \leq 0.025$$). Multivariable regression analysis revealed that atelectasis (atelectasis vs. non-atelectasis) was an independent predictor for pneumonia (adjusted odds ratio, 2.33; $95\%$ confidence interval (CI): 1.24 – 4.38; $$P \leq 0.008$$) (Table 2). Among the 1,941 patients, 145 ($7.5\%$) were admitted to ICU after the surgery, with a rate significantly higher in the atelectasis group than in the non-atelectasis (12.1 vs. $6.5\%$, $P \leq 0.001$). However, after adjustment for confounders, no increase in ICU admission risk was observed (adjusted odds ratio, 1.68; $95\%$ CI: 0.99 – 2.86; $$P \leq 0.054$$). The duration of postoperative LOS was significantly longer in the atelectasis group than in the non-atelectasis (median, 7 [IQR, 5–10] days vs 6 [3–8] days; $P \leq 0.001$). Adjusted median duration was 2.19 days longer in the atelectasis group (unstandardized regression coefficient, 2.19; $95\%$ CI: 0.821 – 2.834; $P \leq 0.001$). Five patients died within 30 days after the surgery: 0 in in the atelectasis group and 5 in the non-atelectasis group ($$P \leq 0.275$$).Table 2Potential predictors of postoperative pneumonia among patients undergoing elective non-cardiothoracic surgeryVariablesOdds ratio$95\%$ confidence intervalP-valueBMI, kg/m20.8870.823 – 0.9560.002Asthma3.0321.020 – 9.0110.046Hypertension2.5831.342 – 4.9710.004Hypoalbuminemia4.2702.319 – 7.8610.000ASA physical status(as continuous variable)3.0501.978 – 4.7040.000Type of surgery,relative to lower abdominal surgery Brain surgery3.6140.988 – 13.2250.052 Back and spine surgery3.4001.324 – 8.7290.011 Upper abdominal surgery1.8720.781 – 4.4880.160 Orthopedic surgery2.2770.962 – 5.3900.061 Other surgeries4.1451.350 – 12.7230.013Type of reversal agents, relative to pyridostigmine0.071 Sugammadex1.6450.861 – 3.1430.132 None5.4541.119 – 26.5870.036Postoperative atelectasis2.3341.244 – 4.3770.008Results are from fully adjusted multivariate analysis with backward elimination (Wald test) accounting for all baseline characteristicsBMI Body mass index, ASA American society of anesthesiologists
## Discussion
The present study demonstrated that patients with postoperative atelectasis had a 2.33-fold higher risk of postoperative pneumonia compared with those without. The atelectasis was also associated with an extended LOS. However, it was not related to ICU admission rate after adjustment for confounders.
We found that patients with atelectasis were at increased risk of postoperative pneumonia but were also predisposed by independent risk factors to postoperative pneumonia including age [3, 19, 22], BMI [3, 23], a history of hypertension [23] or diabetes mellitus [23] and duration of surgery [22, 24] in our study. Nevertheless, adjustment for these risk factors still revealed direct association between the postoperative atelectasis and the risk of postoperative pneumonia. Our findings are important as the postoperative pneumonia might be preventable, at least in part, by reducing or reversing atelectasis in the perioperative period. In fact, intraoperative atelectasis during general anesthesia is known to be prevented by limiting the fraction of inspired oxygen (“absorption atelectasis”) [25] and by promoting alveolar recruitment with PEEP and recruitment maneuvers [26]. In addition, postoperative atelectasis has been reduced by using individual PEEP settings [27], and by maintaining a consistent positive pressure at the airway with pressure support and PEEP during emergence from general anesthesia as well [28]. To the best of our knowledge, this is the first report to strongly suggest postoperative atelectasis as a risk factor for pneumonia in surgical patients.
It has been known that atelectasis may induce local immune dysfunction and inflammation, thereby increasing the susceptibility to infection. In addition, perioperative changes in lung mechanics and breathing patterns due to general anesthesia and surgery and/or consequent atelectasis are known to weaken the lung defense system [29, 30], compromising both cough and mucociliary clearance against pathogens distal to the obstruction [31, 32]. Local depletion or dysfunction of surfactant due to a significant atelectasis, the use of anesthetic agents [33], and/or prolonged mechanical ventilation [34] also compromise the protective response against infection because surfactant has direct antimicrobial properties [35]. Taken together, compromised local immune system, impaired mucociliary clearance, and surfactant dysfunction appears to be responsible for some of the increased risk of pneumonia in post-surgical patients with atelectasis. Furthermore, atelectasis was shown to reduce the penetration of antibiotics into the affected lung, making it difficult to obtain the correct drug concentrations to fight against potential pathogens [36].
A recent meta-analysis demonstrated that a high inspired oxygen fraction (versus a low inspired oxygen fraction) had no effect on the incidence of pneumonia despite the increased incidence of postoperative atelectasis in patients undergoing non-thoracic surgery under general anesthesia [37]. Three possible mechanisms have been proposed to explain the atelectasis: resorption of alveolar air (i.e. absorption atelectasis), direct compression of lung tissue (i.e. compression atelectasis), and surfactant impairment [38]. The atelectasis was mainly attributed to absorption of alveolar air that spontaneously resolves within 24 to 48 h in a previous study [37]. On the other hand, upper abdominal surgery, which favors the development of compression atelectasis, was more frequent and the duration of general anesthesia eliciting both forms of atelectasis (i.e. surfactant dysfunction and compression) [38] was longer in the atelectasis group than in the non-atelectasis in our study. The discrepancy between the studies may be accounted for by different pathogenic mechanisms underlying the development of atelectasis.
The reported incidence of postoperative pneumonia varies widely by the site and type of surgery performed from $0.5\%$ to $28.9\%$ [18, 20, 39]. The incidence in our study ($3.2\%$) was higher than that ($1.8\%$) in ASA status III patients undergoing non-cardiothoracic predominantly abdominal and pelvic surgery [39]. The subjects were limited to 7-day postoperative follow-up in their study, whereas ours were to 30-day. In addition, $38.0\%$ of postoperative CXRs were obtained from patients with symptoms of respiratory diseases including fever, shortness of breath, and cough. It has been reported that atelectasis itself does not complicate pneumonia, but may contribute to the development of pneumonia when exposed to infectious agents [32]. Since the fever that accompanies atelectasis is related to infection distal to the obstructed airway [40, 41], atelectasis may have progressed to pneumonia in some patients with postoperative CXR. Alternatively, it is also possible that pneumonia might have led to atelectasis because the presence of fever may reflect infectious complications rather than atelectasis. A longer follow-up period and bias in patient selection may explain why the incidence of pneumonia in our study was higher than that in a previous one [39].
Reversal of neuromuscular blockade with sugammadex was associated with a lower incidence of major PPCs, including atelectasis and pneumonia compared to the use of anticholinesterase in patients undergoing non-cardiac surgery in a previous study [42]. In contrast, the use of sugammadex resulted in a higher incidence of atelectasis compared to the use of anticholinesterase in our study ($25.9\%$ vs. $13.9\%$, $P \leq 0.001$), although it had no significant effect on pneumonia ($3.7\%$ vs. $2.6\%$, $$P \leq 0.201$$). In our hospital, sugammadex is generally used for patients who are at high risk for PPCs (e.g. upper abdominal or thoracic surgery), particularly in the elderly, whereas reversal agent options are currently limited by price. The bias in treatment and patient selection may explain the discrepancy between the studies. In fact, upper abdominal surgeries were more frequent [19] and the patients were older [24] in the atelectasis group compared to the non-atelectasis in our study, both of which are indeed risk factors for atelectasis.
Although the atelectasis seen during anesthesia resolves within 24 h after laparoscopy in non-obese subjects [43], it may persist for at least 24 h in most morbidly-obese patients [43] or with major surgeries [44]. A meta-analysis of postoperative atelectasis in a heterogeneous group of patients demonstrated CXR evidence of atelectasis in $57\%$ of patients, with little improvement on the third day following the surgery [45]. By contrast, the incidence of atelectasis determined by CXRs obtained at various time points from the first to the 7th postoperative day was $19.2\%$ ($\frac{373}{1}$,941), being much lower in our study. Moreover, $38.0\%$ of postoperative CXRs were obtained because of respiratory symptoms including fever, which could lead to bias in patient selection in our study. Large prospective studies in which chest images are obtained early after surgery in every patient are needed to precisely determine the role of atelectasis in the development of pneumonia.
Our study has several limitations. First, it is a single center study, and there may be a lack of representativeness of the entire population. Second, being a retrospective study, it is not free from bias in patient selection and treatment. Not all covariates were controlled, nor were demographic and clinical characteristics. Third, the intraoperative use of high oxygen concentration or no PEEP is known to be associated with increased severity of atelectasis [46]. However, anesthetic management during the surgery was not harmonized among patients, and every patient followed routine anesthetic management in our hospital. Fourth, CXR, an inexpensive and the most frequently ordered radiological test, was used to determine atelectasis, resulting in some under-estimation of actual atelectasis cases. Chest CT scans have shown greater diagnostic sensitivity in patients undergoing lower abdominal surgery compared with CXR [44] and remains the accepted standard when measuring atelectasis. In addition, lung ultrasound was shown to be superior to CXR in diagnosing PPCs following cardiothoracic surgery. It has been widely used since it is portable, dynamic, and free of radiation [28, 47, 48]. Finally, we did not control for baseline lung function and did not report on the specific interventions used to manage postoperative atelectasis, which limits the ability to make specific recommendations for clinical practice.
In conclusion, our retrospective study demonstrated that patients with postoperative atelectasis had a 2.33-fold higher risk of postoperative pneumonia than those without. Moreover, the development of postoperative atelectasis was associated with a prolonged LOS, while not being with ICU admission. Clinicians should try to prevent or treat postoperative atelectasis to reduce devastating adverse events including postoperative pneumonia, and to decrease resource utilization and healthcare expenditure in patients undergoing elective non-cardiothoracic surgery. Atelectasis as a risk factor for pneumonia warrants further investigation.
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|
---
title: 'Risk of subclinical atherosclerosis across metabolic transition in individuals
with or without fatty liver disease: a prospective cohort study'
authors:
- Zhuojun Xin
- Jiaojiao Huang
- Qiuyu Cao
- Jialu Wang
- Ruixin He
- Tianzhichao Hou
- Yi Ding
- Jieli Lu
- Tiange Wang
- Zhiyun Zhao
- Weiqing Wang
- Guang Ning
- Min Xu
- Yufang Bi
- Yu Xu
- Mian Li
journal: Nutrition & Metabolism
year: 2023
pmcid: PMC10007748
doi: 10.1186/s12986-023-00734-3
license: CC BY 4.0
---
# Risk of subclinical atherosclerosis across metabolic transition in individuals with or without fatty liver disease: a prospective cohort study
## Abstract
### Background
Metabolic dysfunction is a major determinant in the progression of fatty liver disease. It is pivotal to evaluate the metabolic status and subsequent transition in fatty liver population and to identify the risk of subclinical atherosclerosis.
### Methods
The prospective cohort study included 6260 Chinese community residents during 2010–2015. Fatty liver was determined as hepatic steatosis (HS) by ultrasonography. Metabolic unhealthy (MU) status was defined as having diabetes and/or ≥ 2 metabolic risk factors. Participants were categorized into 4 groups according to the combination of metabolic healthy (MH)/MU and fatty liver status (MHNHS, MUNHS, MHHS and MUHS). Subclinical atherosclerosis was assessed by elevated brachial-ankle pulse wave velocity, pulse pressure and/or albuminuria.
### Results
$31.3\%$ of the participants had fatty liver disease and $76.9\%$ were in MU status. During a 4.3-year follow-up, $24.2\%$ of participants developed composite subclinical atherosclerosis. Multivariable adjusted odds ratios for composite subclinical atherosclerosis risk were (1.66 [1.30–2.13]) in MUNHS group and (2.57 [1.90–3.48]) in MUHS group. It seemed that participants with fatty liver disease were more prone to be remained in MU status ($90.7\%$ vs$.50.8\%$) and less likely to regress to MH status ($4.0\%$ vs. $8.9\%$). Fatty liver participants progressed to (3.11 [1.23–7.92]) or maintained MU status (4.87 [3.25–7.31]) significantly impelled the development of the composite risk, while regressing to MH status (0.15 [0.04–0.64]) were more intended to mitigate the risk.
### Conclusions
The current study emphasized the importance of assessing metabolic status and its dynamic changes, especially in the fatty liver population. Regressing from MU to MH status not only benefited the systematic metabolic profile but also ameliorated future cardiometabolic complications.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12986-023-00734-3.
## Introduction
Globally, fatty liver disease has become an epidemic, affecting approximately $25\%$ of adult population [1]. Prevailing fatty liver disease poses individuals at increased risk of both end-stage liver diseases and extra-hepatic complications [2–5]. Accumulating evidence has indicated the critical role of metabolic dysregulation in the process of adverse prognosis, for which proposed the new terminology of metabolic dysfunction-associated fatty liver disease (MAFLD) [6].
There is a substantial overlap between non-alcoholic fatty liver disease (NAFLD) and MAFLD [7, 8]. Considering the strong association with body mass index (BMI), fatty liver patients characterized overweight or obesity are included into MAFLD criteria, which accounted for more than $50\%$ [8, 9]. Recently, a cross-sectional cohort study further pointed out that a considerable proportion ($26.6\%$) of this subgroup were metabolic healthy, referring to the absence of type 2 diabetes and metabolic risk factors of MAFLD criteria [10]. In view of its dynamic feature, the presence of metabolic healthy status emphasized the importance of holistically assessing the metabolic status and further metabolic transition among fatty liver population.
Mounting data supported the strong link of fatty liver disease with atherosclerotic cardiovascular disease as well as subclinical markers of atherosclerosis [11–13]. It has been suggested that fatty liver patients at a high risk of cardiovascular progression is more frequently associated with metabolic abnormality. Moreover, fatty liver disease is not only manifested by excessive liver fat deposition, but also accompanied with metabolic abnormalities, leading to a highly heterogeneous condition [14]. The concept of MAFLD also emphasized the importance of metabolic heterogeneity. However, there is few data covering the metabolic transition towards fatty liver population, and it remains unclear to what extent that fatty liver population with varying phenotypes of metabolic status and metabolic transition are associated with the risk of subclinical atherosclerosis, an early lesion status of cardiovascular disease, such as arterial stiffness, coronary calcification and endothelial dysfunction.
The aim of this study was firstly to confirm the distribution of metabolic healthy/metabolic unhealthy (MH/MU) status among participants with ultrasound-based hepatic steatosis, and to evaluate the individual and combined associations of fatty liver disease and MH/MU status as well as metabolic transition with the risk of incident subclinical atherosclerosis, and to further discuss the potential effect and related determinants of MU regression on the established prognosis.
## Study design and population
The study population was from a community-based cohort study in Jiading District of Shanghai, China. The detailed protocol has been published previously [15]. In brief, the cohort study was launched among 10,375 permanent residents (≥ 40 years old) between March and August 2010. Baseline health examinations comprising of a standard questionnaire and clinical measurements were completed for each participant. After a follow-up interval for up to 5 years, participants were re-invited for an on-site visit during August 2014 and May 2015. For the current study, we excluded individuals who registered for death at follow-up ($$n = 265$$), failed to the on-site follow-up visit ($$n = 3541$$) and had missing data on the baseline ($$n = 16$$) or follow-up ($$n = 293$$) hepatic ultrasound, leaving 6260 for ensuing analysis. In the separate outcome analysis, pre-existing subclinical atherosclerosis at baseline and data missing at follow-up reflected by elevated brachial-ankle pulse wave velocity (baPWV) ($$n = 1593$$), elevated pulse pressure (PP) ($$n = 1592$$) and albuminuria ($$n = 507$$) were further excluded from baPWV, PP and albuminuria analyses, respectively. 2526 were additionally excluded with any of elevated baPWV, elevated PP or albuminuria or respective missing data from the composite subclinical atherosclerosis analysis. Detailed selection flowchart was presented in Fig. 1. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the Institutional Review Board of Ruijin Hospital. Enrolled participants signed written informed consents. Fig. 1Study population flow diagram. BaPWV, brachial–ankle pulse wave velocity; PP, pressure pulse
## Data collection
Standard questionnaires were administered to collect information regarding individuals’ demographic characteristics, medical history, cigarette smoking, alcohol intake and physical activity both at baseline and follow-up interview. Physical activity was assessed by the short form of the Global Physical Activity Questionnaire and calculated into metabolic equivalent minutes per week (MET-min/wk) [16]. The anthropometry data including body weight, height and waist circumference were measured by trained staff, with a standard protocol applied. Automated electronic blood pressure monitoring was used with Omron model HEM-752 FUZZY. The average of three seated measurements was adopted for analysis. PP was defined as systolic blood pressure (SBP) minus diastolic blood pressure (DBP) from the average of the three readings. BMI was calculated as body weight (kg) divided by square of the height (m2).
Blood samples were drawn after ≥ 10 h of fasting to evaluate levels of fasting plasma glucose, triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and liver enzymes (including aspartate transaminase, alanine transaminase, and gamma-glutamyl transferase). Then, participants underwent a standard 75-g load oral glucose tolerance test to examine 2-h postprandial plasma glucose by the glucose oxidase method on an automated analyzer (Modular Analytics P800; Roche). Glycated hemoglobin (HbA1c) was determined by high-performance liquid chromatography using the VARIANT II Hemoglobin Testing System (Bio-Rad Laboratories). Plasma lipids and liver enzymes were assessed by an automated analyzer (Modular E170; Roche). The index of homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as fasting serum insulin (μIU/mL) × fasting plasma glucose (mmol/L)/22.5. The cutoff of HOMA-IR ≥ 2.5 was defined as insulin resistance [17].
A first-voided, early-morning spot urine sample was obtained for the measurement of urinary albumin (mg/dL) and creatinine (mmol/L) and tested by the immunoturbidimetric method (Beijing Atom High-Tech, Beijing, China) and Jaffe’s kinetic method on an automatic analyzer (Hitachi 7600–020, Tokyo, Japan), respectively. Women experiencing menstruation on the survey day were not included in the present study. Urinary albumin-to-creatinine ratio (UACR) was calculated by dividing the urinary albumin concentrations by the urinary creatinine concentrations and presented in mg/g.
All participants underwent baPWV measurement at baseline and follow-up visit on Colin VP-1000 (Model BP203RPE II, form PWV/ABI) after 10-min rest for the evaluation of artery stiffness. Pulse waves were obtained simultaneously with suitable cuffs placed on the upper sides of bilateral arms and ankles. The distance from bilateral upper arms to ankles was corrected for its difference of time delay when obtaining the baPWV. The greater value of bilateral baPWV was adopted for analysis.
## Assessment of MU status and fatty liver disease
Criteria of metabolic risk factors were defined according to MAFLD consensus as the following [6]: [1] waist circumference ≥ $\frac{90}{80}$ cm; [2] blood pressure ≥ $\frac{130}{85}$ mmHg and/or taking antihypertensive medication; [3] triglycerides ≥ 150 mg/dl and/or taking lipid-lowering medication; [4] HDL-C < 40 mg/dl for men and HDL-C < 50 mg/dl for women or taking lipid-lowering medication; [5] prediabetes: fasting plasma glucose (FPG) 100 to 125 mg/dl or 2 h-postprandial glucose (2 h-PG) 140 to 199 mg/dl, or HbA1c $5.7\%$ to $6.4\%$; and [6] HOMA-IR ≥ 2.5. MU status was defined as having type 2 diabetes (FPG ≥ 126 mg/dl or 2 h-PG ≥ 200 mg/dl, or HbA1c ≥ $6.5\%$ or taking hypoglycemic medicine) and/or ≥ 2 metabolic risk factors [10].
Liver ultrasound was operated by two radiologists who were blinded to the protocol, using a high-resolution B-mode tomographic ultrasonic system (Esaote Biomedica SpA, Italy) with a 3.5-MHz probe. Fatty liver disease was determined as hepatic steatosis by the presence of ≥ 2 of 3 abnormal imaging findings: diffusely increased echogenicity (‘bright’) liver—with liver echogenicity greater than kidney or spleen, vascular blurring, and deep attenuation of ultrasound signal [18].
Study participants were divided into four groups according to baseline MH/MU and fatty liver status: [1] metabolic healthy and no hepatic steatosis, MHNHS; [2] metabolic unhealthy and no hepatic steatosis, MUNHS; [3] metabolic healthy and hepatic steatosis, MHHS; and [4] metabolic unhealthy and hepatic steatosis, MUHS.
## Assessment of subclinical atherosclerosis
Subclinical atherosclerosis was separately defined by elevated baPWV, elevated PP and albuminuria [19–22]. The combination pattern was regarded as the composite outcome. Baseline and incident elevated baPWV and elevated PP were referred to upper quartiles of baseline baPWV (≥ 1768.0 cm/s) and baseline PP (≥ 67.3 mmHg). Incident albuminuria was defined as UACR ≥ 30 mg/g.
## Statistical analysis
For statistic description, means ± standard deviations or medians (interquartile ranges) were fitted for continuous variables and numbers (proportions) for categorical variables. HOMA-IR, triglycerides, alanine aminotransferase, aspartate transaminase, gamma-glutamyl transferase and UACR were logarithmically transformed to achieve a normal distribution. Differences of baseline characteristics among the four groups were determined using one-way ANOVA and chi-square test.
We adopted multivariable logistic regression analysis to assess the longitudinal associations of baseline metabolic health and fatty liver status as well as metabolic status changes with incident subclinical atherosclerosis (separate/composite). We selected a priori potential confounders for adjustment in multivariable models based on knowledge of their associations with MAFLD and subclinical atherosclerosis. Potential confounders including age, sex, follow-up interval (Model 1), current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, baseline BMI and BMI change (Model 2), were adjusted in the analysis models.
To further illustrate the metabolic regression of MUHS group during follow-up, changes of metabolic risk parameters between baseline and follow-up visits were compared between the stable MU and MU to MH groups. Logistic regression models with generalized estimating equations were used to explore the associated metabolic parameters in metabolic regression. For repeated-measures analysis, the multivariable model was adjusted for age, sex, current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, waist circumference, SBP, DBP, triglycerides, HDL-C, FPG, 2 h-PG, HbA1c, HOMA-IR and BMI. All covariates except sex and education were repeated-measured both at baseline and follow-up and modelled as time-varying variables.
Sensitivity analysis was conducted [1] in exclusion of participants receiving treatment with hypoglycemic medications or insulin, blood pressure- or lipid-lowering medications during the baseline and follow-up, eliminating the interference of metabolic related medications on the metabolic transition; [2] in exclusion of participants with advanced stage assessed by Fibrosis-4 score > 2.67, excessive alcohol consumption and other liver diseases, specifying the actual relationship of simple fatty liver disease combining with metabolic abnormality with risk of subclinical atherosclerosis.
Results were represented as odds ratios (OR) with $95\%$ confidence intervals (CI), with a 2-tailed alpha value of 0.05 considered statistically significant. Statistical analysis was performed on SAS 9.2 (SAS Institute, Cary, NC).
## Baseline characteristics of study population
Overall, $31.3\%$ of the participants had ultrasound-detected hepatic steatosis and $76.9\%$ were categorized as MU status. Participants with hepatic steatosis but remaining in MH status (MHHS group) accounted for $1.0\%$ among the baseline population ($$n = 6260$$). Compared with MHNHS group, MHHS group displayed a slightly unfavorable metabolic profile, including higher levels of BMI, waist circumference, blood pressure, plasma glucose, lipid and liver enzymes, but younger age, higher educational attainment and more time occupied in physical activity. The aforementioned metabolic profile along with baPWV, PP and UACR were significantly worse in MU groups, irrespective of fatty liver status. Additionally, participants with fatty liver performed higher levels of BMI and waist circumference, as well as liver enzymes than no fatty liver counterparts (all $P \leq 0.05$). Baseline sociodemographic and biochemical characteristics of the entire study population were presented in Table 1.Table 1Baseline characteristics of the study population according to MH/MU and fatty liver status ($$n = 6260$$)No fatty liver (4301, $68.7\%$)Fatty liver (1959, $31.3\%$)P valueMHNHSMUNHSMHHSMUHSNo. of participants, n (%)1382 (22.1)2919 (46.6)63 (1.0)1896 (30.3) < 0.0001Age (years)54.7 ± 8.459.0 ± 8.753.6 ± 6.558.0 ± 8.2 < 0.0001Male sex, n (%)545 (39.4)995 (34.1)36 (57.1)688 (36.3) < 0.0001High school and above, n (%)353 (25.5)511 (17.5)19 (30.2)374 (19.7) < 0.0001Current drinking, n (%)310 (22.4)516 (17.7)22 (34.9)366 (19.3) < 0.0001Current smoking, n (%)366 (26.5)596 (20.4)27 (42.9)422 (22.3) < 0.0001Vigorous activity ≥ 75 min/week or moderate-vigorous ≥ 150 min/week, n (%)203 (14.7)457 (15.7)11 (17.5)343 (18.1)0.0454Hypertension, n (%)353 (25.5)1921 (65.8)15 (23.8)1407 (74.2) < 0.0001Anti-hypertensive medications, n (%)118 (8.6)876 (30.0)5 (8.1)790 (41.6) < 0.0001Type 2 diabetes, n (%)0 [0]516 (17.7)0 [0]691 (36.5) < 0.0001Anti-diabetic medications or insulin, n (%)0 [0]234 (8.0)0 [0]224 (11.8) < 0.0001Dyslipidemia, n (%)210 (15.2)1239 (42.5)17 (27.0)1179 (62.2) < 0.0001Lipid lowering medications, n (%)0 [0]7 (0.2)0 [0]7 (0.4)0.1665Body mass index (kg/m2)22.8 ± 2.324.9 ± 2.825.3 ± 2.027.6 ± 3.0 < 0.0001Waist circumference (cm)75.6 ± 6.581.9 ± 7.582.8 ± 5.789.2 ± 7.7 < 0.0001SBP (mmHg)127.7 ± 17.2144.2 ± 18.5128.6 ± 15.9147.1 ± 19.0 < 0.0001DBP (mmHg)77.6 ± 9.483.9 ± 9.880.0 ± 8.686.1 ± 10.1 < 0.0001PP (mmHg)50.1 ± 12.860.4 ± 15.448.6 ± 10.361.0 ± 16.1 < 0.0001Fasting plasma glucose (mg/dL)87.8 ± 8.099.4 ± 24.089.3 ± 9.0110.4 ± 35.5 < 0.0001Postprandial plasma glucose (mg/dL)103.9 ± 23.8146.4 ± 72.7110.6 ± 28.5185.3 ± 91.8 < 0.0001HbA1c (%)5.4 ± 0.35.8 ± 0.85.5 ± 0.36.2 ± 1.2 < 0.0001HOMA-IR1.0 (0.7–1.4)1.5 (1.1–2.2)1.4 (1.0–1.9)2.6 (1.8–3.8) < 0.0001Triglycerides (mg/dL)86.7 (68.1–111.5)123.9 (90.3–169.0)105.3 (84.1–125.7)167.3 (121.2–232.3) < 0.0001LDL-C (mg/dL)114.0 ± 28.6126.2 ± 33.6123.3 ± 31.0128.2 ± 35.3 < 0.0001HDL-C (mg/dL)57.9 ± 11.050.8 ± 12.354.6 ± 11.546.4 ± 10.4 < 0.0001Total cholesterol (mg/dL)196.8 ± 33.2208.1 ± 38.7204.8 ± 35.8213.2 ± 42.2 < 0.0001ALT (IU)15.6 (12.3–20.8)17.1 (13.4–22.5)19.2 (15.0–25.9)23.8 (17.6–34.7) < 0.0001AST (IU)20.9 (18.0–24.8)21.1 (18.2–24.7)22.0 (17.3–25.5)22.4 (19.1–27.6) < 0.0001GGT (IU)16.0 (12.0–24.0)20.0 (14.0–29.0)26.0 (15.0–48.0)30.0 (21.0–47.5) < 0.0001BaPWV (cm/s)1419.6 ± 284.91634.8 ± 350.31426.5 ± 218.51678.1 ± 357.7 < 0.0001UACR (mg/g)4.0 (2.4–6.6)4.8 (2.8–8.8)3.6 (2.4–5.1)5.9 (3.2–11.9) < 0.0001Values are means ± SD, medians (interquartile ranges) or numbers (proportions)SBP systolic blood pressure; DBP diastolic blood pressure; PP pulse pressure; HbA1c glycated hemoglobin; HOMA-IR homeostasis model assessment of insulin resistance; LDL-C low density lipoprotein cholesterol; HDL-C high density lipoprotein cholesterol; ALT alanine aminotransferase; AST aspartate aminotransferase; GGT gamma-glutamyl transferase; baPWV brachial–ankle pulse wave velocity; UACR urinary albumin-to-creatinine ratio; MHNHS metabolic healthy and no hepatic steatosis; MUNHS metabolic unhealthy and no hepatic steatosis; MHHS metabolic healthy and hepatic steatosis; MUHS metabolic unhealthy and hepatic steatosisP values were calculated from one-way ANOVA for continuous variables and χ2 test for categorical variables
## Risk of incident subclinical atherosclerosis (composite/separate) for participants with fatty liver in relation to the number of metabolic abnormalities
Figure 2 presented the associations of fatty liver combining increasing numbers of metabolic abnormalities (including metabolic risk factors and presence of diabetes) with incident subclinical atherosclerosis. Compared with participants without fatty liver or type 2 diabetes or any metabolic risk factors, fatty liver participants with increasing numbers of metabolic risk factors and diabetes tended to pose gradually incremental risk on the composite subclinical atherosclerosis (P for trend < 0.0001). The risk was most significant in the subgroup of combining with diabetes and metabolic risk factors (OR 5.14, $95\%$ CI 2.87–9.20). Similar significance and tendency were simultaneously observed in the risk of elevated baPWV (OR 5.24, $95\%$ CI 2.68–10.26) and elevated PP (OR 10.61, $95\%$ CI 4.58–24.60), whereas it was only prominent in the combination subgroup of albuminuria analysis (OR 3.64, $95\%$ CI 1.69–7.81). Those findings preliminarily indicated the dose-dependent effect of metabolic abnormalities on the poor prognosis of fatty liver population. Fig. 2Risk of incident subclinical atherosclerosis (composite/separate) for participants with fatty liver in relation to the number of metabolic abnormalities ORs ($95\%$ CIs) were adjusted for age, sex, follow-up interval, current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, baseline BMI and BMI change. NFL no fatty liver; FL fatty liver; DM diabetes mellitus; baPWV brachial–ankle pulse wave velocity; PP pressure pulse; OR odds ratio; CI confidential interval; BMI body mass index. * This group included type 2 diabetes participants with/without metabolic abnormalities
## Risk of subclinical atherosclerosis (composite/separate) according to combination of baseline MH/MU and fatty liver status
As shown in Table 2, during a median follow-up period of 4.3 years, $24.2\%$ ($\frac{902}{3734}$) of composite subclinical atherosclerosis were recorded, including $17.7\%$ ($\frac{827}{4667}$), $13.4\%$ ($\frac{627}{4668}$) and $8.3\%$ ($\frac{476}{5753}$) of elevated baPWV, elevated PP and albuminuria, respectively. Compared with MHNHS group, participants in MU status had an increased risk of incident composite subclinical atherosclerosis ([OR 1.66, $95\%$ CI 1.30–2.13] for MUNHS group and [OR 2.57, $95\%$ CI 1.90–3.48] for MUHS group) after full adjustments. Similar detrimental effects were investigated in the separate analysis of elevated baPWV and elevated PP. But for albuminuria, the risk was observed only in MUHS group (OR 1.81, $95\%$ CI 1.23–2.65). Furthermore, there was no significant association observed in MHHS group with regards to the risk of composite or separate subclinical atherosclerosis. The results tended to be more significant when excluding advanced stage, excessive alcohol consumption and other liver diseases (Additional file 1: Table S1).Table 2Risk of incident subclinical atherosclerosis (composite/separate) according to MH/MU and fatty liver status at baselineNCase, n (%)Model 1 OR ($95\%$ CI)P valueModel 2 OR ($95\%$ CI)P valueComposite *Subclinical atherosclerosis* ($\frac{902}{3734}$, $24.2\%$)MHNHS1092152 (13.9)1.00–1.00–MUNHS1617439 (27.2)1.95 (1.58–2.41) < 0.00011.66 (1.30–2.13) < 0.0001MHHS5410 (18.5)1.57 (0.76–3.25)0.22671.56 (0.68–3.56)0.2907MUHS971301 (31.0)2.60 (2.07–3.27) < 0.00012.57 (1.90–3.48) < 0.0001Elevated baPWV ($\frac{827}{4667}$, $17.7\%$)MHNHS1231118 (9.6)1.00–1.00–MUNHS2107430 (20.4)1.88 (1.50–2.37) < 0.00011.82 (1.38–2.40) < 0.0001MHHS576 (10.5)1.29 (0.53–3.17)0.57341.69 (0.62–4.57)0.3043MUHS1272273 (21.5)2.34 (1.83–2.99) < 0.00012.48 (1.78–3.45) < 0.0001Elevated PP ($\frac{627}{4668}$, $13.4\%$)MHNHS124771 (5.7)1.00–1.00–MUNHS2078338 (16.3)2.55 (1.94–3.35) < 0.00012.42 (1.75–3.36) < 0.0001MHHS584 (6.9)1.49 (0.52–4.31)0.45942.08 (0.70–6.20)0.1890MUHS1285214 (16.7)2.83 (2.12–3.77) < 0.00012.92 (2.00–4.26) < 0.0001Albuminuria ($\frac{476}{5753}$, $8.3\%$)MHNHS130270 (5.4)1.00–1.00–MUNHS2684211 (7.9)1.18 (0.88–1.57)0.26841.09 (0.77–1.53)0.6276MHHS633 (4.8)1.00 (0.30–3.28)0.99390.85 (0.20–3.63)0.8233MUHS1704192 (11.3)2.01 (1.50–2.69) < 0.00011.81 (1.23–2.65)0.0024Model 1 was adjusted for age, sex and follow-up interval;Model 2 was further adjusted for current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, baseline BMI and BMI changeMHNHS metabolic healthy and no hepatic steatosis; MUNHS metabolic unhealthy and no hepatic steatosis; MHHS metabolic healthy and hepatic steatosis; MUHS metabolic unhealthy and hepatic steatosis; baPWV brachial–ankle pulse wave velocity; PP pulse pressure; OR odds ratio; BMI body mass index
## Transition of metabolic status and the risk of composite subclinical atherosclerosis
Considering the transient nature of MH status, the transitional trajectory was further investigated among the general population. Overall, $17.6\%$ of the participants experienced metabolic transition and those who moved to or stayed in MU status tended to be older, with lower educational attainment and concomitantly higher levels of adipose, glucose, blood pressure and lipid parameters at baseline (Additional file 1: Table S2). When dividing the baseline population into two groups based on the presence of fatty liver at baseline, it seemed that participants with fatty liver disease were more prone to be remained in MU status ($90.7\%$ [$\frac{930}{1025}$] for fatty liver group vs$.50.8\%$ [$\frac{1376}{2709}$] for no fatty liver group) and less likely to regress to MH status ($4.0\%$ [$\frac{41}{1025}$] for fatty liver group vs. $8.9\%$ [$\frac{241}{2709}$] for no fatty liver group) (Fig. 3).Fig. 3ORs ($95\%$ CIs) of composite subclinical atherosclerosis across varying transition of metabolic status. ORs ($95\%$ CIs) were adjusted for age, sex, follow-up interval, current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, baseline BMI and BMI change. MH metabolic healthy; MU, metabolic unhealthy; OR odds ratio; CI confidential interval; BMI body mass index Taken the participants without fatty liver at baseline and maintaining MH status for reference, progressing from MH to MU status ([OR 2.52, $95\%$ CI 1.66–3.82] for no fatty liver group and [OR 3.11, $95\%$ CI 1.23–7.92] for fatty liver group) and maintaining MU status ([OR 3.27, $95\%$ CI 2.27–4.72] for no fatty liver group and [OR 4.87, $95\%$ CI 3.25–7.31] for fatty liver group) contributed to an increased risk of composite subclinical atherosclerosis, irrespective of fatty liver status at baseline. Additionally, it was noteworthy that the composite risk was not significantly increased when regressing to MH status. Sensitivity analysis showed comparable results when excluding the influence of metabolic related medications as well as advanced stage and other etiologies of fatty liver disease (Additional file 1: Tables S3 and S4).
## Modified effect on the risk of subclinical atherosclerosis when regressing from MU to MH status and associated improvement of metabolic risk factors
In the composite outcome analysis, there were 282 out of 2588 participants with MU status regressed to MH status, of which 32 ($11.4\%$) developed the established outcome, $\frac{3}{41}$ ($7.3\%$) for MUHS group and $\frac{29}{241}$ ($12.0\%$) for MUNHS group, respectively. *In* general, MU regression modified the risk of composite subclinical atherosclerosis compared with those remaining in MU status (OR 0.27, $95\%$ CI 0.17–0.43). This modification seemed to be more prominent in participants with fatty liver (OR 0.15, $95\%$ CI 0.04–0.64) than non-counterparts (OR 0.30, $95\%$ CI 0.18–0.49) and independent of metabolic medications as well as advanced stage and other etiologies of fatty liver disease (Fig. 4, Additional file 1: Tables S5 and S6). Separate analysis towards the risk of elevated baPWV, elevated PP and albuminuria were generally consistent with the composite outcome (Additional file 1: Tables S7 and S8).Fig. 4Modified effect of MU regression on the risk of composite subclinical atherosclerosis. ORs ($95\%$ CIs) were adjusted for age, sex, follow-up interval, current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, baseline BMI and BMI change. MH metabolic healthy; MU metabolic unhealthy; MUHS metabolic unhealthy and no hepatic steatosis; MUNHS metabolic unhealthy and hepatic steatosis; OR odds ratio; CI confidential interval; BMI body mass index Among baseline MUHS participants ($$n = 1896$$), 48 ($2.5\%$) regressed to MH status. In comparison with stable MU group, participants regressing to MH status tended to display a more significant improvement in repeated-measured levels of waist circumference, SBP, triglycerides, HDL-C, HbA1c, fasting and postprandial plasma glucose after full adjustments (Table 3). Specific changes of metabolic risk factors were presented in Additional file 1: Table S9.Table 3Adjusted ORs ($95\%$ CI) of metabolic regression with repeated-measured metabolic risk factors among baseline MUHS groupAge- and sex- adjustedMultivariable-adjusted†OR ($95\%$ CI)P valueOR ($95\%$ CI)P valueWaist circumference, cm0.92 (0.90–0.95) < 0.00010.91 (0.86–0.97)0.0015SBP, mmHg0.95 (0.93–0.97) < 0.00010.95 (0.92–0.98)0.0012DBP, mmHg0.95 (0.92–0.97) < 0.00010.998 (0.95–1.05)0.9285Triglycerides, mg/dL0.49 (0.31–0.75)0.00130.47 (0.23–0.96)0.0393HDL-C, mg/dL6.08 (2.86–12.94) < 0.00013.72 (1.13–12.28)0.0307FPG, mg/dL0.42 (0.29–0.62) < 0.00010.51 (0.33–0.79)0.00222 h-PG, mg/dL0.75 (0.71–0.78) < 0.00010.79 (0.70–0.89) < 0.0001HbA1c, %0.13 (0.07–0.23) < 0.00010.21 (0.08–0.55)0.0015HOMA-IR0.49 (0.38–0.64) < 0.00010.77 (0.54–1.10)0.1545BMI, kg/m20.78 (0.70–0.87) < 0.00010.97 (0.80–1.17)0.7637SBP systolic blood pressure; DBP diastolic blood pressure; HDL-C high density lipoprotein cholesterol; FPG fasting plasma glucose; HbA1c glycated hemoglobin; HOMA-IR homeostasis model assessment of insulin resistance; BMI body mass index; MUHS metabolic unhealthy and hepatic steatosis; OR odds ratio; CI confidence interval†Logistic regression models with generalized estimating equations were adjusted for age, sex, current smoking and drinking status (yes/no), education (≥ 12 years or not), log-transformed physical activity, waist circumference, SBP, DBP, triglycerides, HDL-C, FPG, 2 h-PG, HbA1c, HOMA-IR and BMI. All covariates with the exception for sex and education were repeated measured both at baseline and follow-up and modelled as time-varying variables
## Discussion
On the basis of metabolic abnormalities proposed by MAFLD criteria, this prospective cohort study evaluated the overall metabolic status among participants with/without fatty liver disease. Increased risk of subclinical atherosclerosis was observed in varying combinations of fatty liver status and metabolic abnormalities, defining as the presence of type 2 diabetes and/or ≥ 2 metabolic risk factors, except MHHS group. During the 4.3-year follow-up period, mostly proportions of participants either maintained MU status or progressed from MH to MU status, which impelled the development of subclinical atherosclerosis. In contrast to the metabolic progression, MU regression exhibited a mitigative effect on the composite risk, especially among participants with fatty liver disease. MUHS participants regressing to MH status tended to have a more prominent improvement on waist circumference, SBP, triglycerides, HDL-C, HbA1c, fasting and postprandial plasma glucose.
Since changing term from NAFLD to MAFLD, fatty liver disease associated with metabolic abnormalities is of growingly public health concern. Previous evidence has been explicit that NAFLD or the new term MAFLD was an independent risk factor for both clinical cardiovascular events [23–25] and subclinical atherosclerotic lesion [26, 27]. Results in the prior half of the current study were generally in line with previous cohort findings, supporting the notion that fatty liver disease combining with metabolic abnormalities synergistically contributed to the increased risk of subclinical atherosclerosis, reflected by artery stiffness (elevated baPWV and PP) and endothelial dysfunction (albuminuria). MHHS group, representing the phenotype who developed simple hepatic steatosis ahead of metabolic dysfunction, did not show significant association with the subclinical atherosclerotic risk in our cohort. The results were consistent with a Korean cross-sectional study comparing hepatic fibrosis and cardiovascular risk among the MH-MAFLD with healthy control [10]. Whether the neutralized effect or the limitation of sample size matters, more data are needed in the future.
On the other hand, metabolic status transition is complex and dynamically changing over time. Nevertheless, long-term data regarding the metabolic status transition in fatty liver population are still limited. Previous data were mainly referred to metabolic syndrome among participants with obesity. Corresponding transition rate of MH progression was about $40\%$ to $50\%$ over 8–20 follow-up years in different populations [28–31]. Additionally, the MH individuals had the potential to progress to MU status over time across all BMI categories. The transition accompanying with a greater degree of adiposity sequenced a higher cardiovascular risk [32]. Even though the follow-up duration of our cohort was relatively short, we observed $17.6\%$ of metabolic transition, in which participants characterized with older age, lower educational attainment and higher levels of cardiometabolic parameters, even in the normal-high range, were prone to progress to MU status. Additionally, participants with fatty liver were more predisposed to be stuck in the MU status, which contributed to a more evident risk of developing subclinical atherosclerosis. Pooling all the evidence highlighted the necessity of close monitor and prompt intervention to impede the metabolic progression, especially among those who were concomitant with general or visceral fat deposition.
In addition to the metabolic progression, regressing from MU to MH status benefits not only to the systematic metabolic profile but also to the related complications. In a Korean nationwide cohort study, regression of MU status among participants with normal weight and obesity was significantly related to decreased risk of incident cardiovascular events and all-cause mortality [33]. Similarly, the evident benefit on the composite risk was discovered in the current study when regressing to MH status, especially among participants with fatty liver disease. This beneficial effect was not influenced by metabolic medications and more likely to be attributed to lifestyle modification, indicating its cornerstone position in the treatment of fatty liver disease. Furthermore, findings from our previous research manifested that baseline MAFLD participants with low probability of fibrosis regressing to non-MAFLD at follow-up decreased the risk of elevated baPWV by $43.1\%$ [27]. Findings may need to be verified in a larger-scale and longer-term cohort, nevertheless, our study provided the evidence to some extent that recommendations for the primary cardiometabolic risk prevention should not overlook the importance of maintaining MH status regardless of fatty liver status.
The precise determinants responsible for MH progression have not been fully understood. A multitude of factors, including genetics, age, waist circumference, BMI, lipids, glycemic parameters, poor dietary quality, physical inactivity and gut microbiota, interact in a complex and dynamic manner to influence individuals’ MH status [34–36]. Moreover, accumulating visceral fat was found to be associated with progressing to MU phenotype while decreasing visceral fat mass was associated with MU regression [37]. A recent prospective cohort study also manifested that the presence of NAFLD facilitated both MH-obesity progression and 10-year cardiovascular disease risk [38]. Our study further investigated that participants with MU regression were more likely to be associated with improvement of waist circumference, blood pressure, lipids and glucose, which implicated the priority to pay constant attention to among fatty liver population. Findings from a nationwide cohort study also demonstrated that if NAFLD participants with prediabetes or diabetes could achieve ≥ 2 of metabolic goals towards glycemia, blood pressure or lipids, risk of cardiovascular and chronic kidney disease would be mitigated [39]. Pathophysiology of increased visceral fat mass in relation to atherosclerosis and cardiometabolic diseases can be mainly explained by insulin resistance, subclinical inflammation, dysregulated adipokine secretion and increased release of fatty acids into the circulation, implying potential targets of therapy for fatty liver disease and metabolic comorbidities [40, 41]. Experimental studies have shown that diets enriched with omega-3 polyunsaturated fatty acids increase insulin sensitivity, reduce intrahepatic triglyceride content and ameliorate steatohepatitis [42].Moreover, the Mediterranean diet plays a beneficial role in metabolic profile and has been shown to reduce the risk of cardiovascular disease and diabetes, two outcomes highly relevant in NAFLD patients [43, 44].
A close relationship between glycemic control and fatty liver disease was found in this study. Given the most evident significance was presented in the group of fatty liver combining with diabetes and glucose parameters were all involved in the MU regression, glycemic control would be a more prominent target towards related prognosis among fatty liver population.
To our knowledge, this was the first prospective study with the specific aim to evaluate the associations of overall metabolic status among participants with/without fatty liver disease, and further metabolic transition with the risk of incident subclinical atherosclerosis, under the context of metabolic abnormalities proposed by MAFLD definition. The strengths of our study included the prospective design and the investigation of effects towards varying metabolic transition on incident subclinical atherosclerosis risk. Factors in relation to the metabolic regression among fatty liver population were further explored using the repeated measures analysis. Our findings provided certain evidence towards the critical role of metabolic abnormalities in the course of fatty liver disease, as highlighted in MAFLD definition. Additionally, MHHS participants were not definitely found to be associated with subclinical atherosclerosis risk during the follow-up period, while baseline characteristics with the youngest age, highest level of education, as well as drinking and smoking habits, indicating the optimal matching to long-term lifestyle intervention. For MU individuals, accurate assessment, targeted treatment and intensive follow-up were necessary to impede the progressive course, especially among patients who exposed to continuous hepatic steatosis.
Several limitations still merited to mention. First, the inflammatory indicator of high-sensitive C-reaction protein was not measured or included in the criteria of MU status. Second, the current study may not be sufficiently powered to assess the associations of MHHS and further MU regression with subclinical atherosclerotic risk, due to the relatively small sample and short follow-up duration. Third, causal explanations were not accessible with a single follow-up visit, in that the metabolic transition and subclinical outcomes developed in parallel. Fourth, confounders were adjusted based on knowledge of their associations with MAFLD and subclinical atherosclerosis. Lifestyle modification, such as smoking, alcohol drinking, physical activity and body weight management, remains the cornerstone for MAFLD treatment [45]. Additionally, improving cardiovascular health has been a target for prevention of MAFLD as well as subclinical atherosclerosis and cardiovascular disease [15, 46]. Despite of the discreet adjustments for potential confounders, the possibility of residual confounding factors due to uncollected variables such as dietary information cannot be excluded. Therefore, those significant implications warranted to be consolidated in a cohort with wider-generalizability.
## Conclusion
In summary, the current study built upon the emphasis to better evaluate overall metabolic status and drew a synergistic effect of fatty liver disease along with metabolic abnormalities on the increased risk of subclinical atherosclerosis. During up to 5-year follow-up, participants who were caught in the metabolic exacerbation had an increased risk of subclinical atherosclerosis, while fatty liver participants achieving MU regression were more intended to mitigate the risk. Undoubtedly, MH warrants to be maintained and defended via a comprehensive management, especially the improvement of waist circumference, blood pressure, glucose and lipids among fatty liver population which were highlighted in our study. In the era of precision medicine, multifaceted risk stratification will conduce to optimize the efficacy and cost-effectiveness of diagnosis and targeted intervention.
## Supplementary Information
Additional file 1. Table S1. Risk of incident subclinical atherosclerosis (composite/separate) according to MH/MU and fatty liver status at baseline excluding FIB-4 > 2.67, other liver diseases and excessive alcohol consumption. Table S2. Baseline characteristics according to the metabolic transition. Table S3. Transition of MH status and the risk of composite subclinical atherosclerosis without glucose-, blood pressure- or lipid-lowering pharmacological treatment. Table S4. Transition of MH status and the risk of composite subclinical atherosclerosis excluding FIB-4 > 2.67, other liver diseases and excessive alcohol consumption. Table S5. Effect of metabolic status improvement on composite risk of subclinical atherosclerosis among MU population without glucose-, blood pressure- or lipid-lowering pharmacological treatment. Table S6. Effect of metabolic status improvement on composite risk of subclinical atherosclerosis among MU population excluding FIB-4 > 2.67, other liver diseases and excessive alcohol consumption. Table S7. The effect of metabolic status improvement on separate subclinical atherosclerosis risk among MU population. Table S8. The effect of metabolic status improvement on separate subclinical atherosclerosis risk among MU population without glucose-, blood pressure- or lipid-lowering pharmacological treatment. Table S9. Changes of metabolic risk factors between stable MU and MU to MH groups among baseline MUHS participants. Table S10. Baseline characteristics of participants included and those lost to follow-up.
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|
---
title: Associations of accelerometer-based sedentary bouts with adiposity markers
among German adults – results from a cross-sectional study
authors:
- Lisa Voigt
- Antje Ullrich
- Stefan Groß
- Diana Guertler
- Lina Jaeschke
- Marcus Dörr
- Neeltje van den Berg
- Ulrich John
- Sabina Ulbricht
journal: BMC Public Health
year: 2023
pmcid: PMC10007749
doi: 10.1186/s12889-023-15304-8
license: CC BY 4.0
---
# Associations of accelerometer-based sedentary bouts with adiposity markers among German adults – results from a cross-sectional study
## Abstract
### Background
Long periods of uninterrupted sitting, i.e., sedentary bouts, and their relationship with adverse health outcomes have moved into focus of public health recommendations. However, evidence on associations between sedentary bouts and adiposity markers is limited. Our aim was to investigate associations of the daily number of sedentary bouts with waist circumference (WC) and body mass index (BMI) in a sample of middle-aged to older adults.
### Methods
In this cross-sectional study, data were collected from three different studies that took place in the area of Greifswald, Northern Germany, between 2012 and 2018. In total, 460 adults from the general population aged 40 to 75 years and without known cardiovascular disease wore tri-axial accelerometers (ActiGraph Model GT3X+, Pensacola, FL) on the hip for seven consecutive days. A wear time of ≥ 10 h on ≥ 4 days was required for analyses. WC (cm) and BMI (kg m− 2) were measured in a standardized way. Separate multilevel mixed-effects linear regression analyses were used to investigate associations of sedentary bouts (1 to 10 min, >10 to 30 min, and >30 min) with WC and BMI. Models were adjusted for potential confounders including sex, age, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use.
### Results
Participants ($66\%$ females) were on average 57.1 (standard deviation, SD 8.5) years old and $36\%$ had a school education >10 years. The mean number of sedentary bouts per day was 95.1 (SD 25.0) for 1-to-10-minute bouts, 13.3 (SD 3.4) for >10-to-30-minute bouts and 3.5 (SD 1.9) for >30-minute bouts. Mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m− 2 (SD 3.8). The daily number of 1-to-10-minute bouts was inversely associated with BMI (b = -0.027; $$p \leq 0.047$$) and the daily number of >30-minute bouts was positively associated with WC ($b = 0.330$; $$p \leq 0.001$$). All other associations were not statistically significant.
### Conclusion
The findings provide some evidence on favourable associations of short sedentary bouts as well as unfavourable associations of long sedentary bouts with adiposity markers. Our results may contribute to a growing body of literature that can help to define public health recommendations for interrupting prolonged sedentary periods.
### Trial registration
Study 1: German Clinical Trials Register (DRKS00010996); study 2: ClinicalTrials.gov (NCT02990039); study 3: ClinicalTrials.gov (NCT03539237).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15304-8.
## Introduction
Higher amounts of sedentary behaviour have been found to be associated with a range of health risks including incidence of cardiovascular disease [1, 2], type-2 diabetes [1, 3, 4], and cardio-metabolic risk factors such as higher waist circumference (WC) and body mass index (BMI) [4]. Also, evidence on detrimental relationships of total sedentary time with cardiovascular [3, 5] and all-cause mortality [3, 6] has accumulated. Amongst other eminent scientific authorities [7, 8], the World Health Organization recommends in their recently updated guidelines on physical activity and sedentary behaviour that ‘adults should limit the amount of time spent being sedentary’ [9].
In addition to limiting total sedentary time, several countries e.g., Australia [10], Canada [11], Germany [12], or the United Kingdom [13] have included the recommendation to break up prolonged periods of sitting in their national public health guidelines. Since the 2000s, evidence on deleterious effects of sedentary behaviour patterns has grown suggesting that prolonged sitting periods without interruptions may increase cardio-metabolic health risks in addition to those raised from total amounts of sedentary time. According to the Sedentary Behavior Research Network, sedentary behaviour patterns can be defined as ‘the manner in which sedentary behaviour is accumulated throughout the day or week while awake’, with a sedentary bout being defined as ‘a period of uninterrupted sedentary time’ and a sedentary break as ‘a non-sedentary bout in between two sedentary bouts’ [14]. There is some evidence from observational studies on associations between various indicators of sedentary behaviour patterns and cardio-metabolic biomarkers, such as WC [15–20], BMI [17, 18, 20, 21], triglycerides [15, 17, 20], and 2-hour plasma glucose [15]. Few studies have investigated prospective outcomes such as incidence of cardiovascular disease [2, 22] or all-cause mortality [22–26]. As findings on deleterious associations are not consistent across and within studies, experts come to conclude that the existing evidence to date remains insufficient and inconclusive [7, 9, 27].
Sedentary bouts and breaks have been operationalized using various minimum durations with the study that first introduced the concept classifying each ≥ 1 min interruption after ≥ 1 min of sedentary time as a break [15, 16]. The choice of minimum durations affect the number of both breaks and bouts being observed [28]. In the aforementioned study [15], quartiles of breaks in sedentary time with metabolic risk variables were reported showing that participants with more than 673 sedentary breaks across the entire data-collection period had a significantly lower WC than those with less than 506 breaks. However, these results may be hard to translate into a feasible and acceptable public health message. Thus, several studies used cut offs in order to differentiate between sedentary bouts with respect to their length [17–19, 24, 26]. Among the variety of thresholds applied, the most frequently used was > or ≥ 30 min, respectively, in order to examine health risks of prolonged sitting periods. To additionally differentiate between bouts of short and moderate length and yet with respect to practicability of sedentary interruptions in everyday life, setting another threshold at a duration of not shorter than 10 min seems reasonable.
One issue that has been discussed in the literature is, whether benefits of sedentary breaks simply reflect favourable effects of higher amounts of physical activity that is performed during those breaks [27]. Most of the aforementioned studies took account of total sedentary time and moderate-to-vigorous physical activity (MVPA) as potential confounders. However, some studies did not adjust for light physical activity (LPA) [2, 15, 17, 19, 23], possibly due to problems arising from collinearity between the device-based measures. In recent years, compositional data analysis (CoDA) [29, 30] has gained more and more attention, as this approach enables to simultaneously account for relative amounts of total sedentary time, LPA, and MVPA. Thus, applying CoDA strengthens the rationale behind the attempt to answer the research question whether to promote breaking up sedentary time in addition to already established recommendations on increasing physical activity and reducing total amounts of sedentary time.
Among the variety of cardio-metabolic biomarkers, WC and BMI are easy to assess and with results straightforward to communicate to the public. To our best knowledge, this is the first study among German adults that investigated associations between sedentary behaviour patterns and cardio-metabolic biomarkers. Thus, the aim of our study was to investigate associations of short sedentary bouts with a length of 1 to 10 min, moderate sedentary bouts with a length of >10 to 30 min, and long sedentary bouts with a length of >30 min with two indicators of adiposity, i.e., WC and BMI, accounting for accelerometer-based time-use compositions (i.e., total sedentary time and physical activity).
## Participants and procedure
We combined socio-demographic, anthropometric, and accelerometer data from apparently healthy adults, collected in three previous studies. All studies took place in the area of Greifswald in Northern Germany between 2012 and 2018. Detailed description of the design and sampling procedures for each study are reported elsewhere [31–33]. In short, study 1 (number of the ethical approval: BB $\frac{64}{07}$) [31] was a cross-sectional study comprising a two-stage cardio-preventive screening and examination program. Participants were recruited in general practices, job centres, and via statutory health insurance between June 2012 and December 2013. A subsample of 231 participants wore an accelerometer for seven consecutive days. Study 2 (BB $\frac{002}{15}$a) [32] was a longitudinal study to investigate the feasibility of a computerized, tailored letter intervention to increase physical activity and to reduce sedentary time. The sample was randomly drawn from those of study 1 who agreed to be contacted again. The study was conducted between February 2015 and August 2016. For the present analysis, only data from baseline measurements were used derived from a sample of 175 participants. Further, participants from study 2 were excluded if they already participated in accelerometry in study 1. Study 3 (BB $\frac{076}{18}$) [33] was a cross-sectional study to investigate the agreement of self-reported and accelerometer-based physical activity measures. Participants were recruited at a shopping mall between May and December 2018 and the final sample comprised 365 individuals.
Data from participants were included in the current analyses if (i) socio-demographic, anthropometric, and accelerometer data were complete, (ii) participants had no history of cardiovascular events (myocardial infarction, stroke) or vascular intervention, no diabetes mellitus, and a BMI ≤ 35 kg m− 2, (iii) the same accelerometer wearing protocol was applied for all participants, and (iv) the accelerometer was worn for ≥ 10 h on ≥ 4 days, regardless of whether these days were weekend days or not [34]. The total sample of the present analysis comprised 460 participants.
## Waist circumference and body mass index
WC (cm) and BMI (kg m− 2) were assessed at the cardiovascular examination center of the University Medicine Greifswald by trained and certified medical staff. WC was measured midway between lowest rib and iliac crest using an inelastic tape. Body weight and height were measured with digital scales (Soehnle Industrial solutions GmbH, catalog number SOEHNLE 7720 and ADE GmbH & Co., catalog number MZ 10020, respectively). BMI was calculated by dividing body weight in kg by height in m squared.
## Accelerometer-based measures
Physical activity and sedentary time were obtained using tri-axial ActiGraph Model GT3X + accelerometers (Pensacola, FL) worn on the right hip attached to an elastic belt for seven consecutive days. Participants were instructed to wear the accelerometer during waking hours and to put it off for water-based activities such as morning hygiene or swimming. Using ActiLife version 6.13.3 (ActiGraph, Pensacola, FL), the accelerometers were initialized at a sampling rate of 100 Hz (study 1 and 2) or 30 Hz (study 3) and raw data were integrated into 10 s epochs. Data from the vertical axis were used. ActiGraph accelerometers provide counts as the output metric. To identify accelerometer wear time as well as time spent in different intensities of physical activity, intensity cut points were applied according to Troiano and colleagues [35]: Wear time was determined by removing non-wear time defined as at least 60 min of consecutive zero counts, allowing for 2 min of counts between 0 and 100. Time spent in MVPA was determined by summing minutes per day where the accelerometer count met the intensity-threshold criterion of 2020 counts/minute (i.e., activities of three metabolic equivalents of task or more such as brisk walking). LPA was defined as 100–2019 counts/minute. Time with less than 100 counts/minute was defined as sedentary time [35]. Because time spent in physical activity and time spent sedentary are compositional components of total time (i.e., accelerometer wear time), these variables were expressed as proportions of total time (sedentary time, LPA, and MVPA) and then isometric log-ratio transformed [22, 30] to the following z parameters that were subsequently used as covariates in analyses.
1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z_1}\, = \,\surd \frac{2}{3}{\rm{ln}}\frac{{sedentary\,time}}{{\sqrt {LPA\,{\rm{x}}\,MVPA} }}$$\end{document} 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z_2}\, = \,\surd \frac{1}{2}{\rm{ln}}\frac{{LPA}}{{MVPA}}$$\end{document} A sedentary bout ended when sedentary time was interrupted for ≥ 1 min in which the accelerometer count rose up to or above 100 counts/minute. The mean daily number of bouts with a length of 1 to 10 min, >10 to 30 min, and >30 min was analysed.
## Covariates
Sex, age, school education (< 10 years, 10 years, >10 years), employment (employed, unemployed or retired), and current smoking (yes, no) were obtained by a self-administered questionnaire. Variables related to data collection included study (study 1, study 2, study 3) and season of data collection (spring or summer, autumn or winter).
## Statistical analysis
Multilevel mixed-effects linear regressions were used to examine the associations of sedentary bouts with WC and BMI including study as a higher-level group variable. Models were estimated using the xtmixed command in Stata version 15.1 (Stata Corp, 2017). A maximum likelihood estimator with robust standard errors was chosen. P values below 0.05 were considered significant. Several models were calculated for each outcome in the following way. First, WC was regressed on the number of sedentary 1-to-10-minute bouts per day with adjustment for basic covariates including sex and age (basic model). Second, the following covariates were added to the model: school education, employment, current smoking, season of data collection [36], and composition of accelerometer-based time use in terms of the isometric log-ratio transformed z parameters (adjusted model). Adding the latter enabled to account for potential confounding of associations between sedentary bouts and adiposity markers with relative amounts of total sedentary time and physical activity. Likelihood ratio tests were used to compare models including a quadratic term of the continuous covariate age to linear models in order to test for nonlinearity. Third, the associations of the number of sedentary >10-to-30-minute bouts and >30-minute bouts with WC were analysed in one basic model and one adjusted model each. The same procedure was applied to analyse the associations between sedentary bouts and BMI. To provide a visual representation of the associations in the adjusted models, marginal means for the associations of quartiles of bouts with WC and BMI were estimated and presented in column diagrams. Secondary analyses were conducted separately for women and men.
## Sample characteristics
Characteristics of the total sample ($$n = 460$$) and separately for women ($66\%$) and men are described in Table 1. Participants were, on average, 57.1 (standard deviation, SD 8.5) years old, $36\%$ were highly educated. The mean WC was 91.1 cm (SD 12.3) and mean BMI was 26.9 kg m− 2 (SD 3.8). On average, participants wore the accelerometer for 14.7 h day− 1 (SD 1.5) and spent 10.1 h day− 1 (SD 1.7) sedentary, 3.8 h day− 1 (SD 1.0) in LPA and 0.8 h day− 1 (SD 0.5) in MVPA. Further details on accelerometer-based measures are shown in Table 1.
Table 1Sample characteristics ($$n = 460$$)VariablesOverall ($$n = 460$$)Women ($$n = 303$$)Men ($$n = 157$$)ValuesValuesValues p Age (years)57.1 ± 8.557.2 ± 8.456.9 ± 8.70.708School education0.771 <10 years33 (7.1)20 (6.6)13 (8.3) 10 years262 (57.0)175 (57.8)87 (55.4) >10 years165 (35.9)108 (35.6)57 (36.3)Employment, unemployed or retired163 (35.4)113 (37.3)50 (31.9)0.247Current smoking, yes80 (17.4)55 (18.1)25 (15.9)0.550Season of data collection0.964 Spring or summer279 (60.7)184 (60.7)95 (60.5) Autumn or winter181 (39.3)119 (39.3)62 (39.5)Study0.311 Study 1104 (22.6)62(20.5)42 (26.8) Study 2108 (23.5)73 (24.1)35 (22.3) Study 3248 (53.9)168 (55.5)80 (51.0)Accelerometer wear time (hours day− 1)14.7 ± 1.514.6 ± 1.514.7 ± 1.60.500Moderate-to-vigorous physical activity (hours day− 1)0.8 ± 0.50.7 ± 0.40.9 ± 0.5< 0.001Light physical activity (hours day− 1)3.8 ± 1.03.9 ± 1.03.5 ± 1.1< 0.001Sedentary time (hours day− 1)10.1 ± 1.710.0 ± 1.610.3 ± 1.90.127Number of sedentary 1-to-10-minute bouts day− 195.1 ± 25.098.5 ± 23.288.5 ± 27.0< 0.001Number of sedentary >10-to-30-minute bouts day− 113.3 ± 3.413.2 ± 3.213.5 ± 3.80.350Number of sedentary >30-minute bouts day− 13.5 ± 1.93.3 ± 1.73.9 ± 2.1< 0.001Mean daily time (minutes) of 1-to-10-minute bouts3.2 ± 0.33.2 ± 0.33.2 ± 0.40.085 >10-to-30-minute bouts16.7 ± 1.016.6 ± 0.916.9 ± 1.10.002 >30-minute bouts41.8 ± 8.941.3 ± 9.142.6 ± 8.40.153 Breaks after 1-to-10-minute bouts2.4 ± 1.12.3 ± 0.92.6 ± 1.40.005 Breaks after >10-to-30-minute bouts2.2 ± 2.92.1 ± 2.12.3 ± 3.90.402 Breaks after >30-minute bouts2.8 ± 5.92.8 ± 5.92.8 ± 5.80.982Sedentary 1-to-10-minute bouts as proportion of sedentary time (%)45.1 ± 12.246.5 ± 11.042.3 ± 13.9< 0.001Sedentary >10-to-30-minute bouts as proportion of sedentary time (%)32.3 ± 6.432.0 ± 6.032.8 ± 7.00.226Sedentary >30-minute bouts as proportion of sedentary time (%)22.6 ± 11.421.5 ± 10.624.9 ± 12.60.002Waist circumference (cm)91.1 ± 12.386.7 ± 11.099.6 ± 10.1< 0.001Body mass index (kg m− 2)26.9 ± 3.826.3 ± 3.928.2 ± 3.3< 0.001Data are presented as mean ± standard deviation for continuous variables and as the number of participants (%) for categorical variables. Presented p-values for comparisons between women and men are based on t-test for continuous variables and chi-square test for categorical variables
## Associations between number of sedentary bouts and adiposity markers
The number of sedentary 1-to-10-minute bouts per day were inversely related with WC and BMI in the basic models (b = -0.048; $$p \leq 0.014$$ and b = -0.018; $p \leq 0.001$, respectively; Table 2). In the adjusted models, the association with WC became non-significant (b = -0.063; $$p \leq 0.131$$), whereas the association with BMI remained significant (b = -0.027; $$p \leq 0.047$$). Associations between the number of sedentary >10-to-30-minute bouts per day with WC and BMI were not significant both in the basic models ($b = 0.053$; $$p \leq 0.551$$ and $b = 0.018$; $$p \leq 0.516$$, respectively) and in the adjusted models (b = -0.139; $$p \leq 0.401$$ and b = -0.036; $$p \leq 0.262$$, respectively). The number of >30-minute bouts per day was positively associated with WC ($b = 0.590$; $$p \leq 0.004$$) and BMI ($b = 0.184$; $$p \leq 0.009$$) in the basic models. In the adjusted models, the association with WC was attenuated but remained significant ($b = 0.330$; $$p \leq 0.001$$) whereas the association with BMI was attenuated and no longer significant ($b = 0.113$; $$p \leq 0.184$$).
Table 2Multilevel mixed-effects linear regression models of the association of sedentary bouts with adiposity markers ($$n = 460$$)Basic model aAdjusted model bCoef$.95\%$ CI p Coef$.95\%$ CI p Dependent variable: waist circumference (cm) Number of sedentary 1-to-10-minute bouts day− 1− 0.048− 0.086− 0.0090.014− 0.063− 0.1140.0190.131 Number of sedentary >10-to-30-minute bouts day− 10.053− 0.1210.2270.551− 0.139− 0.4630.1850.401 Number of sedentary >30-minute bouts day− 10.5900.1920.9870.0040.3300.1320.5290.001Dependent variable: body mass index (kg m− 2) Number of sedentary 1-to-10-minute bouts day− 1− 0.018− 0.020− 0.015< 0.001− 0.027− 0.055− 0.0000.047 Number of sedentary >10-to-30-minute bouts day− 10.018− 0.0360.0700.516− 0.036− 0.0990.0270.262 Number of sedentary >30-minute bouts day− 10.1840.0450.3220.0090.113− 0.0540.2810.184Coef. unstandardized regression coefficient, CI confidence intervala Adjusted for sex, age, and age squared. b Adjusted for sex, age, age squared, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use (z1 and z2)Study was included as a higher-level group variable. Likelihood ratio tests were used to decide on the inclusion of age squared in the models To provide a visual representation of the effect size of the associations in the adjusted models, Fig. 1 shows the estimated marginal means for the associations of quartiles of the number of sedentary bouts per day with WC and BMI. Compared to those in the lowest quartile of 1-to-10-minute bouts, those in the third quartile had, on average, a 3.73 cm lower WC (p = < 0.001) and a 0.66 kg m− 2 lower BMI (p = < 0.001) whereas those in the highest quartile had a 3.51 cm lower WC ($$p \leq 0.001$$) and a 1.19 kg m− 2 lower BMI ($$p \leq 0.017$$). Compared to those in the lowest quartile of >30-minute bouts, those in the highest quartile had a 1.44 cm higher WC ($$p \leq 0.040$$). The results for analyses separated by sex are presented in additional files [see Supplementary Material 1 and 2].
Fig. 1Quartiles of the number of sedentary 1-to-10-minute bouts (a and b), >10-to-30-minute bouts (c and d) and >30-minute bouts per day (e and f) with waist circumference (left column) and body mass index (right column). Multilevel mixed-effects linear regression models included study as higher-level group variable. Estimated marginal means ($95\%$ CI) adjusted for sex, age, age squared, school education, employment, current smoking, season of data collection, and composition of accelerometer-based time use (i.e., total time spent sedentary as well as in light and moderate-to-vigorous physical activity. Cut points for quartiles were 76.84, 93.71, and 111.71 bouts per day for 1-to-10-minute bouts; 10.85, 13.35, and 15.71 bouts per day for >10-to-30-minute bouts; and 2.16, 3.15, and 4.50 bouts per day for >30-minute bouts; *$P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$ compared to quartile 1
## Discussion
In this observational study using combined data from three different studies, we examined cross-sectional associations of short (1 to 10 min), moderate (>10 to 30 min), and long (>30 min) sedentary bouts with WC and BMI in subjects without prevalent cardiovascular disease. Our data revealed three main findings: first, there was a statistically significant inverse relationship of the daily number of short sedentary bouts with BMI but not with WC. Second, the daily number of moderate sedentary bouts was not related to WC or BMI. Third, the daily number of long sedentary bouts was significantly associated with a higher WC but not with BMI.
A number of studies investigated associations between sedentary behaviour patterns and obesity metrics [15–21]. However, it is difficult to compare reported results due to methodological differences including, but not limited to, sensing method of the device (accelerometer-based or inclinometer-based), the cut-points chosen to classify sedentary behaviour, and applied definitions of sedentary bouts and breaks [28]. Our results are in line with a number of previous studies that found modest associations of sedentary behaviour patterns with WC [15–18, 21] and BMI [15, 17, 18, 21]. For example, an Australian observational study among 678 middle-aged to older adults investigating various inclinometer-based measures showed that frequently interrupted sitting (compared to patterns with relatively more prolonged sitting) was beneficially associated with WC and BMI [17]. In a Danish study among 692 blue-collar workers applying isotemporal substitution modelling, it was found that replacing long sedentary bouts (>30 min) with brief sedentary bouts (≤ 5 min) was associated with lower levels of adiposity markers including WC and BMI [18]. A meta-analysis published in 2015 investigated the relationship between the frequency of sedentary interruptions and cardio-metabolic health in adults [21]. Results from the included observational studies revealed inverse associations of interruptions with WC and BMI.
In our study, associations of short sedentary bouts were only statistically significant for BMI but not for WC. However, associations of quartiles of short bouts showed that compared to those with the lowest number of bouts (i.e., first quartile) individuals with the highest number of bouts (i.e., third and fourth quartile) had not only a significantly lower BMI, but also a significantly lower WC. Thus, the relationship with WC may be curvilinear (Fig. 1a) which may be the reason why the linear association was not statistically significant. In contrast to the aforementioned studies [17, 18, 21], we did not find a statistically significant association of long sedentary bouts with BMI. However, some of these studies did not account for LPA [15, 17] or total sedentary time [17] in some of their investigated associations which may have led to over-estimated relationships of sedentary behaviour patterns with BMI. For example, in a Finish cohort study comparing groups with different profiles of sedentary accumulation patterns, statistically significant associations of more fragmented patterns with lower BMI disappeared after adjustment for total sedentary time [20]. In our analysis, we simultaneously accounted for amounts of total sedentary time, LPA, and MVPA by applying CoDA, which may be a contributing factor to the difference in findings on BMI compared to previous studies. Further, it should be noted that the high complexity of obesity-related health risks may not be captured by weight-based measures such as BMI [37, 38]; and that BMI has been shown to be less accurate in health risk prediction than measures of body fat distribution including WC [39, 40].
Our results provide some evidence for beneficial associations of short sedentary bouts and for unfavourable associations of long sedentary bouts with adiposity markers. These findings suggest that obesity-related risk factors might be improved if sitting time is frequently interrupted and if sitting periods that last longer than 30 min are avoided. It has been discussed in the literature, whether benefits of sedentary breaks solely stem from favourable effects of higher amounts of physical activity [27]. As we found statistically significant associations of sedentary bouts with adiposity markers after simultaneous adjustment for MVPA and LPA as well as total sedentary time, the accumulation pattern of sedentary time seems to be a relevant factor. Indeed, suggestions have been made on the physiological mechanisms underlying the beneficial effects of regularly interrupting prolonged sitting on the cardiovascular system, such as the maintenance of the muscle pump and blood flow [41]. From a public health perspective, the results of this study provide some indication that obesity-related health risks may be improved if total sedentary time is accumulated in more short and fewer long sedentary bouts. Whilst interrupting sitting every thirty minutes might be feasible and acceptable, breaking up sitting every ten minutes seems highly impractical to be implemented in everyday life. Thus, defining quantitative recommendations on specific thresholds of bout durations remains a challenge.
## Strengths and limitations
This study investigated sedentary behaviour patterns measured by device in a moderate-sized sample of middle-aged to older adults in Germany. Results add data to the literature on associations between uninterrupted sitting time and adiposity markers. We used short, moderate, and long sedentary bouts as measures of sitting patterns and we addressed WC and BMI as generally acknowledged health risk factors to enable inferring a straightforward public health message. Furthermore, we adjusted our analyses for the composition of accelerometer-based time use to draw conclusions on the benefit of interrupting sedentary time in addition to total sedentary time and physical activity levels.
Some limitations of this study should be considered. First, our findings may not be generalizable to the whole general population. Similar to other accelerometer studies, the proportion of non-participants was high and selection bias of highly motivated and physically active individuals is likely. Second, hip-worn accelerometers used in this study assess sedentary time from data indicating a lack of movement (< 100 counts/minute) compared to more movement (≥ 100 counts/minute). As other stationary behaviour such as standing may be captured below this threshold, sitting data might be biased [42–44]. As discussed above, however, our data revealed results similar to those of a previous study using inclinometers [17], which assess body posture to classify sedentary time. Third, some participants in our study may have worn the accelerometer during night’s sleep, indicated by high amounts of wear time (e.g., a number of seven participants ($1.5\%$) showed an average daily wear time of >20 h per day. Thus, there is the possibility of sedentary time being conflated with sleep time if the accelerometer has not been removed [45]. A sensitivity analysis under exclusion of the seven participants revealed results similar to the results of our main analyses. Only the association between >30-minute bouts and WC was no longer significant ($b = 0.395$; $$p \leq 0.065$$). However, as diaries were not used in this study, participants’ sleep time could not be verified and conclusions on this divergence should be drawn with caution. Fourth, we combined accelerometer data from three different studies that applied different sampling rates, i.e. 100 Hz [31, 32] and 30 Hz [33]. This may have caused bias as sampling rate affects the processing of raw acceleration data to activity counts [46]. However, this applies mainly to higher-intensity activities [46] which were less prevalent among our participants. Fifth, we used WC and BMI as indicators of obesity. However, there are other measures that assess body fat distribution as more proximate measures of obesity, such as percent body fat. In our study, obesity-measures other than WC and BMI were not available. Finally, conclusions on the direction of causality cannot be drawn from this cross-sectional study. Longitudinal studies suggest that obesity predicts future amounts of sedentary time whereas associations of the reverse direction remain less evident [27, 47, 48]. The same may apply for associations between obesity and sedentary behaviour patterns. However, evidence on prospective outcomes such as risk for cardiovascular disease [2] and all-cause mortality [22, 23, 26] has accumulated in recent years. For example, in a study among 4,510 U.S. National Health and Nutrition Examination Survey participants investigating accelerometer-based sedentary ≥ 30-minute bouts using latent class analysis it was found that the class with the highest percentage of the day in sedentary bouts had a higher risk of all-cause mortality than the class with the fewest sedentary bouts [26].
Despite these limitations, our finding that sedentary bouts of short, moderate, and long length were differently associated with obesity indicators among 460 individuals deserves further research. If the relationships revealed in this study are found in larger samples, other populations, and within prospective longitudinal studies that allow for inferences on causality, recommendations on sedentary behaviour should explicitly address interruptions of prolonged sitting.
## Conclusion
In a sample of 460 apparently healthy middle-aged to older adults, the daily number of sedentary bouts lasting 1 to 10 min was significantly associated with lower BMI but not with WC and the number of bouts lasting >30 min was significantly associated with higher WC but not with BMI. These relationships persisted independent of time spent in sedentary behaviour, LPA and MVPA. Besides limiting total sedentary time or increasing physical activity, frequent interruptions of sedentary time might improve obesity-related risk factors. Thus, the results of this study to some extent support sedentary behaviour guidelines that promote regular interruptions of sitting.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1 Supplementary Material 2 Supplementary Material 3
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---
title: Type 2 diabetes mellitus and In-hospital Major Adverse Cardiac and Cerebrovascular
Events (MACCEs) and postoperative complications among patients undergoing on-pump
isolated coronary artery bypass surgery in Northeastern Iran
authors:
- Mahin Nomali
- Aryan Ayati
- Amirhossein Tayebi
- Mohammad Eghbal Heidari
- Keyvan Moghaddam
- Soheil Mosallami
- Gholamali Riahinokandeh
- Mahdis Nomali
- Gholamreza Roshandel
journal: BMC Cardiovascular Disorders
year: 2023
pmcid: PMC10007752
doi: 10.1186/s12872-023-03163-5
license: CC BY 4.0
---
# Type 2 diabetes mellitus and In-hospital Major Adverse Cardiac and Cerebrovascular Events (MACCEs) and postoperative complications among patients undergoing on-pump isolated coronary artery bypass surgery in Northeastern Iran
## Abstract
### Background
Diabetes Mellitus (DM) is a rapidly growing disorder worldwide, especially in the Middle East. A higher incidence of coronary artery diseases requiring coronary artery bypass graft (CABG) surgery has been reported in patients with diabetes. We assessed the association between type 2 diabetes mellitus (T2DM) and in-hospital major adverse cardiac and cerebrovascular events (MACCEs) and postoperative complications among patients who underwent on-pump isolated CABG.
### Methods
In this retrospective cohort study, we used the data registered for CABG patients from two heart centers in the Golestan province (North of Iran) between 2007 and 2016. The study population included 1956 patients divided into two groups: 1062 non-diabetic patients and 894 patients with diabetes (fasting plasma glucose ≥126 mg/dl or using antidiabetic medications). The study outcome was in-hospital MACCEs, a composite outcome of myocardial infarction (MI), stroke and cardiovascular death, and postoperative complications, including postoperative arrhythmia, acute atrial fibrillation (AF), major bleeding (defined as reoperation due to bleeding), and acute kidney injury (AKI).
### Results
During the 10-year study period, 1956 adult patients with a mean (SD) age of 59.0 (9.60) years were included. After adjustment for age, gender, ethnicity, obesity, opium consumption, and smoking, diabetes was a predictor of postoperative arrhythmia (AOR 1.30, $95\%$ CI 1.08–1.57; $$P \leq 0.006$$). While it was not a predictor of in-hospital MACCEs (AOR 1.35, $95\%$ CI 0.86, 2.11; $$P \leq 0.188$$), AF (AOR 0.85, $95\%$ CI 0.60–1.19; $$P \leq 0.340$$), major bleeding (AOR 0.80, $95\%$ CI 0.50, 1.30; $$P \leq 0.636$$) or AKI (AOR 1.29, $95\%$ CI 0.42, 3.96; P 0.656) after CABG surgery.
### Conclusion
Findings indicated that diabetes increased the risk of postoperative arrhythmia by $30\%$. However, we found similar in-hospital MACCEs, acute AF, major bleeding, and AKI following CABG surgery in both diabetic and non-diabetic patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-023-03163-5.
## Background
Type 2 Diabetes Mellitus (T2DM) is a multifaceted disorder affecting an increasing portion of the population worldwide [1, 2]. The Middle East, in particular, is one of the primary regions where the number of diabetic patients is rapidly increasing [3, 4]. Diabetic patients are associated with a 4.4-fold increased risk due to cardiovascular disorders [5].
While percutaneous interventions (PCI) have become a widespread treatment for coronary artery disease (CAD), coronary artery bypass graft surgery (CABG) remains the optimal strategy for patients with severe and multivessel CADs. In addition, due to the multivessel nature of CAD in patients with T2DM, various studies have suggested that CABG surgery can be superior to PCI in patients with diabetes and other major cardiovascular risk factors [6–8]. Therefore, CABG surgery is more common among patients with diabetes.
Diabetes can also affect the postoperative outcomes of CABG [9, 10], besides having a greater risk of CAD and CAD-related death [11]. Thus, recent studies have reported worse postoperative outcomes for CABG patients with DM [12–14].
The northeastern region of *Iran is* significant due to its unique ethnic profile. Unlike other areas of the country, a large population of Turkmen ethnicities resides in this region [15]. Several studies of the population of northeastern Iran and Turkmen ethnics have reported a higher prevalence of congenital heart disorders [16], obesity [17], metabolic syndrome [18], cardiovascular diseases [19], hypertension [20], and a high waist circumference [18] compared to other regions of the country. According to the Golestan Cohort Study data, a significantly lower diabetes awareness was reported in this region [21]. The precise reason for these discrepancies is unknown. However, genetic predispositions, higher rural populations, and socioeconomic and lifestyle differences are considered the main contributing factors to this region’s higher prevalence of cardiovascular risk factors [19, 22].
Furthermore, there has been little research on the relationship between diabetes and cardiovascular disorders in this region. As a result, we sought to investigate the effect of T2DM on in-hospital major adverse cardiac and cerebrovascular events (MACCEs) and postoperative complications, such as postoperative arrhythmia, acute AF, major bleeding, and acute kidney injury (AKI) in patients undergoing on-pump isolated CABG in Iran’s northeastern region. Understanding the effects of diabetes on the postoperative outcomes of CABG surgery can be essential for applying better treatment strategies for these patients, considering the unique characteristics of the population in this region.
## Study design
This retrospective cohort study was performed in northeastern Iran (Golestan province, Iran). The study protocol was approved by the institutional review board (IRB) (approval ID 950505.06) and research ethics committee (REC) (ID IR.GOUMS.REC.1395.137) of Golestan University of Medical Sciences in 2016.
## Setting
In this study, we used the data registry of the characteristics of patients who underwent CABG surgery from 2007 to 2016 in Golestan province. Data were collected from two heart centers, including the Kordkuy heart center of Amiralmomenin hospital, affiliated with Golestan University of Medical Sciences (Gorgan, Iran), and the Shafa private heart center, which provides medical services to patients all over the province.
## Participants
Adult patients (> 18 years old) of both sexes who had isolated on-pump CABG procedures between 2007 and 2016 and had complete data on study exposure (status of DM) and covariates were included in the study analysis.
## Variables and measurement
Study variables were demographic and clinical, including past medical history, comorbidities, lipid profile, preoperative medications, clinical characteristics, and operation characteristics. The demographic variables included age, gender, ethnicity, obesity, opium consumption, and smoking status. Opium consumption and smoking status were self-reported data. We considered ethnicity as Turkmen and non-Turkmen ethnicities, the main ethnicities living in Golestan province. In order to define obesity, we calculated the body mass index (BMI) according to the weight and height of each patient. Then, according to the BMI categorization by the center for disease control and prevention (CDC), we considered a BMI of 30 or above as obese [23]. For past medical history, we retrieved the family history of cardiovascular diseases (CVDs) and previous myocardial infarction (MI), which were self-reported data. In addition, various comorbidities were assessed, including hyperlipidemia, hypertension (HTN), chronic obstructive pulmonary disease (COPD), and a lipid profile consisting of low-density lipoprotein (LDL), total cholesterol, and non-high-density lipoprotein (HDL) cholesterol. Non-HDL cholesterol refers to the total cholesterol value minus the HDL cholesterol value. Preoperative medications included Beta-blocker (BB), Statins, and Aspirin. Preoperative clinical characteristics were left ventricular ejection fraction (LVEF) by transthoracic echocardiography (TTE), diagnosis of three-vessel disease, and left main coronary artery (LMCA) stenosis by coronary artery angiography (CAG). Also, operation characteristics included emergency CABG, the left internal mammary artery (LIMA) graft, the number of grafts, cardiopulmonary bypass (CPB) time, and clamp time, which were recorded according to the surgeon’s and perfusionist’s report.
The study outcomes were in-hospital MACCEs (a composite outcome of MI, stroke, and cardiovascular death), postoperative arrhythmia, acute atrial fibrillation (AF), major bleeding, and AKI. Further interpretation and analysis regarding MACCE definitions are presented in the appendix. Myocardial infarction was defined as clinical evidence of acute myocardial ischemia and detection of a rise in cardiac troponin levels with at least one value above the 99th percentile upper limit and at least one of the following:Symptoms of myocardial ischemiaNew ischemic electrocardiogram changesDevelopment of pathological Q waves [24]Stroke was defined as a new focal neurological deficit that lasted more than 24 hours and was confirmed by imaging [25].Postoperative AF and arrhythmias were detected by electrocardiograms (ECGs) read by the attending cardiologists. Major postoperative bleeding was defined as significant bleeding requiring a reoperation [26].AKI was defined as an absolute increase in serum creatinine concentration of 0.3 mg/dL or greater [27, 28].Study exposure was T2DM, which was defined according to fasting plasma glucose (FPG) ≥126 mg/dl or using antidiabetic medications (i.e., Anti-diabetes oral tablets or Insulin).
## Operative Procedure
*After* general anesthesia, a median sternotomy and aortic-right atrial cannulation were performed to achieve a cardiopulmonary bypass [29]. The ascending aorta was occluded, and a cardioplegic solution (Thomas crystalloid cardioplegia solution) was perfused into the heart during CABG [30, 31]. Several infusions of the cardioplegic solution might be administered if an electrical activity or prolonged ischemic time was observed. After completing distal anastomoses, the aortic cross-clamp was removed during myocardial revascularization. After reperfusion, partial occlusion clamps were used to complete the proximal anastomosis. The cross-clamp could still be used after the distal grafts have been conducted to facilitate the proximal grafts [32]. The left internal thoracic artery and greater saphenous vein were the most commonly used bypass grafts.
## Statistical analysis
First, we compared the patients` data across diabetes status. The normality assumption for continuous variables was checked graphically by a histogram plot and the Shapiro-Wilk statistical test. Because of the non-normal distribution of the study variables, they were reported as the median and interquartile range (IQR) and were compared using Mann-Whitney U tests. In order to compare categorical variables, Chi-square tests were used, and the data were reported as numbers and percentages. We used univariate logistic regression analysis to evaluate the association between diabetes and in-hospital outcomes. Then, we used the change-in-estimate (CIE) criterion with a cut-off of more than $10\%$ to detect the probable confounders of this association. Multiple logistic regression analysis was used to adjust for confounders identified by the CIE criterion. Both crude and adjusted ORs were reported for the association between diabetes and in-hospital outcomes with a $95\%$ confidence interval (CI). Data analyses were performed by the statistical package STATA/IC version 14.2 (Stata Corp LP College Station, TX, USA).
## Results
During the 10-year study from 2007 to 2016, demographic and clinical characteristics of 3704 patients who underwent on-pump isolated CABG surgery in the Golestan province were registered. Data related to 1748 patients were excluded due to incomplete data regarding study exposure and baseline data. Thus, the study population included 1956 patients (Fig. 1) with a mean (SD) age of 59.0 (9.60) years. According to the definitions, 1062 patients were non-diabetics, and 894 had diabetes. Fig. 1Study population diagram
## Baseline Characteristics
The patients’ data are demonstrated in Table 1. According to this table, patients with DM were younger, and $44\%$ were female, significantly different from patients without diabetes. Opium consumption and smoking were lower among patients with diabetes. The two groups were similar regarding family history of CVDs, prior MI, and comorbidities. Although there were no differences in lipid profiles between the two groups, LDL, total cholesterol, and non-HDL cholesterol levels were higher than normal values. The frequency of Beta-blocker, Statin and Aspirin consumption, three-vessel disease, and the number of grafts > 3 were higher among patients with diabetes compared to non-diabetics. The proportion of patients with DM with $40\%$ LVEF was higher than in the non-diabetic group ($20\%$ vs. $17\%$), but this difference was not statistically significant ($$P \leq 0.061$$). However, CPB time and clamp time were similar among the two groups (Table 1).Table 1Baseline characteristics of patients with and without diabetes mellitus undergoing isolated on-pump CABG surgeryVariablesWithout DM($$n = 1062$$)With DM($$n = 894$$)P valueAge (year) [Median (IQR)]59 (53–66)58 (53–64)0.040* < 65 years, n (%)748 (70.0)684 (76.5)0.003* ≥ 65 years, n (%)314 (30.0)210 (23.5)Gender, n (%)< 0.001* Male744 (71.0)498 (56.0) Female318 (29.0)396 (44.0)Ethnicity, n (%)0.660 Turkmen73 (7.0)57 (6.0) Non- Turkmen989 (93.0)837 (94.0)Obesity, n (%)0.071 Yes277 (27.0)266 (30.0) No785 (73.0)628 (70.0)Opium consumption, n (%)< 0.001* Ever users347 (33.0)197 (22.0) Never users715 (67.0)697 (78.0)Smoking status, n (%)0.001* Ever users196 (18.5)115 (13.0) Never users866 (81.5)779 (87.0)Past Medical history Family history of CVDs, n (%)0.117 Yes781 (74.0)685 (77.0) No281 (26.0)209 (23.0) Prior MI, n (%)0.811 Yes44 (4.0)39 (4.0) No1018 (96.0)855 (96.0)Comorbidities Hyperlipidemia, n (%)0.310 Yes617 (58.0)499 (56.0) No445 (42.0)395 (44.0) HTN, n (%)0.132 Yes679 (64.0)542 (61.0) No383 (36.0)352 (40.0) COPD, n (%)0.606 Yes39 (4.0)29 (3.0) No1023 (96.0)865 (97.0)Lipid profile LDL (mg/dl) [Median (IQR)]146 (122–180)149 (128–184)0.230 Total cholesterol (mg/dl) [Median (IQR)]215 (185–286)215 (186–280)0.512 Non-HDL (mg/dl) [Median (IQR)]173 (141–244)175 (143–242)0.750Preoperative Medications β-blockers, n (%)< 0.001* Yes466 (44.0)467 (52.0) No596 (56.0)427 (48.0) Statins, n (%)0.001* Yes755 (72.0)693 (78.0) No307 (28.0)201 (22.0) Aspirin, n (%)< 0.001* Yes862 (82.0)782 (88.0) No200 (18.0)112 (12.0)Preoperative clinical characteristics LVEF(%)0.061 ≤ $40\%$180 (17.0)181 (20.0) > $40\%$882 (83.0)713 (80.0) Three vessel disease, n (%)0.020* Yes706 (66.5)638 (71.0) No356 (33.5)256 (29.0) LMCA stenosis, n (%)0.153 Yes85 (8.0)88 (10.0) No977 (92.0)806 (90.0)Operation characteristics Emergency CABG, n (%)0.082 Yes21 (2.0)9 (1.0) No1041 (98.0)885 (99.0) LIMA graft, n (%)0.147 Yes1031 (98.0)877 (98.0) No31 (2.0)17 (2.0) Number of grafts, n (%)0.002* ≤ 3 grafts606 (57.0)448 (50.0) > 3 grafts456 (43.0)446 (50.0) CPB time (min) [Median (IQR)]110 (59–130)110 (57–128)0.279 Clamp time (min) [Median (IQR)]38 (30–48)38 (31–50)0.627DM Diabetes Mellitus, P value Probability value, IQR Interquartile range, CVDs Cardiovascular diseases, MI Myocardial infarction, HTN Hypertension, COPD Chronic obstructive pulmonary disease, AF Atrial fibrillation, LDL Low-density lipoprotein, HDL High-density lipoprotein, LVEF Left ventricular ejection fraction, LMCA Left main coronary artery, LIMA Left internal mammary artery, CPB Cardiopulmonary bypass
## Postoperative Data
In-hospital adverse events occurred in $5\%$ (n/$$n = 44$$/894) of patients with diabetes and $4\%$ (n/$$n = 39$$/1062) of patients without diabetes ($$P \leq 0.172$$). According to the crude analysis, diabetes was not significantly associated with in-hospital MACCEs (Crude OR 1.36, $95\%$CI 0.87, 2.11; $$P \leq 0.174$$). Furthermore, after adjustment for age, gender, ethnicity, obesity, opium consumption, and smoking, diabetes was not a predictor of major adverse events following CABG surgery (adjusted OR 1.35, $95\%$ CI 0.86, 2.11; $$P \leq 0.188$$). ( Table 2).Table 2Comparison of postoperative complications between patients with and without diabetes mellitus undergoing isolated on-pump CABG surgeryIn- hospital outcomesDiabetes GroupCrude OR($95\%$ CI)P_valueAdjusted OR($95\%$ CI)P_valueWithout DM($$n = 1062$$)With DM ($$n = 894$$)MACCEs, n (%)1.36 (0.87, 2.11)0.1741.35 (0.86, 2.11)0.188 ** Yes39 (4.0)44 (5.0) No1023 (96.0)850 (95.0)Post-op arrhythmia, n (%)1.20 (0.99, 1.43)0.0521.30 (1.08, 1.57)0.006*,*** Yes457 (44.0)424 (47.0) No605 (57.0)470 (53.0)Acute AF, n (%)0.80 (0.60, 1.1)0.1580.85 (0.60, 1.19)0.340** Yes92 (9.0)62 (7.0) No970 (91.0)832 (93.0)Major bleeding, n (%)0.75 (0.47, 1.18)0.2160.80 (0.50,1.30)0.636**** Yes50 (5.0)32 (4.0) No1012 (95.0)862 (96.0)Acute kidney injury, n (%)1.39 (0.46, 4.15)0.5561.29 (0.42, 3.96)0.656** Yes6 (1.0)7 (1.0) No1056 (99.0)887 (99.0)OR Odds ratio, CI Confidence Interval, P_value Probability valueMACCEs Major adverse cardiac and cerebrovascular events (i.e., composite of MI, stroke and cardiovascular death), AF Atrial fibrillation, Post-op Postoperative *Statistically significant ($P \leq 0.05$)**Adjusted by age ≥ 65 years, gender, ethnicity, obesity, opium consumption, and smoking status ***Adjusted by age ≥ 65 years, gender, ethnicity, obesity, opium consumption, smoking status, preoperative Aspirin ****Adjusted by age ≥ 65 years, gender, ethnicity, obesity, opium consumption, smoking status, number of grafts, LIMA graft, CPB time, and clamp time The distribution of postoperative complications in patients with and without diabetes is demonstrated in Fig. 2. Despite a higher proportion of in-hospital MACCEs and postoperative arrhythmia in patients with diabetes compared to non-diabetics, these differences were not statistically significant (P 0.174 and P 0.052, respectively).Fig. 2Postoperative complications by diabetes. MACCEs: Major adverse cardiac and cerebrovascular events (i.e., composite of MI, stroke and cardiovascular death). Post-op: Postoperative. AF: Atrial fibrillation. DM: Diabetes. AKI: Acute kidney injury. P: Probability value (i.e. $P \leq 0.05$ was considered as statistically significant) Table 2 compares postoperative complications between patients with and without diabetes mellitus undergoing isolated on-pump CABG surgery and indicates the results of the crude and adjusted analyses. According to this table, patients with diabetes experienced a higher proportion of in-hospital MACCEs compared to those without diabetes ($5\%$ vs. $4\%$, respectively). However, the crude analysis indicated no significant association between diabetes and in-hospital MACCEs (crude OR 1.36, $95\%$ CI 0.87, 2.11; $$P \leq 0.174$$). Furthermore, diabetes was not a predictor of major adverse events following CABG surgery (adjusted OR 1.35, $95\%$ CI 0.86, 2.11; $$P \leq 0.188$$) (Table 2).
Diabetic patients experienced a higher proportion of postoperative arrhythmia than non-diabetic patients ($47\%$ vs. $44\%$, respectively). However, it was not statistically significant (crude OR 1.20, $95\%$ CI 0.99–1.43, $$P \leq 0.052$$). While, after adjustment by age, gender, ethnicity, obesity, opium consumption, and smoking status, we found an association between diabetes and postoperative arrhythmia, as diabetes was associated with a $30\%$ increase in the risk of postoperative arrhythmia (adjusted OR 1.30, $95\%$ CI 1.08–1.57, $$P \leq 0.006$$) (Table 2).
The proportions of acute AF, major bleeding, and AKI were not different between the two groups. Crude analyses indicated no association between diabetes and acute AF (Crude OR 0.80, $95\%$ CI 0.60–1.1; $$P \leq 0.158$$), major bleeding (Crude OR 0.75, $95\%$ CI 0.47–1.18; $$P \leq 0.216$$) or AKI (Crude OR 1.39, $95\%$CI 0.46, 4.15, $$P \leq 0.556$$) after surgery. Adjusted analyses indicated that diabetes was not a predictor of acute AF (adjusted OR 0.85, $95\%$ CI 0.60–1.19, $$P \leq 0.340$$), major bleeding (adjusted OR 0.80, $95\%$ CI 0.50–1.30, $$P \leq 0.636$$) or AKI (adjusted OR 1.29, $95\%$ CI 0.42, 3.96, $$P \leq 0.656$$) after CABG surgery, as well (Table 2). The results of the adjusted analyses for study outcomes are demonstrated in Fig. 3.Fig. 3Adjusted odds ratios for study outcomes. MACCEs: Major adverse cardiac and cerebrovascular events (i.e., composite of MI, stroke and cardiovascular death). Post-op: Postoperative. AF: Atrial fibrillation
## Discussion
In this retrospective cohort study of patients undergoing CABG surgery, patients with T2DM were significantly younger and had a higher percentage of women than other patients. Diabetic patients were treated with more preoperative medications, including Beta-blockers, Statins, and Aspirin. Furthermore, being burdened with DM was significantly associated with a higher chance of developing three-vessel disease and receiving more than three grafts during the surgery. However, even after adjustment for other known risk factors, DM was not a predictor of in-hospital major adverse events in these patients. On the other hand, the adjusted analysis indicated that DM was a predictor of postoperative arrhythmias and increased the risk by $30\%$.
In addition to being a major risk factor for CAD, diabetes can affect the treatment strategy for CAD [33]. Accordingly, 20 to $30\%$ of patients undergoing CABG are burdened with diabetes [34–36].
In our study, diabetic patients undergoing CABG surgery were significantly younger than non-diabetic patients ($76.5\%$ under 65 years old vs. $70\%$, $$P \leq 0.003$$). These results were consistent with the Wang et al. study [37], in which the mean age of diabetic patients treated with Insulin who underwent CABG was significantly lower than non-diabetic patients [mean (SD): 62.7 (9.3) vs. 64.2 (8.4), $P \leq 0.001$]. In this regard, Stamou et al. [ 38] revealed that DM was more common among CABG patients aged 60–69 years compared to patients older than 80 ($28\%$ vs. $15\%$). In a cohort study conducted in the USA and Canada, Carson et al. [ 34] showed that diabetic patients treated with Insulin undergoing CABG are younger than non-diabetic patients [mean (SD): 63.8 (10.2) vs. 65.1(1.09)]; Also, consistent with our study, they revealed that male gender predominancy was imperceptible among DM patients ($55.4\%$ vs. $74.0\%$ for non-diabetics).
Three-vessel disease and LMCA stenosis are indications for CABG surgery [39]. In a 2015 study, Jia et al. [ 40] utilized CT coronary angiography to compare CAD in patients with and without DM. They reported a significant association between DM and the number of diseased segments [OR ($95\%$CI): 2.14 (1.09–2.6)] and a higher rate of multivessel disease among diabetic patients ($15\%$ vs. $7\%$, $P \leq 0.001$). Our study showed that three-vessel disease was significantly more common among diabetic patients ($71.0\%$ vs. 66.5, P: 0.020), but no significant difference was observed regarding the left main coronary artery (LMCA) stenosis ($$P \leq 0.153$$). This was consistent with the Yamaguchi et al. [ 41] study, in which three-vessel disease was significantly more prevalent among diabetic CABG candidates ($75.1\%$ vs. $68.5\%$, $P \leq 0.001$). Furthermore, in their study, the proportion of LMCA stenosis was not significantly different among the two groups. In 2020, wang et al. [ 37] conducted a study on 4325 CABG patients and reported that both three-vessel disease and LMCA stenosis were significantly more common among Insulin treated diabetic patients compared to non-diabetic patients. In this regard, no difference was detected among non-diabetic and oral-treated diabetic patients.
In Yamaguchi et al. study [41], there was no significant difference between diabetic and non-diabetic patients regarding post-CABG in-hospital death or stroke; Our study supports their findings, suggesting that DM is not significantly associated with in-hospital adverse events. Further to previous studies, DM was not a predictor of in-hospital MACCE following CABG surgery after adjustment by other risk factors. Furthermore, in a Chinese cohort study by Zhang et al. [ 42], DM was not associated with in-hospital post-CABG death, cerebrovascular accident (CVA), or MI. However, it was a predictor of long-term death or CVA. Their study assessed the resources and surgery costs in both groups, revealing that DM patients had higher costs for both initial hospitalization and follow-up.
Additionally, we analyzed the distribution of other postoperative complications in the two groups. After adjustment for potential confounders, our analysis showed that DM is a predictor of postoperative arrhythmias, increasing its risk by $30\%$. However, we detected no association between diabetes and postoperative AF. Diabetic patients are generally associated with a higher rate of arrhythmia [43–45]. However, studies have contradictory reports regarding diabetes and postoperative arrhythmias; *In a* retrospective cohort study on the assessment of postoperative Atrial Fibrillation (POAF) in CABG patients, Ismail et al. reported diabetes as an independent factor for POAF in these patients [46]. Mangi et al. also reported significantly higher rates of diabetes in post-CABG patients with POAF compared to patients without postoperative arrhythmia [47]. Although, in a meta-analysis on post-CABG arrhythmia by Woldendorp et al., diabetes was not significantly higher in patients with POAF compared to patients with a normal postoperative sinus rhythm [48].
One of the most serious postoperative complications in our patients was post-CABG major bleeding. The adjusted analysis indicated that diabetes was not associated with major postoperative bleeding in CABG patients. In a multicenter European study, Biancari et al. assessed the risk factors of post-CABG bleeding. Similar to our results, they indicated that diabetes was not associated with an increased risk of post-CABG bleeding [49]. Moreover, in the results of a large-scale study on post-CABG bleeding by Hansson et al., diabetes was also not associated with post-CABG bleeding [26].
Furthermore, our study failed to detect an association between diabetes and postoperative AKI. Previous studies have reported contradictive results. While various studies have indicated diabetes as a risk factor for postoperative AKI [50–52], several other studies did not suggest diabetes as a significant risk factor [53–55]. Inconsistencies in the definition of AKI might be a main contributing factor to the variations observed in the study results [56].
Due to its high prevalence and increasing incidence rate, diabetes is a significant public health issue in Iran [20, 57, 58]. Additionally, compared to previous years, the age-standardized mortality rate of DM had a notable rise in 2015 ($11.3\%$ in 2015 vs. $8.7\%$ in 2000) [59]. In a 2011 study, Javanbakht et al. [ 60] reported 842.6 ± 102 USD for the average medical cost of diabetes per capita, and 412.8 ± 64.5 USD were complication costs. They reported cardiovascular disease expenditure as the most significant complication cost component ($42.3\%$). Thus, conducting extensive studies on the issue in different regions is necessary to develop effective management strategies for cardiovascular disease in diabetic patients.
## Limitations
This large-scale study was conducted in Northeastern Iran. The current study was the first from this region, including ethnic diversities, especially Turkmen ethnicity. However, our study faced some limitations. Several variables, such as duration of diabetes, HbA1C level, and type of antidiabetic medications (oral pills or Insulin), were unavailable in the dataset we used to consider in the analysis, which may affect the study results. In this study, the in-hospital outcome was considered, and the long-term outcomes were not evaluated.
## Conclusion
To the best of our knowledge, this is the first study from Northeastern Iran focusing on the effect of DM on the in-hospital MACCEs and postoperative complications after CABG surgery. Severe and multivessel coronary artery disease was more common among diabetic CABG patients compared to other CABG patients. This difference could explain the higher number of grafts used in these patients during the operation. Despite the differences, the risk of in-hospital MACCEs, acute AF, major bleeding, and AKI was not statistically significant between diabetic and non-diabetic patients following a CABG surgery. While, diabetes was associated with a $30\%$ increase in the risk of postoperative arrhythmia, which requires further attention.
## Supplementary Information
Additional file 1: Appendix. Table S1. Comparison of In-hospital MACCEs (+Acute kidney injury) between patients with and without diabetes mellitus undergoing isolated on-pump CABG surgery.
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|
---
title: 'Clinical guidelines for the management of weight during pregnancy: a qualitative
evidence synthesis of practice recommendations across NHS Trusts in England'
authors:
- Lucy Goddard
- Nerys M. Astbury
- Richard J. McManus
- Katherine Tucker
- Jennifer MacLellan
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10007759
doi: 10.1186/s12884-023-05343-9
license: CC BY 4.0
---
# Clinical guidelines for the management of weight during pregnancy: a qualitative evidence synthesis of practice recommendations across NHS Trusts in England
## Abstract
### Background
Women who enter pregnancy with a Body Mass Index above 30 kg/m2 face an increased risk of complications during pregnancy and birth. National and local practice recommendations in the UK exist to guide healthcare professionals in supporting women to manage their weight. Despite this, women report inconsistent and confusing advice and healthcare professionals report a lack of confidence and skill in providing evidence-based guidance. A qualitative evidence synthesis was conducted to examine how local clinical guidelines interpret national recommendations to deliver weight management care to people who are pregnant or in the postnatal period.
### Methods
A qualitative evidence synthesis of local NHS clinical practice guidelines in England was conducted. National Institute for Health and Care Excellence and Royal College of Obstetricians and Gynaecologists guidelines for weight management during pregnancy constructed the framework used for thematic synthesis. Data was interpreted within the embedded discourse of risk and the synthesis was informed by the Birth Territory Theory of Fahy and Parrat.
### Results
A representative sample of twenty-eight NHS Trusts provided guidelines that included weight management care recommendations. Local recommendations were largely reflective of national guidance. Consistent recommendations included obtaining a weight at booking and informing women of the risks associated with being obese during pregnancy. There was variation in the adoption of routine weighing practices and referral pathways were ambiguous. Three interpretive themes were constructed, exposing a disconnect between the risk dominated discourse evident in the local guidelines and the individualised, partnership approach emphasised in national level maternity policy.
### Conclusions
Local NHS weight management guidelines are rooted in a medical model rather than the model advocated in national maternity policy that promotes a partnership approach to care. This synthesis exposes the challenges faced by healthcare professionals and the experiences of pregnant women who are in receipt of weight management care. Future research should target the tools utilised by maternity care providers to achieve weight management care that harnesses a partnership approach empowering pregnant and postnatal people in their journey through motherhood.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-023-05343-9.
## Background
Less than half of women in the UK enter pregnancy with a Body Mass Index (BMI) below 30 kg/m2 [1]. Risks associated with a raised BMI include gestational diabetes, pre-eclampsia, postpartum haemorrhage, prolonged labour, birth defects and fetal macrosomia [2, 3]. Although well documented within the literature, a lack of awareness of the risks of obesity in pregnancy and challenges surrounding losing weight preclude many women from weight loss attempts prior to conception [4, 5].
Maternity health care professionals indicate a lack of confidence, knowledge and skill as significant barriers to delivering effective and supportive weight management care [6]. As a result, women report inconsistent advice, confusion over the information they do receive and a lack of support from healthcare professionals despite wanting weight management advice [7–10]. This leads women to seek information from other sources, and can induce anxiety around weight gain [9, 11].
The UK National Institute for Health and Care Excellence (NICE) and Royal College of Obstetricians and Gynaecologists (RCOG) have produced guidelines for weight management before, during and after pregnancy [12, 13]. They provide recommendations for maternity practitioners in how to deliver safe care for women classified as obese (BMI ≥ 30 kg/m2) at the start of pregnancy. Based on the best available research evidence and expert consensus, these national guidelines are interpreted by NHS Trusts in England to fit the local context, population, resources and infrastructure. They turn best practice recommendations into practical actions at the interface of maternity care delivery, supporting practitioners to support women and birthing people in optimum weight management.
The aim of this review was to analyse how local NHS clinical guidelines for weight management during pregnancy and the postnatal period interpret national guidance, synthesising findings to extend interpretation of observed variations between and within national and local guidelines.
## Design
We performed a qualitative evidence synthesis of local NHS clinical practice guidelines using the framework analysis approach of Gale et al. [ 2013] and synthesis method of Thomas and Harden [2008] [14, 15]. Analytical rigour and transparency were ensured through careful documentation of the synthesis process described here.
## Search method
We approached a purposive sample of NHS Trusts across England. A sample of at least 28 Trusts was selected pragmatically by the principle researcher (LG) aiming to ensure representation of Trusts from the seven NHS regions in order to provide adequate variation of the pre-specified attributes [16]. These attributes were considered to potentially influence the results and included rural/ urban setting, catchment population size, number of births per year and university/ teaching status. Guidelines were downloaded from their website or an application was made through a Freedom of Information process (email or online form) between 1st April and 1st June 2021. In the case of non-responders, a Trust within the same region of similar size and university status was contacted. Upon receipt of each guideline, the associated Trust was assigned a unique sequential code to ensure anonymity.
## Quality appraisal
The purpose of this review was not to judge the quality of the existing guidelines but rather sought to present what is currently recommended and examine the content of the guideline. Therefore, no quality appraisal was performed.
## Data abstraction
Narrative sections of the localised guidelines were extracted and entered verbatim into NVivo for coding (QSR International Pty Ltd. [2020] NVivo (released in March 2020)).
## Synthesis
Narrative data were coded into a deductive framework of 18 pre-defined categories, constructed from the recommendations of the NICE Weight management in pregnancy guideline and the RCOG Care of Women with Obesity in Pregnancy guideline by the principle researcher (LG) [12, 13] (Table 1). Descriptive memos for each category supported consistency in coding. Each pre-defined category, and the coded text within it, was extracted and imported into Excel (Microsoft Excel 2016) to generate a matrix, enhancing visualisation for data comparison and interpretation. Cells were tagged with direct quotes or important interpretations, in order to capture nuances and ambiguity between guidelines. This led to collapse and refinement of categories into broad descriptive themes. Reflexivity was practiced throughout the data synthesis, supported by discussions with the full research team. Interpretations were informed by risk discourse and the Birth territory Theory, to synthesise the descriptive themes into three analytical themes that structure the presentation of our synthesis findings [17, 18].Table 1Framework based on pre-specific categories constructed from NICE and RCOG guidelinesNICE/ RCOG recommendation (category)Description and instructionAddress any concerns about physical activity in pregnancyNICE: "Advise that moderate-intensity physical activity will not harm her or her unborn child. "This includes reassuring women about the safety of physical activity in pregnancyAddressing a woman's concernsNICE: Be "…sensitive to any concerns she may have about her weight. "Any discussion on the sensitive manner in which conversations about weight should be had and the importance of communication between the woman and healthcare professionalsAdvice on benefits of healthy diet and physical activityNICE: "Advise that a healthy diet and being physically active will benefit both the woman and her unborn child during pregnancy and will also help her to achieve a healthy weight after giving birth. "Discussions with the healthcare professional around healthy eating and being physically active that is not specific. Code as ‘Specific dietary or physical activity advice’ if specific advice is givenBehaviour change advice“Evidence-based behaviour change advice includes: understanding the short, medium and longer-term consequences of women's health-related behaviour helping women to feel positive about the benefits of health-enhancing behaviours and changing their behaviours recognising how women's social contexts and relationships may affect their behaviour helping plan women's changes in terms of easy steps over time identifying and planning situations that might undermine the changes women are trying to make"Only code this if it does not fit into any other code. For example, if they are provided with individualised advice, code only as ‘Practical and tailored information’. This is because this code describes the way in which advice (within the other codes) may be providedBreastfeedingNICE: "Midwives and other health professionals should encourage women to breastfeed. They should reassure them that a healthy diet and regular, moderate-intensity physical activity and gradual weight loss will not adversely affect the ability to breastfeed or the quantity or quality of breast milk. "RCOG: "*Obesity is* associated with low breastfeeding initiation and maintenance rates. Women with a booking BMI 30 kg/m2 or greater should receive appropriate specialist advice and support. "Dieting or weight loss in pregnancyNICE: "Dieting during pregnancy is not recommended as it may harm the health of the unborn child. "RCOG: "Anti-obesity or weight loss drugs are not recommended for use in pregnancy. "Any recommendation that refers to dietary programmes/ diets that promote weight lossDiscuss her current eating habits and physical activity levelsNICE: "At the earliest opportunity, for example, during a pregnant woman's first visit to a health professional, discuss her eating habits and how physically active she is. Find out if she has any concerns about diet and the amount of physical activity she does and try to address them. "Dispelling mythsNICE: "Dispel any myths about what and how much to eat during pregnancy. For example, advise that there is no need to 'eat for two' or to drink full-fat milk. Explain that energy needs do not change in the first 6 months of pregnancy and increase only slightly in the last 3 months (and then only by around 200 cal per day)."Explaining risksNICE: "Explain to women with a BMI of 30 or more at the booking appointment how this poses a risk, both to their health and the health of the unborn child. Explain that they should not try to reduce this risk by dieting while pregnant and that the risk will be managed by the health professionals caring for them during their pregnancy. "Do not code if risks are only stated at the beginning of the guideline (as part of the introduction). It should be clear whether risks are discussed with the womanHeight and weight at the first contactNICE: "Measure weight and height at the first contact with the pregnant woman" "Weight, height and BMI should be recorded in notes, the woman's hand-held record and the patient information system"Not within NICE or RCOG recommendationsAnything that is related to weight management that is not referred to by NICE or RCOG guidelinesPractical and tailored informationNICE: "Offer practical and tailored information. This includes advice on how to use Healthy Start vouchers to increase the fruit and vegetable intake of those eligible for the Healthy Start scheme (women under 18 years and those who are receiving benefit payments)."Any guideline that indicates providing personalised or tailored advice based on the individual woman. If this is coded, check for code “Discuss her current eating habits and physical activity levels”If women are referred to a dietician or other programme it is likely they will receive personalised advice however for the purpose of examining the guideline only and therefore, first line of care, do not code if not explicitly described that advice is personalised or tailoredRecommended weight gain rangesNICE: "Many pregnant women ask health professionals for advice on what constitutes appropriate weight gain during pregnancy. However, there are no evidence-based UK guidelines on recommended weight-gain ranges during pregnancy. ”RCOG: "*There is* a lack of consensus on optimal gestational weight gain. Until further evidence is available, a focus on a healthy diet may be more applicable than prescribed weight gain targets. "Code if weight gain thresholds are given (and which ones, i.e. IOM) even if the lack of evidence on weight gain ranges is acknowledgedReferralsNICE "Offer women with a BMI of 30 or more at the booking appointment a referral to a dietitian or appropriately trained health professional for assessment and personalised advice on healthy eating and how to be physically active. "Does not include obstetric or anaesthetic referrals for risk assessment or birth planning. Only referrals relating to weight managementCode even if guideline is not clear about who the referral is for or who/ where they are being referredCode if weight management programmes are highlighted, for example, Slimming World, even if there isn’t a formal referral process describedSignposting to reputable sources of informationNICE "Reputable sources of information and advice about diet and physical activity for women before, during and after pregnancy include: The Department of Health's 'The pregnancy book' and 'Birth to five' and the NHS eat well website." " Advise her to seek information and advice on diet and activity from a reputable source"Code if women are provided with a Trust leaflet/ signposted to Trust website to access content on weight management during pregnancy but do not code if these resources only detail obstetric or anaesthetic risksSpecific dietary or physical activity adviceNICE: Specific dietary advice, e.g. eat fibre rich foods, eat at least 5 portions of fruit and vegetables each day, avoid increasing calorie intake. Specific physical activity advice e.g. 150 min of exercise per weekWeighing womenNICE: "Do not weigh women repeatedly during pregnancy as a matter of routine. Only weigh again if clinical management can be influenced or if nutrition is a concern"RCOG: “For women with obesity in pregnancy, consideration should be given to reweighing women during the third trimester to allow appropriate plans to be made for equipment and personnel required during labour and birth”Code if women are weighed throughout pregnancy with information on recommended gestationsCode if recommendation is risk assessment weight in the third trimester as this is a weight that would influence clinical management. However, it is important to note, this is not for weight management purposes. For example, it is not for the purpose of starting a conversation with the woman about her diet and physical activity levelsWeight management postpartumNICE: "Encourage them to lose weight after pregnancy. "RCOG: "Women should be supported to lose weight postpartum and offered referral to weight management services where these are available. "Code both those that recommend providing healthy lifestyle advice and those that signpost women to other healthcare professionals/ weight management services
## Theoretical framework
Identification of a theoretical underpinning is vital in midwifery research to tackle and explore complex phenomena, such as weight management, as it ensures findings consider different perspectives; essential to improve care practices and thus, women’s experiences [19]. Birth Territory Theory (BTT), chosen as the theoretical framework here, concerns the balance of power in the birth room and how maternity professionals can support and work in partnership with women [17, 18]. ‘ Midwifery guardianship’, the theories key concept, exemplifies integrative power described as when the power between all present during care is equal and the shared, desired outcome is for the woman to feel confident and empowered. In contrast, disintegrative power disables the woman’s confidence in her abilities to achieve a positive pregnancy and birth experience and can induce the woman to become passive in her own care. Disintegrative power, enacted through medical surveillance, draws closely on the concept of risk embedded in maternity services [20, 21].
The Birth Territory theory and the concepts within it, reflect the ambition of UK maternity policy to listen to, understand and work in partnership with each individual woman to ensure a positive pregnancy and birth experience whilst minimising poor outcomes. Thus, the BTT was appropriate to provide an in-depth interpretation of the synthesis findings in the context of current midwifery practice.
## Results
A total of 29 hospital NHS Trusts were contacted and 28 guidelines received (Fig. 1, Additional file 1). Nine were classified as rural or remote, and 14 as University or teaching hospitals, with five classified as both rural or remote with University or teaching status. The number of hospitals within individual Trusts ranged from one to eight, with number of births for the year 2019–2020 ranging from 955 to 14,270 (Table 2) [22–24]. All the guidelines received classified women with obesity as having a BMI ≥ 30 kg/m2, with the exception of one guideline (Guideline C) classifying women as obese with a BMI ≥ 35 kg/m2, for the purpose of the guideline. Table 2Characteristics of NHS Trusts in England who provided guidelinesCode allocated to NHS TrustNumber of births per yearaRural or remotebTeaching/ University statusNo. main hospital sites within TrustName of guidelineB955RemoteNo1Standard Operational Procedure for the Management of Pregnancy Women whose Body Mass *Index is* ≥ 30 kg/m2H1295RemoteNo1Obesity in Pregnancy guidelineY1650RemoteNo1Guideline for the Management of obesity during pregnancyAA1925NoNo1Obesity in pregnancy birth and postnatal periodC1960RuralNo1Clinical guideline for the management of Obesity in PregnancyL3775NoYes1Obesity in pregnancy, labour and puerperiumX5840NoNo1The management of obesity in pregnancyK2580NoYes2Care of Pregnant Women with a Raised BMI ≥ 30 kg/m2Q3440NoYes2BMI Management in PregnancyE4065NoNo2Raised Body Mass Index in PregnancyO4270RuralYes2Obesity: Management of Pregnant Women with a Raised BMI at Initial ConsultationBB5125RuralYes2Assessing and managing extremes of BMI in pregnancyF1495NoNo3Obesity in Maternity Care GuidelineA2825RemoteYes3Obesity in PregnancyG3805NoNo3Management of Obesity in Pregnancy GuidelineS4010RuralYes3Increased Body Mass Index (BMI) in Pregnancy, Labour and Post Delivery Clinical GuidelineJ4955NoNo3Management of obese women in pregnancyU5585NoNo3Obese Pregnant WomenW5910NoNo3Management of Women with Obesity in PregnancyM6310NoNo3Obesity in pregnancyN9030NoNo3Care of women with obesity in pregnancyR5230NoYes4BMI: Optimal weight for pregnancy and childbirth. Guidelines for women with BMI > 30 kg/m2Z14,270NoYes4Obesity in PregnancyT8960NoYes5Obesity in MaternityI6007NoYes6Raised BMI during pregnancyD48204540Figures from two merged TrustsNoYes7Obesity in PregnancyP4420RemoteYes8Obesity guidelinesV7640NoYes1Management of a pregnancy for women with a BMI > 30 kg/m2Total = 28N/a9 rural/ remote14 University/ teaching hospitalsN/aN/aaSOURCE: NHS Digital $\frac{2019}{20}$bRural is classed as the top $10\%$ of Trusts with the highest proportion of patients living in rural areas in $\frac{2018}{19}$, calculated from Hospital Episode Statistics. Remote is classed as those with 'unavoidable small hospitals' (SOURCE: Nuffield Trust) *The synthesis* of local clinical guideline interpretations of national guidance to deliver weight management care to people who are pregnant or in the postnatal period identified three core themes:Recommendations at odds with a partnership approach to careAdvocating surveillance – to what end?Discretionary and ambiguous pathways
## Recommendations at odds with a partnership approach to care
Healthcare professionals are advised in national guidance to discuss weight in a sensitive manner and allow opportunities for a woman to raise the concerns she may have about her lifestyle and the ways to address these [12, 13]. The local guidelines reflected this in varying degrees in their weight management recommendations. Ten emphasised the importance of sensitive and respectful conversations around weight where the language used was an important contributor to achieve a good relationship with the woman that laid the foundation for an equalised partnership (Guideline H, I, L, M, S, X, Y, Z, AA, BB). Seven guidelines further emphasised the importance of this to empower women (Guideline S, Z, AA, BB, H, M), to encourage active engagement in care (Guideline AA, BB, H, M, Z), and to promote a positive pregnancy and birth experience (Guidelines L, H). Three guidelines highlighted the sensitive nature of conversations around weight stigma and the lower self-esteem that may be experienced by women of a higher weight (Guideline L, M and I). An awareness of weight stigma and lower self-esteem was not addressed in twenty-five guidelines. A clinical approach was advocated in five guidelines where additional mental health screening was recommended (Guideline J, N, P, X, Z). This reflects RCOG recommendations that as women with a BMI above 30 kg/m2 are at an increased risk of mental health problems, they should be screened for these during pregnancy, without necessarily exploring the source of the mental distress [13].
Twenty-seven of the 28 guidelines recommended healthcare professionals discuss the risks associated with obesity during pregnancy with some recommending to explain “the ways in which they [the risks] may be minimised” or management strategies to reduce the risks (Guidelines A, C, D, E, F, L, Q, U, Z). Guidelines mostly provided a list of possible risks for healthcare professionals to discuss, with one guideline (Guideline N) advising a sticky label, listing the risks, be placed in the women’s hand-held notes. $\frac{5}{28}$ guidelines provided risk calculations alongside at least one complication (Guidelines M, O, V, X, Y). No guideline offered a risk assessment framework to help healthcare professionals frame risk, or guidance on how risks may be minimised. Guideline H recommended that women should be advised of the risks but be reassured that most women with a raised BMI have straightforward pregnancies and healthy babies. This is unlike the general tone around risk observed in the majority of guidelines but paid attention to how risk information is translated by a woman who may already be feeling vulnerable. Conversely, it could cause confusion in why risk is discussed if most women experience no complications or alternatively, provide false reassurance regarding their potential for poorer outcomes [25].
Advocating a healthy lifestyle and the provision of practical advice on how to improve dietary intake and activity levels are recommended in national guidelines [12, 13]. Informing women of the benefits of adopting health behaviours during pregnancy was recommended in most local guidelines ($\frac{26}{28}$) but were limited in how they could work with women to achieve this. Seven guidelines offered brief recommendations on what a diet should consist of and recommendations for physical activity but overall information on how to translate this into the reality of women’s lives was not evident (Guideline F, J, K, M, X, Y, Z).
According to national guidance, healthcare professionals should tailor weight management advice to the individual needs of birthing people, enlisting behaviour change knowledge and techniques to provide an individualised approach to care [12]. At a local level, three guidelines mirrored this more collaborative approach (Guideline E, F, G). They recommended healthcare professionals to initiate questions about the woman’s own personal lifestyle, offering a woman an opportunity to frame their experience in a non-judgemental space; exemplifying midwifery guardianship. Three guidelines recommended addressing women’s concerns around physical activity (Guideline F, I, M) or weight (Guideline F, M) but no guidelines made recommendations to address women’s concerns specifically about her diet. Understanding the woman and providing an opportunity to listen to her concerns is central to ensuring equality in care. Guideline G described behavioural techniques such as goal setting to support women to makes changes and expressed that “every contact with women during pregnancy…is an opportunity for tailored health promotion”. Practical steps to identify barriers to behaviour change with women and make plans to overcome them were not clear in any guideline.
Twenty-two guidelines advised midwives to signpost women to reputable sources about healthy lifestyles during pregnancy. As stated in guidance, all women should be provided with accurate and accessible information but only one guideline explicitly identified leaflets that were provided in different languages (Guideline S). It was not evident in other guidelines that there was leaflet provision for those whose first language was not English. If supported with good communication, providing leaflets can be useful in providing women with information and tools to engage in healthy behaviours during pregnancy. However, if it is not reinforced with a conversation and incorporated into the woman’s individual context, it risks being discarded [26].
## Advocating surveillance – to what end?
National guidance recommends that an accurate height and weight should be obtained at the first antenatal contact and a further weight in the third trimester should be considered to plan for appropriate equipment and personnel for birth [12, 13]. Additional weight measurements should only be taken when “clinical management can be influenced or if nutrition is of concern”. All 28 guidelines recommended a baseline BMI to be calculated at the booking ($\frac{27}{28}$) or first scan ($\frac{1}{28}$) appointment. An initial weight measurement is part of the risk assessment to determine the appropriate clinical pathway for the woman and her pregnancy as women with a higher BMI have poorer outcomes and therefore should have increased surveillance [13]. The majority of guidelines ($\frac{24}{28}$) recommended further weight measurements with 13 aligning with national guidance to re-weigh for risk assessment purposes or if nutrition was a concern (Guidelines E, F, G, J, M, N, O, Q, V, W, Z, AA, BB). Eleven recommended healthcare professionals to monitor weight throughout pregnancy (Guideline K, R, H, I, L, P, S, T, U, X, Y). The purpose of re-weighing was clear in only two guidelines to support weight management (Guideline K and R). Women need clear and consistent messaging to feel encouraged and supported. Re-weighing with no given rationale can take away a woman’s sense of control especially as the guidance on how to act on a weight measurement was not clear in any guideline. Monitoring weight throughout pregnancy strongly speaks to the idea that pregnancy is an opportunity for increased surveillance to bring the body back to the “norm” and to identify when further surveillance or intervention is needed in order to guarantee safety as defined by the medical system. Weighing women for the purpose of clinical management, such as for drug calculations, is an example of this and has an important role in ensuring the woman receives appropriate and safe care. However, weighing women with no rationale or no subsequent guidance could be interpreted as controlling the pregnancy but to no clear advantage to the woman or the healthcare professional. For some women, weighing can induce feelings of shame and guilt and this needs considering within the recommendation when there is no clear pathway following a measurement.
National guidance does not provide weight gain thresholds for pregnancy because it concludes that there is no evidence or a lack of consensus on optimal gestational weight gain [12, 13]. Whilst 15 guidelines acknowledged this, eight guidelines recommended weight gain thresholds (Guideline I, M, Q, AA, J, R, X, Y). These were based either on the Institute of Medicine guidelines or on recommendations of which the source was unclear [27]. Current maternity care in the UK sits within a discourse of risk that perhaps explains the advocacy or attempts to adopt weight gain thresholds but we do not have the evidence in which to determine what a healthy gain looks like. This non-conclusive evidence results in recommendations lacking information about what to do should a woman fall above or below thresholds. This challenges the healthcare professional’s ability to support the woman effectively, thus, putting at risk the trusting relationship.
## Discretionary and ambiguous pathways
National guidance recommends a referral should be offered to women with a BMI of 30 kg/m2 or above at booking and then again in the postnatal period [12, 13]. A dietician or appropriately trained healthcare professional can then perform an assessment and provide personalised advice on a healthy lifestyle. Referrals for additional weight management support were suggested ($\frac{21}{28}$) but clear eligibility criteria and how to initiate the referral were frequently missing or unclear. Table 3 displays a breakdown of the suggested places for referral. Referrals were sometimes at the healthcare professional’s discretion to consider whether it was necessary and was dependent on resources or availability of services in the area. Referral pathways could have an important role in offering women an opportunity for more specialised or individualised care regarding her weight that may not be possible in routine appointments. Only Guideline R had a clear referral process where attendance at a specific workshop was requested via the midwife as early as possible. An additional referral was offered to women with a BMI above 25 to funded weight management services that included either one-to-one telephone support with a specialist midwife or support from a dietician via an App, depending on the area. Table 3Types of referrals suggested in the guidelinesReferral typeNumber of TrustsNHS Trust CodeDietician/ dietetic service15B, E, F, H, I, K, L, N, Q, R, X, Y, Z, AA, BBWeight management programme (unspecified)3G, A, AAWeight management programme offered within the Trusta8D, F, K, L, M, O, Q, R,Weight management programme offered in the communityb5K, O, P, R, UaSpecified antenatal classes, workshops, specialist midwife follow-up or similarbCommunity programmes including Slimming World ($$n = 3$$) or Weight Watchers ($$n = 1$$) National guidance recommends that healthy lifestyle messages should be reinforced in the postnatal period and women should be advised to seek further support to manage weight or dietetic advice [12, 13]. This was recommended in 23 guidelines. Pregnancy is a life event for the woman and her family that continues beyond the remit of maternity service provision. The transition in services after birth is a tentative period as intervention, advice and information is often suspended or orientated toward the baby meaning women can feel a sudden loss of support and advice. Guideline I recommended that healthcare professionals write to the GP to consider measures to help with weight reduction. This transfer and sharing of information with the woman’s GP could provide a useful support link when women no longer fall within the scope of maternity services. Clear referral pathways after birth are essential in upholding the important health messaging established during pregnancy that are fundamental in determining the potential course of subsequent pregnancies. Breastfeeding was advocated, specifically in relation to supporting weight management, in four guidelines. ( Guideline A, D, G, R).
## Key findings
While local recommendations were largely reflective of national guidance, our synthesis has exposed a disconnect between the partnership approach emphasised in national maternity policy, and the risk dominated discourse of local practice recommendations for weight management during pregnancy. The most consistent recommendations were to obtain a weight measurement at booking, inform women of the benefits of a healthy lifestyle and of the risks associated with being obese during pregnancy. Specific, practical advice about how to achieve a healthy lifestyle was lacking, there was variation in the adoption of routine weighing practices and referral pathways were ambiguous.
The Birth Territory Theory challenges the focus on surveillance and risk observed in the majority of guideline recommendations. In contrast but in alignment with the BTT, few guidelines promoted or facilitated an individualised or partnership approach; a characteristic of midwifery guardianship that displays an awareness of the sensitive and emotive nature of weight management conversations.
## Existing literature
Healthcare professionals face significant personal and contextual barriers to having conversations about weight during pregnancy [28–30]. Barriers include a lack of knowledge, skill, confidence and a fear of stigmatising women who enter pregnancy with obesity. The focus on risk, absence of clear instruction on providing behaviour change advice, unclear pathways and omission of practical lifestyle advice on how to work with women to manage weight that was observed in the local guidelines offers explanation to why such barriers may exist. Weight management advice is often sought by women but insensitive counselling around weight and in particular, risk, and the medicalisation of these pregnancies can result in depersonalisation of care and feelings of shame and guilt [6, 8, 31]. Creating a non-judgemental space, where a woman feels safe to discuss her concerns with the healthcare professional is fundamental within conversations about weight that do not induce weight stigma. However, local guidelines appear to fail healthcare professionals in advocating midwifery guardianship.
Local clinical guidelines derive from a medical perspective in which healthcare professionals dictate the course of clinical management, intervention and treatment based on the best available evidence. However, weight management is complex and incorporates biological, psychological, environmental and social factors, and the guidelines examined often overlooked these factors and were orientated around risk and surveillance. Focus on risk disempowers women and elevates anxiety [26]. During risk conversations, women can feel judged for their weight that can affect her decisions during pregnancy and if complications arise, made to feel they are to blame [25]. Pregnant women understand risk in a context specific way dependent on their previous experiences and socio-cultural factors emphasising the importance of individual assessment and framing of risk around the woman’s cultural and social context to support her sense making [32]. Women want constructive advice to follow new information about her risk and the omission of this detail creates challenges for healthcare professionals to ameliorate the anxieties created by risk counselling [33]. Clinical guidelines can support the complex interplay between scientific evidence, healthcare provider’s expertise and the woman’s preferences by considering how each recommendation and the evaluated outcome may support, or inhibit, personalised and sensitive conversations that would instigate a partnership approach to care [34].
## Implications for research and practice
Reconceptualising weight management care to align with the partnership approach that enacts midwifery guardianship is warranted. Whilst we work in a medical system that utilises clinical guidelines to inform healthcare professionals, recommendations must consider how a woman’s social, environmental and psychological context can be considered in order to reduce weight stigmatisation, increase her choice and empower her. To achieve the partnership approach that maternity care policy advocates, the manner in which weight conversations in pregnancy are had, the subsequent communication of practical, individualised advice as well as the dominant risk-focus of the guidelines must be re-examined.
Implementing evidence-based recommendations must be complemented by equipping healthcare professionals with specific training to increase skills, knowledge and confidence, frequently identified as barriers to providing supportive weight management care. This includes offering professionals support in communicating risk about weight in a way that the woman can make sense of considering her values, beliefs and preferences to dissipate feelings of anxiety or guilt. Co-designing clinical guidelines with women who have experienced pregnancy with obesity or excessive gestational weight gain could be fundamental in the design of tools we use to deliver weight management care.
## Strengths and limitations
To our knowledge, this is the first study to use a theoretical framework to understand weight management guidelines that are used across different NHS Trusts in England. Adding to existing evidence surrounding weight management care provision during pregnancy, this study helpfully adds a new perspective that challenges current practices by highlighting the disconnect between the ambition of maternity policy and guideline recommendations. It has also provided some context for the barriers faced by healthcare professionals as well as the experiences of pregnant women; providing momentum for ongoing inspection of national and local clinical guidelines that may hinder progression towards supportive, collaborative weight management care that empowers women.
A purposive sample generated a well-represented national picture of guidelines from all seven NHS regions in England. Maternity units in Scotland, Wales and Northern Ireland were not included due to the differences in NHS or non-NHS healthcare structures, which would have made comparability with NICE and RCOG guidelines difficult. Therefore, a study comparing local guidelines beyond England may be warranted.
Using the NICE and RCOG guidelines as a deductive analysis framework was useful to ensure we explored all aspects of weight management care. However, we did not include associated guidelines in our framework such as ‘Maternal and child nutrition’ [PH11], ‘Antenatal care’ [NG201] or ‘Postnatal care’ [NG194] guidelines [35–37]. It is important to acknowledge that they may provide additional detail on the care women receive. However, the focus of this synthesis was weight management and therefore, to ensure the study had a clear focus on this, other guidelines were not used in the framework for analysis.
## Conclusion
Our guideline synthesis has shown that locally interpreted weight management guidelines are rooted in a risk discourse, rather than a partnership approach that is advocated in maternity policy. This disconnect could explain the rifts in weight management care delivery that is evidenced in existing literature. Although some consistent recommendations existed and were reflective of national guidance, ambiguous care pathways and unclear recommendations prevailed that would hinder a healthcare professionals ability to translate these into practice. Future research should address the tools in which maternity healthcare providers use to provide weight management care and a revaluation of the system in which they work in order to achieve the ambition for all pregnant and postnatal people to receive sensitive, personalised and safe weight management care.
## Supplementary Information
Additional file 1: Figure 1. A map of NHS Trusts in England contacted for guidelines. Map created with ZeeMaps (www.zeemaps.com).
## Authors’ information
LG, the principle researcher, is a registered midwife currently undertaking a PhD. Regular discussions with JM, a qualitative researcher with a midwife/nurse background, and the wider research team were held during interrogation of the data where interpretations were derived from the perspective of healthcare professional and researcher.
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|
---
title: Hypoxia-responsive circular RNA circAAGAB reduces breast cancer malignancy
by activating p38 MAPK and sponging miR-378 h
authors:
- Kuan-Yi Lee
- Chia-Ming Liu
- Li-Han Chen
- Chien-Yueh Lee
- Tzu-Pin Lu
- Li-Ling Chuang
- Liang-Chuan Lai
journal: Cancer Cell International
year: 2023
pmcid: PMC10007766
doi: 10.1186/s12935-023-02891-0
license: CC BY 4.0
---
# Hypoxia-responsive circular RNA circAAGAB reduces breast cancer malignancy by activating p38 MAPK and sponging miR-378 h
## Abstract
### Background
Breast cancer is a prevalent disease in women, with high prevalence worldwide. The hypoxic microenvironment of solid tumors develops during the progress of carcinogenesis and leads to greater malignancy and treatment resistance. Recently, accumulating evidence indicates that non-coding RNAs, such as circular RNAs (circRNAs), play a pivotal role in altering cellular functions. However, the underlying mechanisms of circRNAs in breast cancer are still unclear. Therefore, the purpose of this study was to investigate the role of a tumor-suppressive circRNA, circAAGAB, in breast cancer by assuming down-regulation of circAAGAB under hypoxia and the properties of a tumor suppressor.
### Methods
Firstly, circAAGAB was identified from expression profiling by next generation sequencing. Next, the stability of circAAGAB increased by interacting with the RNA binding protein FUS. Moreover, cellular and nuclear fractionation showed that most circAAGAB resided in the cytoplasm and that it up-regulated KIAA1522, NKX3-1, and JADE3 by sponging miR-378 h. Lastly, the functions of circAAGAB were explored by identifying its down-stream genes using Affymetrix microarrays and validated by in vitro assays.
### Results
The results showed that circAAGAB reduced cell colony formation, cell migration, and signaling through p38 MAPK pathway, as well as increased radiosensitivity.
### Conclusion
These findings suggest that the oxygen-responsive circAAGAB acts as a tumor suppressor in breast cancer, and may contribute to the development of a more specific therapeutic regimen for breast cancer.
## Introduction
Cancer is a major disease worldwide with high occurrence, poor prognosis, and high mortality [1]. Because of genetic and epigenetic changes, normal cells progressively transform into cancer cells, resulting in uncontrolled cell division and rapid growth [2]. Two main categories of genes are involved in the process of carcinogenesis: oncogenes and tumor suppressor genes. Tumor suppressor genes can inhibit cell proliferation and tumor development in normal cells. However, when tumor suppressor genes are inactivated by loss of function mutations, they facilitate tumorigenesis [3]. During solid tumor progression, the rapid proliferation of cancer cells outpaces the growth of the surrounding blood vessels and results in insufficient blood supply, leading to a hypoxic microenvironment. Thus, cancer cells must alter their molecular mechanisms and metabolism to adapt to hypoxia in order to support continuous growth and proliferation.
In addition, due to these genetic alterations, tumor hypoxia enhances resistance to treatments such as chemotherapy, radiotherapy, and immunotherapy. Hypoxia decreases pH and forms an acidic microenvironment, which leads to drug resistance through a series of mechanisms, such as a lower concentration of the drug caused by the ion trapping phenomenon [4] or activity of a multidrug transporter p-glycoprotein [5]. Also, hypoxia enhances resistance to radiation therapy [6]. Thus, most hypoxic tumor cells grow continuously. Furthermore, immunity is also suppressed in a hypoxic microenvironment by inhibiting the recruitment of T-cells, myeloid-derived suppressor cells, macrophages, and neutrophils [7], or increasing resistance of tumor cells to the cytolytic activity of immune effectors [8, 9], as well as up-regulating immune checkpoint proteins, such as programmed death 1 ligand (PD-L1) [10] and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) receptor [11]. Altogether, tumor hypoxia makes cancer cells more malignant and resistant to therapy.
A growing body of evidence shows that non-coding RNAs play a pivotal role in regulating signaling pathways and modulating tumor progression [12, 13]. Circular RNAs (circRNAs), a class of non-coding RNAs with a single-stranded circular structure, were considered as functionless byproducts of aberrant RNA splicing at first [14]. In fact, they are functional nucleic acids derived from pre-mRNA and created through back-splicing of the 3’ end of a donor to the 5’ end of an acceptor [15]. This particular form enables circRNAs to lack polyA tails and be more stable than linear RNAs [16]. Recently, a number of reports have indicated that circRNAs have many biological functions [17]. For example, circRNAs act as a sponge for microRNA (miRNA) to inhibit its interaction with its target genes in the cytoplasm [18–20]. CircRNAs also interact with RNA binding proteins (RBPs) to regulate transcription [21–23], splicing, or epigenetic alterations [21, 23–25]. Some research also demonstrated that circRNAs can be translated into functional small peptides through ribosome binding on internal ribosome entry site (IRES) [26, 27]. As a result of these diverse functions, circRNAs are involved in the pathogenesis of various human diseases, such as cardiovascular disease [28], diabetes [29], nervous system disorders [30], and cancer [31]. Especially, circRNAs play the crucial roles of oncogene or tumor suppressor via a variety of mechanisms. For instance, an oncogenic circRNA, circRNA-MYLK, induced epithelial–mesenchymal transition (EMT), cell proliferation, and angiogenesis by activating the VEGFA/VEGFR2 signaling pathway in bladder cancer [32]. In contrast, cir-ITCH acted as a tumor suppressor in colorectal cancer by regulating the Wnt/β-catenin pathway [33]. However, the detailed mechanism of circRNA in regulating breast cancer cells as they adapt to hypoxia still remains unclear.
In this study, a hypoxia-responsive circRNA, circAAGAB, derived from the alpha- and gamma-adaptin-binding protein p34 gene AAGAB, was identified in breast cancer MCF-7 cells by RNA sequencing, and the circular structure and expression levels under different oxygen concentrations were validated. CircAAGAB resided mainly in the cytoplasm and was found to sponge the miRNA miR-378 h and bind to the RNA binding protein FUS. Finally, genes regulated by circAAGAB were identified by Affymetrix microarrays, and in vitro functional assays showed inhibition of proliferation and migration ability as well as the increase of radiosensitivity in breast cancer MDA-MB-231 cells overexpressing circAAGAB.
## Cell culture
Breast cancer cell lines, MCF7 and MDA-MB-231, and the HEK293T cell line were cultured in Dulbecco’s Modified Eagle Medium (GIBCO, Carlsbad, CA, USA). MDA-MB-361 cells were cultured in L-15 medium (GIBCO). ZR-75-30 cells were cultured in Dulbecco's Modified Eagle Medium: Nutrient Mixture F-12 (GIBCO). T47D cells were cultured in RPMI 1640 medium (GIBCO). All breast cancer cell lines and HEK293T were supplemented with $10\%$ fetal bovine serum (FBS) (HyClone, Logan, UT, USA) and $1\%$ penicillin–streptomycin solution (GIBCO). All cell lines were incubated at 37 °C in a humidified incubator with $5\%$ CO2 under normoxia. To verify whether circAAGAB was oxygen-responsive, cells were cultured in a hypoxic chamber (InVivO2-200, Ruskinn Technology, Bridgend, UK) filled with $0.5\%$ O2, $5\%$ CO2, and $94.5\%$ N2 for 24 h. After incubation under hypoxia, cells were moved to the humidified incubator with normoxic conditions for another 24 h to mimic re-oxygenation.
## Cell line authentication
Cell experiments were performed on cells that were passaged less than 15 times and were routinely tested for mycoplasma using PCR Mycoplasma Detection Kit (ABM Inc., Vancouver, Canada). The cell lines were purchased and authenticated by the Bioresource Collection and Research Center, Food Industry Research and Development Institute (Hsinchu, Taiwan).
## Plasmid construction, RNA interference, and miRNA overexpression
To overexpress circular RNA circAAGAB, the circAAGAB sequence was inserted into the DNA plasmid pCIRC2T7 to construct pCIRC2T7-circAAGAB (BioMed Resource Core (BMRC) of the 1st Core Facility Lab, College of Medicine, National Taiwan University). To check the interaction and binding site between circAAGAB and miR-378 h, plasmid pMIR-REPORT-circAAGAB (BMRC) was constructed by inserting the circAAGAB sequence behind the sequence of firefly luciferase. In addition, pMIR-REPORT-circAAGAB-mut (BMRC) was constructed by mutating the putative binding site on circAAGAB. To knock down the expression of FUS, MDA-MB-231 cells were transfected with 5 μM of small interfering RNA (siRNA) (Dharmacon, Lafayette, CO, USA) in 2 mL medium for 48 h. To overexpress miR-378 h, MDA-MB-231 cells were transfected with 5 μM of miR-378 h mimics (Dharmacon) in 2 mL medium for 48 h. After transfection, MDA-MB-231 cells were lysed to extract total RNA.
## Genomic DNA extraction, RNA isolation, reverse transcription, and quantitative RT-PCR
Genomic DNA (gDNA) was extracted by QIAamp DNA Kits (Qiagen, Hilden, Germany). Cells were lysed by Nucleozol reagent (Machery-Nagel, Düren, Germany) and total RNA was purified according to the manufacturer’s protocols. Subsequently, total RNA was reverse-transcribed to complementary DNA (cDNA) using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Carlsbad, CA, USA). For reverse transcription of miRNA, SuperScript IV Reverse Transcriptase (Invitrogen, CA, USA) was used with the primers (Table 1). cDNA acted as the template for quantitative RT-PCR with OmicsGreen qPCR MasterMix (OmicsBio, New Taipei City, Taiwan), and the cycle threshold (Ct) value was detected by StepOnePlus Real-Time PCR System (Thermo Fisher, Waltham, MA, USA). The relative gene expression was evaluated using the 2−∆∆Ct method. Table 1The primers for quantitative RT-PCRGene/miRNAPrimerSequence (5ʹ to 3ʹ)Reverse transcription miR-3127-5pGTTGGCTCTGGTGCAGGGTCCGAGGTATTCGCACCAGAGCCAACCTTCCCA miR-671-5pGTTGGCTCTGGTGCAGGGTCCGAGGTATTCGCACCAGAGCCAACCTCCAGC miR-422aGTTGGCTCTGGTGCAGGGTCCGAGGTATTCGCACCAGAGCCAACGCCTTCT miR-378iGTTGGCTCTGGTGCAGGGTCCGAGGTATTCGCACCAGAGCCAACCCTTCTG miR-378 hGTTGGCTCTGGTGCAGGGTCCGAGGTATTCGCACCAGAGCCAACCCATCTG U6 snRNACGCTTCACGAATTTGCGTGTCATQuantitative RT-PCR circAAGABForwardCTGAATGCCAATGTGTGGTCReverseCTATCAAGGCCCGATTTTTG AAGABForwardACAGCACACAAA AATCGGGCReverseATTGGCATTCAGGGCTTGGA AAGAB exon 2ForwardAGTGACTTCCAATGATGCTGT GReverseTGTGTGCTGTCAAAGTAAACCAC 18S rRNAForwardTCAACTTTCGATGGTAGTCGCCGTReverseTCCTTGGATGTGGTAGCCGTTTCT GAPDHForwardAACGGGAAGCTTGTCATCAATGGA AAReverseGCATCAGCAGAGGGGGCAGAG BCAR4ForwardGTTCCGATGCTTGTCTTGCTCReverseCCAAAGACGAAGATGCCAGG U6 snRNAForwardGCTTCGGCAGCACATATACTAAAATReverseCGCTTCACGAATTTGCGTGTCAT miR-3127-5pForwardCGATCAGGGCTTGTGGAAReverseGTGCAGGGTCCGAGGT miR-671-5pForwardCGGAGGAAGCCCTGGAReverseGTGCAGGGTCCGAGGT miR-422aForwardCGCGACTGGACTTAGGGTCReverseGTGCAGGGTCCGAGGT miR-378iForwardCGCGACTGGACTAGGAGTReverseGTGCAGGGTCCGAGGT miR-378 hForwardCGCACTGGACTTGGTGTReverseGTGCAGGGTCCGAGGT KIAA1522ForwardCCAGGACAACGTCTTCTTTCCReverseCAGCCACCCTTGTTCAGTTTC NKX3-1ForwardCCCACACTCAGGTGATCGAGReverseGAGCTGCTTTCGCTTAGTCTT PHC3ForwardACAGCAGTCAAGTATGTCCCAReverseCTTGCCGTTAGGGTAGGGG ELAC1ForwardGCTGGTCTTCCATTATGTGGTTReverseGTGAAAGAGGCGAAATGCTTTT ZNF124ForwardAAAGCCTTAGGTTTTTCCCGTTReverseACATGGATAGGGTTCTTCACCA RAB10ForwardCTGCTCCTGATCGGGGATTCReverseTGATGGTGTGAAATCGCTCCT KCNIP2ForwardAATGTCCCAGCGGAATTGTCAReverseGAGCCATCATGGTTGGTGTCA PLCXD2ForwardGACTCGTTTCTTACACAGCACCReverseAGCGTCAAACTTTCCACACTG JADE3ForwardGACGTTCTGTTTATCCGACCCReverseCCACAACCCATTTCTGCAAGG FUSForwardAGCTGGTGACTGGAAGTGTCReverseGGTAGCCGCCTCGATCATAG EGR1ForwardCCAGTGGAGTCCTGTGATCGReverseTTCATCGCTCCTGGCAAACT
## Western blotting
MDA-MB-231 cells were lysed in RIPA lysis buffer (Millipore, Billerica, MA, USA) with $0.1\%$ SDS and sonicated. Subsequently, the proteins diluted by sample buffer were separated by sodium dodecyl sulfate‑polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred onto polyvinylidene difluoride (PVDF) membranes (Bio‑Rad, Hercules, California, USA). The membranes containing proteins were blocked in Lightning Blocking Buffer (ArrowTec Life Science, Taiwan) for 5 min. Afterwards, membranes were incubated with primary antibodies against FUS (cat. no. A5921; ABclonal, Woburn, MA, USA), EGR1 (cat. no. 4154; Cell Signaling, Danvers, MA, USA), ATF3 (cat. no. 18665; Cell Signaling), phospho-p38 MAPK-T180/Y182 (cat. no. AP0526; ABclonal), total p38 MAPK (cat. no. A4771; ABclonal), vimentin (cat. no. ARG66199; Arigo Biolaboratories, Hsinchu, Taiwan), E-cadherin (cat. no. 3195; Cell Signaling), phospho-histone H2AX-Ser139 (cat. no. 2577; Cell Signaling), caspase-3 (cat. no. MBS8811560; MyBioSource, San Diego, CA, USA), GAPDH (cat. no. 2118; Cell Signaling), and ACTB (β-actin; cat. no. 4968; Cell Signaling) overnight at 4 °C. After washing 3 times with Tris-buffered saline with $1\%$ Tween-20, the membranes were hybridized with horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 h. Protein expression was visualized by an enhanced chemiluminescence substrate (Millipore, Billerica, MA, USA) and imaged using a BioSpectrum Imaging System (UVP, Upland, CA, USA). The intensities of bands were analyzed using ImageJ 1.48v (National Institutes of Health, USA).
## RNase R treatment
To confirm the circular structure of circAAGAB, total RNA was treated with 3 U RNase R (Lucigen, LGC Ltd, Teddington, UK) and 10X reaction buffer (Lucigen), and then incubated at 37 °C for 20 min. After RNA reverse transcription and PCR, PCR products were subjected to gel electrophoresis and visualized by a UVP Gel Solo system (Analytik Jena US, Upland, CA, USA).
## RNA pull-down assay
A total of 1 × 107 MDA-MB-231 cells were lysed by cell lysis buffer (25 mM Tris–HCL pH 7.5, 150 mM NaCl, 1 mM EDTA, $0.5\%$ NP-40) for each sample. Then, a Pierce Magnetic RNA–Protein Pull-Down Kit (Thermo Fisher) was used according to the manufacturer’s protocols. Magnetic beads conjugated with streptavidin were incubated with 3 μg biotinylated DNA oligo probe for 1 h. After the complex was formed, cell lysate was added into tubes containing the beads and incubated for 4 h. Subsequently, the complex was washed 4 times with wash buffer from the kit and 500 μL of cell lysis buffer. Finally, washed samples were measured by western blotting.
## Actinomycin D treatment
At approximately $60\%$ confluency, MDA-MB-231 cells were transferred into 6-well plates. Cells were treated with 5 μg/mL actinomycin D (Sigma, Saint Louis, MO, USA) dissolved in DMSO and collected at the indicated time points. Total RNA was purified after the cells were lysed. After treatment with actinomycin D, the RNA expression levels of circAAGAB were analyzed by quantitative RT-PCR.
## Nuclear-cytoplasmic fractionation
For nuclear-cytoplasmic fractionation, RNAs in 8 × 105 cells were extracted by a Cytoplasmic & Nuclear RNA Purification Kit (Norgen Biotek Corp., Ontario, Canada) according to the manufacturer’s protocols. The isolated RNA was detected by quantitative RT-PCR and normalized to GAPDH (cytoplasmic control) or BCAR4 (nuclear control).
## RNA fluorescence in situ hybridization (RNA-FISH)
MDA-MB-231 cells (6 × 105) were first seeded in cover glass chamber (80826, ibidi, Martinsried, Germany) overnight, washed with PBS once, and fixed with $4\%$ paraformaldehyde for 10 min. After washing with PBS twice, cells were dehydrated with $70\%$ EtOH for 2 h, and incubated with RNA probe (125 nM) at 37 °C overnight. The RNA labeling probes conjugated with 5ʹ modification 6-FAM (TTC CAA GGA TAT CAT TCT TCA TCA) were designed to target the back-splicing site of circ-AAGAB. Next, the cells were washed at 37 °C for 30 min, and mounted with Mounting Medium containing DAPI (ab104139, Abcam, Cambridge, UK). Finally, the images were acquired using a ZEISS LSM880 laser confocal microscopy (ZEISS, Heidelberg, Germany).
## Luciferase reporter assay
HEK293T cells were cultured in 24-well plates with 4 × 104 cells per well. Cells were co-transfected with 50 μg of pMIR-REPORT-circAAGAB or pMIR-REPORT-circAAGAB-mut, 2 × 10–2 ng of miR-378 h mimics or mimic control, and 1 μg of Renilla luciferase vector as the transfection control. After transfecting for 48 h, cells were lysed by 100 μL luciferase lysis buffer (92.8 mM K2HPO4, 9.2 mM KH2PO4 and $0.2\%$ Triton X-100 in ddH2O) on ice for 15 min and then centrifuged at 12,000xg relative centrifugal force for 2 min at 4 °C. Afterwards, the supernatant was isolated, and the luciferase signal was measured using the Dual-Glo Luciferase Assay System (Promega, Fitchburg, WI, USA).
## Microarray analysis
MDA-MB-231 cells were transfected with 4 μg of pCIRC2T7-circAAGAB plasmids and total RNA was purified. Subsequently, microarray experiments were done through the service of the Core Instrument Center, National Health Research Institutes (Miaoli, Taiwan). Briefly, human single-stranded cDNA was generated from the amplified complementary RNA with the WT Plus cDNA Synthesis Kit (Affymetrix, Santa Clara, CA, USA), and then fragmented and labeled with the WT Terminal Labeling Kit (Affymetrix). RNA expression profiling was performed using the Clariom S Assay (Affymetrix). After scanning, the data from the Affymetrix microarray was normalized by robust multichip averaging. Visual representation of expression profiles was evaluated by principal component analysis (PCA) and hierarchical clustering by the Genesis 1.7.7 program (Graz University of Technology, Graz, Austria). Interactions between genes, biological functions, and pathways were analyzed by Ingenuity Pathway Analysis (IPA, Ingenuity Systems Inc., Redwood City, USA). The datasets generated during the current study are available in the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158779).
## Colony formation assay
MDA-MB-231 cells were cultured in 6-well plates with 2 × 105 cells per well and transfected with 4 μg of pCIRC2T7-circAAGAB plasmids for 24 h. Cells were reseeded into 6-well plates with 500 cell per well. After 3 weeks, cells were fixed by fixing buffer containing $75\%$ methanol and $25\%$ acetate (Sigma), and $0.1\%$ crystal violet (Sigma) was added to dye the cells. Colonies of at least 50 cells were counted manually.
## Cell migration and invasion assay
MDA-MB-231 cells were seeded in 6-well plates with 2 × 105 cells per well and transfected with pCIRC2T7-circAAGAB plasmids for 24 h. For cell migration, 6 × 105 cells were seeded into the transwell chambers and incubated for 24 h. For cell invasion, 1 × 106 cells were seeded into the transwell chambers coated with Matrigel and incubated for 48 h. After migration and invasion, cells were fixed by fixing buffer containing $75\%$ methanol and $25\%$ acetate (Sigma), and $0.1\%$ crystal violet (Sigma) was added to stain the cells. Cells on the membrane were counted manually.
## Ionizing radiation (IR) treatment
MDA-MB-231 cells were seeded and transfected with pCIRC2T7-circAAGAB plasmids for 24 h. Then, the cells were exposed to 10 Gy of γ-rays by an IBL-637 Cesium-137 γ-ray machine (Cis-Bio International, Ion Beam Applications, Belgium) and harvested at 24 h after irradiation. Finally, the cells were stained by propidium iodide (PI) and analyzed on a Beckman Coulter FC500 instrument (Beckman Coulter, Inc.) using CXP Analysis Software v2.3 (Beckman Coulter, Inc.).
## Cell apoptosis and cell cycle analysis
MDA-MB-231 cells were seeded in 10 cm dishes with 1 × 106 cells per dish and transfected with pCIRC2T7-circAAGAB plasmids for 24 h. Cells for examining apoptosis were harvested and stained by using a FITC Annexin V Apoptosis Detection Kit I (PharMingen, BD Biosciences, NJ, USA) according to the manufacturer’s protocols. Cells for examining the cell cycle were harvested and fixed by $75\%$ ethanol at − 20 °C overnight, then lysed with $0.5\%$ Triton X-100 (Amersham, Little Chalfont, Buckinghamshire, UK), subjected to RNase A (Qiagen) treatment (20 ng RNase A/mL in PBS) and stained with PI (BD Biosciences, NJ, USA) solution (20 μg PI/mL in PBS) in the dark for 10 min. Afterwards, cell cycle and cell apoptosis were detected by a Beckman Coulter FC500 instrument (Beckman Coulter, Inc.) using CXP Analysis Software v2.3 (Beckman Coulter, Inc.).
## Statistical analysis
All quantitative data are presented as the means ± standard deviations of data from at least three independent experiments, and an unpaired Student’s t-test was used to compare differences between groups. All analyses were performed using Microsoft Office Excel software, and a P-value < 0.05 was considered to be statistically significant.
## Characterization of hypoxia-responsive circular RNA circAAGAB
Since hypoxia promotes the malignancy of solid tumors, in order to determine whether circRNA acts as a tumor suppressor in breast cancer, hypoxia-downregulated circRNAs were explored by RNA sequencing from MCF-7 cells growing under normoxia (O2), hypoxia (N2), and re-oxygenation (Re-O2) conditions. The transcriptome of different oxygen conditions was profiled by Illumina sequencing after depleting ribosomal RNA [34]. CircRNA Identifier (CIRI) [35] was used to predict putative circRNAs by identifying the sequence of the back-splicing junction. The criteria for selecting hypoxia-downregulated circRNAs consisted of significant differences (P-value < 0.05) between N2 and O2 conditions and N2 and Re-O2 conditions, as well as no significant differences (P-value > 0.05) between O2 and Re-O2 conditions. CircAAGAB, consisting of exons 2 to 5 of AAGAB (5,007 nucleotides) (Fig. 1), was chosen for further experiments because it was down-regulated the most under hypoxia as compared with normoxia. Fig. 1Characterization of hypoxia-responsive circular RNA circAAGAB in breast cancer cells under different oxygen concentrations. A Sequence of back-splicing junction using CircRNA Identifier (CIRI) [35]. B *Gel electrophoresis* of PCR products. Divergent primers (black arrows; ← →) and convergent primers (white arrows; → ←) for PCR. Genomic DNA (gDNA) or complementary DNA (cDNA) was used as the template to examine endogenous circAAGAB in MCF-7 and MDA-MB-231 cells. The PCR product length is 248 bp using divergent primers and 159 bp by using convergent primers. C Relative expression levels of circAAGAB by quantitative RT-PCR. Random primers or oligo dT were used for RT-PCR. N.D.: not detected. D *Gel electrophoresis* of PCR products from MCF-7 cells treated with RNase R under different oxygen concentrations. MCF-7 cells were treated with 3U RNase R for 20 min under normoxia (O2), hypoxia (N2), or re-oxygenation (Re-O2). The PCR product length of circular AAGAB is 248 bp and length of linear AAGAB is 254 bp. E Endogenous expression levels of circAAGAB in 5 breast cancer cell lines and normal breast epithelial MCF-10A cells by quantitative RT-PCR. F Quantitative RT-PCR analysis of circAAGAB in 5 breast cancer cell lines under normoxia and hypoxia. All of the quantitative RT-PCR results were normalized to the internal control 18S. *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ Firstly, to validate this circRNA by PCR, divergent primers (black arrows) were designed for priming at the back-splicing junction of circAAGAB; convergent primers (white arrows) were designed at exon 2 for both circular and linear AAGAB (Fig. 1B). gDNA and cDNA were used as the template in MCF-7 and MDA-MB-231 cells. The results showed the existence of linear AAGAB in gDNA and cDNA by using convergent primers (→ ←), and that circular AAGAB could only be amplified by using divergent primers (← →) in cDNA, not gDNA (Fig. 1B). In addition, given that the absence of a polyA tail is one of the characteristics of circRNAs, random primers and oligo dT primers were used to reverse-transcribe total RNA to cDNA, followed by quantitative RT-PCR. The results showed that only random primers, not the oligo dT primer, were able to amplify circAAGAB (Fig. 1C). Fourthly, total RNA was extracted from MCF-7 cells under O2, N2, and Re-O2 conditions, and was treated with 3 U of RNase R, an enzyme to digest linear RNAs. The product amplified by the divergent primer (circAAGAB) maintained the same expression level through RNase R treatment, but linear AAGAB was degraded in the presence of RNase R (Fig. 1D).
To determine the endogenous expression of circAAGAB in breast cancer cells, MDA-MB-361, MCF-7, T47D, ZR-75-30 and MDA-MB-231 cells, and human breast epithelial cell line MCF-10A cells were examined by quantitative RT-PCR. The results revealed that MDA-MB-231 cells had the least endogenous expression of circAAGAB (Fig. 1E). Furthermore, circAAGAB was significantly ($P \leq 0.05$) down-regulated under hypoxia in all examined breast cancer cell lines (Fig. 1F). These data suggested that the endogenous circAAGAB was downregulated under hypoxia in breast cancer cells.
## Identification of RNA binding protein of circAAGAB
Next, to identify RBPs interacting with circAAGAB, the bioinformatics tools, Encyclopedia of RNA Interactomes (ENCORI; (http://starbase.sysu.edu.cn/) and RNA-Binding Protein DataBase (RBPDB; http://rbpdb.ccbr.utoronto.ca/) were used to predict putative RBPs. ELAVL1, FUS, KHDRBS3, and YTHDC1 were the commonly predicted RBPs (Fig. 2A). Also, RNA-Protein Interaction Prediction (RPISeq) (http://pridb.gdcb.iastate.edu/RPISeq/) predicted the possible binding between circAAGAB and the four RBPs (Fig. 2B). In this study, FUS was chosen to explore whether it binds with circAAGAB. Firstly, RNA pull-down assays using a biotin-labeled probe against the junction of exons 2 and 5 of circAAGAB were performed in MDA-MB-231 cells. The results of western blotting illustrated that FUS could be pulled down by the probe specifically targeting circular AAGAB, but not by control probe (Fig. 2C). Next, to explore the effects of FUS on circAAGAB, FUS was knocked down by siRNA in MDA-MB-231 cells, and this significantly ($P \leq 0.05$) decreased the expression of circAAGAB (Fig. 2D). Lastly, to explore the mechanism by which FUS silencing down-regulated circAAGAB in MDA-MB-231 cells, the stability of circAAGAB was examined. MDA-MB-231 cells were transfected with siRNA against FUS and simultaneously treated with actinomycin D, a transcription inhibitor, and then the expression levels of circAAGAB were examined at 0, 2, 4, 8, 12 h, respectively. The results displayed that the expression levels of circAAGAB were decreased significantly ($P \leq 0.05$) after silencing FUS (Fig. 2E). These findings suggested that FUS increased the stability of circAAGAB through direct binding. Fig. 2RNA binding protein FUS increases the stability of circAAGAB via direct binding. A Venn diagram of the predicted RNA binding proteins (RBPs). RNA binding proteins with circAAGAB were predicted by ENCORI (http://starbase.sysu.edu.cn/) and RBPDB (http://rbpdb.ccbr.utoronto.ca/). * The 4 common RBPs are shown. B The predicted possibility of interaction between circAAGAB and RBP. The binding possibility was calculated by RNA–Protein Interaction Prediction (RPISeq) (http://pridb.gdcb.iastate.edu/RPISeq/) using random forest (RF) and support vector machine (SVM) classifiers. Predictions with probabilities > 0.5 were considered positive. C RNA pull-down assay. Cell lysates were extracted from MDA-MB-231 cells, pulled down by biotin-labeled probe against circAAGAB, and subjected to western blotting using FUS antibody. D Expression levels of RNA in MDA-MB-231 cells transfected with siRNA against FUS by quantitative RT-PCR. NC: nontargeting control siRNA. Internal control: GAPDH. E Stability assay of circAAGAB. MDA-MB-231 cells were transfected with 5 μg/mL actinomycin D and siRNA against FUS. The expression levels of circAAGAB were measured by quantitative RT-PCR at various time points. NC: nontargeting control siRNA. Internal control: GAPDH. * $P \leq 0.05$, **$P \leq 0.01$
## Identification of microRNA sponged with circAAGAB
Next, to explore the regulatory roles of circAAGAB in breast cancer cells, nucleus-cytoplasm fractionation for RNA was performed in MCF-7 and MDA-MB-231 cells under hypoxia. The results displayed that circAAGAB was mostly distributed in the cytoplasm under hypoxia in both MCF-7 (Fig. 3A) and MDA-MB-231 cells (Fig. 3B). RNA-FISH assay also indicated that the location of circ-AAGAB was mainly at cytoplasm (Fig. 3C). Since circRNAs in cytoplasm were reported to serve as miRNA sponges, the potential miRNA targets were predicted by ENCORI and miRDB (http://mirdb.org/). Among the predicted miRNAs, there were 12 miRNAs in common (Fig. 3D). The top 5 miRNAs with the highest target score from miRDB analysis (miR-3127-5p, miR-671-5p, miR-422a, miR-378i, and miR-378 h) were chosen for experimental validation (Fig. 3D). First, MCF-7 and MDA-MB-231 cells were transfected with the vector, pCIRC2T7-circAAGAB, to overexpress circAAGAB. After circAAGAB was successfully overexpressed in MCF-7 and MDA-MB-231 cells (Fig. 3E), the expression levels of the predicted miRNAs were measured in MCF-7 (Fig. 3F) and MDA-MB-231 (Fig. 3G) cells overexpressing circAAGAB. Among the sponged miRNAs, the results showed that only miR-378 h expression was significantly ($P \leq 0.05$) reduced in both MCF-7 (Fig. 3F) and MDA-MB-231 (Fig. 3G) cells overexpressing circAAGAB. To further verify the direct binding of circAAGAB to miR-378 h, the luciferase reporter plasmid, pMIR-REPORT-circAAGAB, was constructed, and the potential binding site of miR-378 h on circAAGAB, i.e., the seed region of miR-378 h, was mutated (Fig. 3H). When HEK293T cells were co-transfected with pMIR-REPORT-circAAGAB and miR-378 h mimics, the luciferase signals were significantly ($P \leq 0.01$) reduced, and this result was reversed in the presence of the circAAGAB mutant (Fig. 3I). Furthermore, as the expression of circAAGAB was downregulated under hypoxia, the expression levels of miR-378 h were upregulated in MDA-MB-231 cells under hypoxia (Fig. 3J).Fig. 3CircAAGAB acts as a sponge for miR-378 h and inhibits its effect on target genes. A Nucleus-cytoplasm fractionation of RNA in MCF-7 cells under hypoxia. Distribution of circAAGAB in MCF-7 cells was detected by quantitative RT-PCR. GAPDH: cytoplasmic marker; BCAR4: nuclear marker. B Nucleus-cytoplasm fractionation of RNA in MDA-MB-231 cells under hypoxia. C RNA fluorescence in situ hybridization. Probe labelled with digoxigenin-conjugated UTP was designed to hybridize circAAGAB. DAPI: nucleus marker. Scale bar: 20 μm. D Venn diagram of the predicted miRNAs. The circAAGAB-binding miRNA candidates were predicted by ENCORI (http://starbase.sysu.edu.cn/) and miRDB (http://mirdb.org/). * Top 5 miRNAs with the highest target score of the 12 common miRNAs are shown. E Expression levels of circAAGAB in cells overexpressing circAAGAB by quantitative RT-PCR. MCF-7 and MDA-MB-231 cells were transfected with 4 μg circAAGAB plasmids (pCIRC2T7-circAAGAB). Internal control: 18S rRNA. F, G Expression levels of miRNAs in MCF-7 (F) or MDA-MB-231 (G) cells overexpressing circAAGAB. The top 5 miRNAs with highest target score from miRDB were chosen for validation by quantitative RT-PCR. Internal control: 18S rRNA. H Schematic graph of the potential binding site of circAAGAB on miR-378 h. The binding site was aligned by ENCORI. Letters in red are the mutation site. I Luciferase reporter assay. HEK293T cells were co-transfected with pMIR-REPORT-circAAGAB or its mutant plasmid, miR-378 h mimics, and Renilla plasmid for 48 h. The luciferase signal was measured by the Dual-Glo luciferase reporter assay system. The firefly signal was normalized to Renilla signal and mimic control. J Expression levels of circAAGAB and miR-378 h in MDA-MB-231 cells under hypoxia. NDRG1: positive control of hypoxia. K Expression levels of the predicted target genes of miR-378 h in cells overexpressing miR-378 h. MDA-MB-231 cells were transfected with miR-378 h mimics. RNA levels of the target genes predicted by miRDB and DIANA (http://diana.imis.athena-innovation.gr/DianaTools/index.php) were measured by quantitative RT-PCR. Internal control: GAPDH. L Expression levels of the downstream genes in MDA-MB-231 cells overexpressing circAAGAB and/or miR-378 h by quantitative RT-PCR. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$ Next, to investigate the target genes of miR-378 h, miRDB and DIANA (http://diana.imis.athena-innovation.gr/DianaTools/index.php) were first used to predict the target genes. The top 9 genes with the highest target scores from the miRDB analysis were examined in MDA-MB-231 cells overexpressing miR-378 h. The RNA levels of only three genes, KIAA1522, NKX3-1, and JADE3, were significantly ($P \leq 0.05$) decreased (Fig. 3K). Subsequently, quantitative RT-PCR was performed to confirm whether KIAA1522, NKX3-1, and JADE3 were downstream genes of circAAGAB. As the results show, KIAA1522, NKX3-1, and JADE3 expression significantly ($P \leq 0.05$) increased in MDA-MB-231 cells overexpressing circAAGAB, and downregulated in cells overexpressing miR-378 h alone or together with circAAGAB (Fig. 3L). These results suggested that miR-378 h was the downstream gene of circAAGAB and circAAGAB disinhibited the target genes of miR-378 h, KIAA1522, NKX3-1, and JADE3, by sponging miR-378 h (Fig. 3L).
## Identification of circAAGAB-regulated genes
To investigate the cellular function of circAAGAB in breast cancer cells, differentially expressed genes were first identified in MDA-MB-231 cells overexpressing circAAGAB by Affymetrix microarrays. The genomic profiling of MDA-MB-231 cells overexpressing circAAGAB was evaluated by PCA. As shown in Fig. 4A, the distribution between circAAGAB-overexpressing samples (yellow dots) and the empty control samples (blue dots) was separated, indicating the different transcription profiling in MDA-MB-231 cells overexpressing circAAGAB. The criteria we used to define differentially expressed genes were a ≧1.5-fold change and a P-value < 0.05. In total, 77 differentially expressed genes were identified, 45 up-regulated and 32 down-regulated genes (Fig. 4B, C). Next, IPA was used to analyze the functions which the circAAGAB-regulated genes were involved in. One of the representative networks showed that the circAAGAB-regulated genes were involved in cell death and survival (Fig. 4D). In this network, 2 hub genes, EGR1 and ATF3, which had the highest fold changes in the microarray data, were validated by quantitative RT-PCR (Fig. 4E) and western blotting (Fig. 4F&G). Both showed significant ($P \leq 0.01$) up-regulation in MDA-MB-231 cells overexpressing circAAGAB (Fig. 4E–G). In addition, results of canonical pathway analysis revealed that circAAGAB downstream genes participated in the p38 MAPK signaling pathway (Fig. 4H). The results of western blotting indeed showed that phosphorylated p38 MAPK was significantly ($P \leq 0.05$) activated in MDA-MB-231 cells overexpressing circAAGAB (Fig. 4I, J). These results indicated that circAAGAB regulated apoptosis-related genes, EGR1 and ATF3, and the p38 MAPK signaling pathway. Fig. 4CircAAGAB-regulated genes are involved in the p38 MAPK signaling pathway. A Principal component analysis (PCA) of samples overexpressing circAAGAB. PCA was plotted by the expression profiles of differentially expressed probes after quantile normalization. Yellow dots represent the circAAGAB-overexpressing group, and blue dots represent the empty vector group. B Volcano plot of differentially expressed genes in MDA-MB-231 cells overexpressing circAAGAB by Affymetrix microarray. Horizontal dashed line corresponds to $$P \leq 0.05.$$ Vertical dashed lines correspond to fold change (FC) > 1.5X. C Heat map of differentially expressed genes. Red: higher expression levels as compared to the average of this gene in the empty vector group. Green: lower expression levels. Scale bar: 10 genes. D A representative network of circAAGAB downstream genes by Ingenuity Pathway Analysis. E Expression levels of EGR1 and ATF3 in MDA-MB-231 cells overexpressing circAAGAB by quantitative RT-PCR. Internal control: GAPDH. F Western blotting of EGR1 and ATF3 protein in MDA-MB-231 cells overexpressing circAAGAB. Loading control: ACTB. G Quantification of EGR1 and ATF3 protein levels in MDA-MB-231 cells overexpressing circAAGAB. H Canonical pathways analysis of circAAGAB downstream genes. I Western blotting for the phosphorylated (p) p38 MAPK in MDA-MB-231 cells overexpressing circAAGAB. t: total protein. J Quantification of phosphorylated (p) p38 MAPK in MDA-MB-231 cells overexpressing circAAGAB. Phosphorylated p38 MAPK was normalized to total (t) p38 MAPK. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$
## Identification of function of circAAGAB
Lastly, in vitro functional assays were performed. To determine the effects of circAAGAB on cell proliferation and colony formation, BrdU and MTT assays (data not shown) were conducted in MDA-MB-231 cells. The results indicated that circAAGAB inhibited colony formation (Fig. 5A, B), but had no effect on short-term cell proliferation by BrdU assays and MTT assays (data not shown). In addition, transwell assays were performed to evaluate cell migration (Fig. 5C, D) and invasion (Fig. 5E, F). The data illustrated that migratory and invasive cells were significantly ($P \leq 0.05$) decreased in MDA-MB-231 cells overexpressing circAAGAB (Fig. 5C–F). Also, the EMT markers vimentin (VIM) and E-cadherin (ECAD), were further measured. As expected, vimentin (mesenchymal marker) expression was significantly ($P \leq 0.05$) down-regulated, and E-cadherin (epithelial marker) was significantly ($P \leq 0.05$) up-regulated after overexpressing circAAGAB (Fig. 5G, H). These results suggested that circAAGAB played the role of tumor suppressor, inhibiting colony formation, cell migration, invasion, and EMT in MDA-MB-231 cells. Fig. 5CircAAGAB inhibits colony formation, cell migration, invasion and epithelial-mesenchymal transition in MDA-MB-231 cells. A Colony formation assay in MDA-MB-231 cells overexpressing circAAGAB. MDA-MB-231 cells were transfected with circAAGAB plasmids, seeded into 6-well dishes, and incubated for 3 weeks. B Quantification of colony formation assay. C Migration assays of MDA-MB-231 cells overexpressing circAAGAB. Cells were transfected with circAAGAB plasmids, and 6 × 105 cells were seeded into the inserts. The image was taken at 24 h after seeding. D Quantification of the migrating cells by transwell assays. E Invasion assays of MDA-MB-231 cells overexpressing circAAGAB. Cells were transfected with circAAGAB plasmids, and 1 × 106 cells were seeded into the inserts with Matrigel coating on the membrane. The image was taken at 48 h after seeding. F Quantification of the invasive cells by transwell assays. G Western blotting for epithelial–mesenchymal transition markers vimentin (VIM) and E-cadherin (ECAD) in MDA-MB-231 cells overexpressing circAAGAB. Loading control for VIM: GAPDH. Loading control for E-cadherin ECAD: ACTB. H Quantification of VIM and ECAD in MDA-MB-231 cells overexpressing circAAGAB. The expression values were normalized to the respective loading controls. * $P \leq 0.05$, ***$P \leq 0.001$ As shown in the previous results, circAAGAB downstream genes were involved in apoptosis. To determine whether circAAGAB regulated cell apoptosis in breast cancer cells, flow cytometry and western blotting were applied in MDA-MB-231 cells overexpressing circAAGAB. However, no sign of apoptosis was observed (data not shown). Therefore, we further examined whether circAAGAB affected radiosensitivity. As shown in Fig. 6, overexpression of circAAGAB in MDA-MB-231 cells after IR significantly ($P \leq 0.01$) increased the sub-G1 proportion of the cell population (Fig. 6A&B). Similarly, PI staining and annexin V staining illustrated that the percentage of cells in late apoptosis was significantly ($P \leq 0.05$) increased in MDA-MB-231 cells 24 h after IR (Fig. 6C, D). Subsequently, the marker of DNA damage and repair, γH2AX (Fig. 6E, F), and the apoptosis marker, caspase-3 (Fig. 6G, H), were examined in MDA-MB-231 cells overexpressing circAAGAB after IR treatment. The increased amounts of γH2AX (Fig. 6E, F) and caspase-3 (Fig. 6G, H) indicated an inability to repair DNA, resulting in more cell death after IR treatment. These results indicated that circAAGAB promoted radiosensitivity in breast cancer cells. All the above results indicated that the hypoxia-responsive circRNA, circAAGAB, interacted with FUS, sponged miR-378 h, restrained cell colony formation, cell migration and invasion, and increased radiosensitivity in breast cancer cells through the p38 MAPK signaling pathway (Fig. 7).Fig. 6CircAAGAB increases radiosensitivity in MDA-MB-231 cells. A Representative diagram of flow cytometry in MDA-MB-231 cells overexpressing circAAGAB and treated with ionizing radiation (IR). Cells were transfected with circAAGAB plasmids and exposed to 10 Gy IR. Cells were then collected at 24 h after IR, fixed with $75\%$ ethanol overnight, and stained with PI. B Quantification of cell cycle and apoptotic cells (sub-G1). C Representative diagram of flow cytometry with annexin-V and PI staining. Cells were transfected with circAAGAB plasmids and exposed to 10 Gy IR. Cells were then collected at 24 h after IR. D Quantification of early and late stages of apoptosis in MDA-MB-231 cells. E, G Western blotting of γH2AX (E) and caspase-3 (CASP3) (G) in MDA-MB-231 cells overexpressing circAAGAB and treated with IR. Cells were transfected with circAAGAB plasmids and exposed to 10 Gy IR. Cells were collected at 4 h after IR for γH2AX, and 6 h for CASP3. Loading controls: ACTB or GAPDH. F, H Quantification of γH2AX (F) and CASP3 (H) in MDA-MB-231 cells overexpressing circAAGAB. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$Fig. 7Schematic diagram elucidates the regulatory mechanisms and function of hypoxia-responsive circAAGAB in breast cancer cells
## Discussion
In this study, the functions and regulatory mechanisms of a novel hypoxia down-regulated circRNA, circAAGAB, were identified in breast cancer. First, the circular structure of circAAGAB and its expression levels under different oxygen concentrations were validated. Next, we established that the stability of circAAGAB increased by binding with the RNA binding protein FUS. Also, circAAGAB acted as a sponge of miR-378 h, resulting in up-regulation of KIAA1522, NKX3-1, and JADE3, target genes of miR-378 h. Finally, circAAGAB reduced colony formation and cell migration and invasion via the p38 MAPK pathway, and also increased radiosensitivity in MDA-MB-231 cells.
CircRNAs were initially reported to interact with RBPs in the cytoplasm and to function as protein sponges, protein decoys, and protein scaffolds [36]. For instance, binding of circPABPN1 with HuR prevented HuR from binding to PABPN1, which led to lower translation of PABPN1 [37]. In this study, to evaluate whether circAAGAB bound to RBPs, ENCORI and RBPDB (http://rbpdb.ccbr.utoronto.ca/) were used to predict putative RBPs. ELAVL1, FUS, KHDRBS3, and YTHDC1 were the common predicted RBPs (Fig. 2A), and the binding possibility was calculated by RNA–Protein Interaction Prediction (RPISeq) (http://pridb.gdcb.iastate.edu/RPISeq/) (Fig. 2B). Since that KHDRBS3 had less endogenous levels in MDA-MB-231 cells (data not shown), that ELAVL1 might have less possibility of binding on circular RNAs because ELAVL1 bound to AU-rich element of mRNAs [38], and that YTHDC1, a reader of m6A [39], was not predicted in the previous version of ENCORI and was added recently, FUS was selected as the RBP candidate for validation. We discovered that FUS directly bound circAAGAB (Fig. 2D) and that knocking down FUS decreased circAAGAB stability (Fig. 2E). Recent studies have reported various mechanisms of regulating circRNA levels. For example, circRNA CDR1as/ciRS-7 was degraded by endonuclease Ago2-mediated cleavage through miR-671 recruitment [40]. m6A-modified circRNAs were shown to be broken down by endoribonucleolytic cleavage through YTHDF2 (m6A reader), HRSP12 (adaptor protein), and RNase P/MRP (endoribonucleases) [41]. On the other hand, recent emerging evidence also suggested that RBPs in the nucleus could regulate circuRNA biogenesis. For instance, QKI modulated circRNA formation via binding to specific motifs on the flanking intron of circRNAs [42]. Some studies have reported that FUS modulates circRNA biogenesis by binding to the introns flanking the back-splicing junction [43, 44]. Therefore, we conclude that FUS both regulates the biogenesis of circAAGAB in the nucleus and improves circAAGAB stability by blocking the excision site of nucleases in the cytoplasm.
Recent evidence suggested that circRNAs can sponge miRNAs to inhibit their effects on target genes, which typically results in up-regulated expression of target genes [18, 19]. In this study, miR-378 h was discovered to be down-regulated in two breast cancer cell lines overexpressing circAAGAB (Fig. 3F&G), up-regulated in hypoxic cells (Fig. 3J), and the direct binding site of miR-378 h on circAAGAB was validated by luciferase reporter assays (Fig. 3H&I). Furthermore, the target genes of miR-378 h, KIAA1522, NKX3-1, and JADE3, were validated as competing endogenous RNAs of circAAGAB, with down-regulation by overexpressing miR-378 h (Fig. 3K) and up-regulation by overexpressing circAAGAB (Fig. 3L). These findings suggested that circAAGAB could act as a sponge for miR-378 h to up-regulate the expression levels of KIAA1522, NKX3-1, and JADE3. Although KIAA1522 was shown to promote malignancy in hepatocellular carcinoma cells [45], NKX3-1 played the role of tumor suppressor in prostate cancer [46], and JADE3 was found to increase stemness in colon cancer [47], this is the first discovery of their tumor suppressor roles in breast cancer. However, whether miR-378 h can directly bind to KIAA1522, NKX3-1, and JADE3 remains unknown and needs to be verified by luciferase assays. Nevertheless, our findings suggested that circAAGAB inhibited breast cancer progression through the circAAGAB-miR-378 h-KIAA1522/NKX3-1/JADE3 axis and could serve as a novel biomarker or target for therapy.
To determine which pathway and cell functions circAAGAB was involved in in breast cancer cells, Affymetrix microarrays were performed in MDA-MB-231 cells overexpressing circAAGAB. Differentially expressed genes from the microarrays were analyzed, and potential pathways were predicted by IPA and validated by quantitative RT-PCR and western blotting. The results showed that circAAGAB up-regulated genes related to cell death and survival, such as EGR1 and ATF3 [48, 49] (Fig. 4E–G), and was implicated in the p38 MAPK signaling pathway (Fig. 4H–J). It has been reported that p38 MAPK is involved in many cellular signaling pathways that modulate tumor malignancy, such as proliferation, migration and invasion, EMT, and apoptosis [50–54], and circRNAs could also affect these cell functions in transformed cells. For example, circ-ITCH inhibited cell proliferation, colony formation, migration and invasion, and promoted cell apoptosis in bladder cancer via regulating p21 and PTEN expression [55]. In this study, our data revealed that circAAGAB could repress colony formation (Fig. 5A, B), cell migration (Fig. 5C, D), and invasion (Fig. 5E, F) in breast cancer. In accordance with this finding, vimentin (mesenchymal marker) expression was down-regulated and E-cadherin (epithelial marker) was up-regulated (Fig. 5G, H).
However, circAAGAB enhanced cell apoptosis only in MDA-MB-231 cells treated with IR (Fig. 6). Up-regulation of γH2AX (Fig. 6E, F) and caspase-3 (Fig. 6G, H) expression after IR treatment proved that double-stranded DNA was indeed broken and caused cell death. The remarkable increase of γH2AX (Fig. 6E, F) and caspase-3 (Fig. 6G, H) expression in MDA-MB-231 cells both overexpressing circAAGAB and treated with IR suggested that DNA repair capability was decreased and induced cell apoptosis. These results inferred that circAAGAB increases radiosensitivity in breast cancer, which is consistent with other evidence that circRNA may affect radiosensitivity. For example, knocking down circ_0086720 increased radiosensitivity in lung cancer by modulating the miR-375/SPIN1 axis [56].
IR can generate reactive oxygen species (ROS) and causes double-strand DNA breaks, inducing a series of DNA damage responses [57]. As shown in previous studies, double-stranded DNA damage activates ataxia telangiectasia mutated and its downstream proteins, including γH2AX, CHK2, p53, and p21. Via these proteins, activation of ATM results in ROS-triggered p38 MAPK activity and leads to cell cycle arrest or cell apoptosis [58–60]. As shown previously in this study, circAAGAB reduced cell colony formation, migration and invasion through the p38 MAPK signaling pathway without radiation treatment. It is possible that circAAGAB could activate apoptosis as part of the DNA damage response via the p38 MAPK signaling pathway. However, the downstream cellular pathways of p38 MAPK were not clear, and further experiments are warranted.
In this study, the regulatory mechanism of circAAGAB and its effects on cellular functions in MDA-MB-231 cells were determined. Nevertheless, there were some limitations in this research. For example, since MDA-MB-231 cells had the lowest endogenous expression levels and better overexpression efficiency of circAAGAB as compared to other breast cancer cell lines, in vitro cellular function assays were performed in MDA-MB-231 cells. Yet, to make the functional role of circAAGAB more convincing, the in vitro cellular function assays could be done in other breast cancer cells with high endogenous expression levels of circAAGAB, such as MCF-7 or MDA-MB-361 cells, by transfecting siRNA against circAAGAB. Furthermore, xenograft assays of MDA-MB-231 cells over-expressing circAAGAB in nude mice and examination of the expression levels of circAAGAB in clinical specimens may be other possible routes in the future.
## Conclusions
Taken together, this study revealed that a hypoxia-responsive circRNA, circAAGAB, interacted with FUS to avoid degradation, sponged miR-378 h to up-regulate KIAA1522, NKX3-1, and JADE3, restrained cell colony formation, cell migration and invasion, and increased radiosensitivity in breast cancer cells through the p38 MAPK signaling pathway.
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|
---
title: 'Mortality prediction in patients with hyperglycaemic crisis using explainable
machine learning: a prospective, multicentre study based on tertiary hospitals'
authors:
- Puguang Xie
- Cheng Yang
- Gangyi Yang
- Youzhao Jiang
- Min He
- Xiaoyan Jiang
- Yan Chen
- Liling Deng
- Min Wang
- David G. Armstrong
- Yu Ma
- Wuquan Deng
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10007769
doi: 10.1186/s13098-023-01020-1
license: CC BY 4.0
---
# Mortality prediction in patients with hyperglycaemic crisis using explainable machine learning: a prospective, multicentre study based on tertiary hospitals
## Abstract
### Background
Experiencing a hyperglycaemic crisis is associated with a short- and long-term increased risk of mortality. We aimed to develop an explainable machine learning model for predicting 3-year mortality and providing individualized risk factor assessment of patients with hyperglycaemic crisis after admission.
### Methods
Based on five representative machine learning algorithms, we trained prediction models on data from patients with hyperglycaemic crisis admitted to two tertiary hospitals between 2016 and 2020. The models were internally validated by tenfold cross-validation and externally validated using previously unseen data from two other tertiary hospitals. A SHapley Additive exPlanations algorithm was used to interpret the predictions of the best performing model, and the relative importance of the features in the model was compared with the traditional statistical test results.
### Results
A total of 337 patients with hyperglycaemic crisis were enrolled in the study, 3-year mortality was $13.6\%$ (46 patients). 257 patients were used to train the models, and 80 patients were used for model validation. The Light Gradient Boosting Machine model performed best across testing cohorts (area under the ROC curve 0.89 [$95\%$ CI 0.77–0.97]). Advanced age, higher blood glucose and blood urea nitrogen were the three most important predictors for increased mortality.
### Conclusion
The developed explainable model can provide estimates of the mortality and visual contribution of the features to the prediction for an individual patient with hyperglycaemic crisis. Advanced age, metabolic disorders, and impaired renal and cardiac function were important factors that predicted non-survival.
Trial Registration Number: ChiCTR1800015981, $\frac{2018}{05}$/04.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-023-01020-1.
## Introduction
Hyperglycaemic crisis is one of the most serious acute metabolic complications of diabetes and includes three subtypes: diabetic ketoacidosis (DKA), hyperosmolar hyperglycaemic state (HHS), and mixed syndrome (combined DKA-HHS) [1]. Inpatients with DKA or recurrent DKA are all at high risk for all-cause mortality [2]. Among diabetic patients, $10\%$ of deaths are caused by confirmed or possible DKA or coma [3]. HHS is common in elderly patients with diabetes mellitus. Despite a relatively low incidence, the mortality of hospitalized patients with HHS can reach up to 10–$20\%$ [4, 5]. Alarmingly, the 30-day mortality in patients with combined features of DKA and HHS is approximately 2.7 times higher than that in patients with isolated hyperglycaemic crisis [6]. In addition to a high risk of short-term mortality, patients with hyperglycaemic crisis episode have a higher long-term mortality after hospital discharge [1, 7]. At present, due to the poor understanding of the pathogenesis of hyperglycaemic crisis and the complexity of treatment, there is a lack of powerful indicators for evaluating the risk of mortality in patients with hyperglycaemic crisis [6]. Predicting the risk of mortality as well as providing personalized analysis of the risk factors in patients with hyperglycaemic crisis at initial diagnosis may help physicians to make correct clinical judgments and select the most appropriate strategy of treatment.
Much effort has been put into the development of prediction models to predict the risk of mortality for patients with hyperglycaemic crisis. Traditionally, linear models, such as logistic regression model and Cox proportional hazard model, have been used to develop such prediction models [8–12]. Nevertheless, modernhigh-dimensional and incomplete medical data present a challenge to traditional statistical models, and the low precision of linear models impedes patient-level use. Lacking adequate prediction tools, physicians mainly rely on subjective judgement, which is prone to errors and biases. Previous studies have applied machine learning to establish models for predicting the clinical outcomes of patients with diabetic complications and achieved promising results [13–15]. Compared with linear models, machine learning models can provide more accurate prediction results by fitting high-dimensional and nonlinear relationships in the data [16–18]. However, most of the developed prediction models were opaque and unexplainable. The improvement of model performance also brings corresponding disadvantages: these models might be perceived as black-box models due to complex computational processes, meaning that the clinician can only see the input and output of the model, and it is difficult to understand how the predictions are generated, which could reduce their acceptance among clinicians [19]. In our recent study, we developed an explainable machine learning model for predicting amputation rate in patients with diabetic foot [20]. The proposal of algorithms that could provide explanations for black-box models might increase the understanding of the model predictions and facilitate clinicians in making more accurate decisions using machine learning models [21]. There is a lack of tools to predict long-term mortality in patients with hyperglycaemic crisis. In addition, to our knowledge, no study has developed a tool to use the machine learning model for predicting mortality in patients with hyperglycaemic crisis directly interpretable. Of note, the predictions of machine learning models are expected to be transparent to enable physicians to better understand and utilize these tools.
Here, we developed a machine learning model for predicting the 3-year mortality of inpatients with hyperglycaemic crises. In addition, we utilized a tool to interpret the developed black-box machine learning model to obtain a method for individualized mortality prediction and risk factor analysis for patients with hyperglycaemic crisis.
## Study design and participants
For model development and validation, we prospectively collected data from patients with hyperglycaemic crises who were hospitalized at four university-affiliated tertiary teaching hospitals between May 2016 and May 2020. All patients were followed up until May 2021. The 3-year mortality rate was chosen as the prediction target because the study was designed to predict the long-term mortality risk of patients, and most of the patients included in the study were followed up for 3 years. The study was conducted in accordance with the Declaration of Helsinki and protocols were approved by the Ethics Committee of Chongqing University Central Hospital. Inpatients aged 18 or older diagnosed with hyperglycaemic crisis were enrolled in the study. Case definition of hyperglycaemic crisis on admission was: [1] DKA: plasma glucose ≥ 13.9 mmol/L, positive urine ketones or serum ketones, and serum bicarbonate ion concentration (HCO3−) ≤ 18 mmol/L or hydrogen ion concentration index (PH) ≤ 7.3; [2] HHS: plasma glucose ≥ 33.3 mmol/L, small urine ketones or serum ketones, HCO3− ≥ 15 mmol/L or PH ≥ 7.3, and effective serum osmolality ≥ 320 mOsm/kg; [3] combined DKA-HHS: plasma glucose ≥ 33.3 mmol/L, positive urine ketones or serum ketones, HCO3− ≤ 18 mmol/L or PH ≤ 7.3, and effective serum osmolality ≥ 320 mOsm/kg. The effective serum osmolality was calculated from the following equation: 2 × [Na+ (mEq/L)] + [plasma glucose (mmol/L)].
The blood glucose, serum sodium, serum potassium, PH, HCO3−, base excess, serum creatinine, blood urea nitrogen, cystatin C, creatine kinase, total cholesterol, low-density lipoprotein cholesterol, triglyceride, C-reactive protein, procalcitonin, creatinine kinase MB isoenzyme (CK-MB), alanine aminotransferase, and aspartate aminotransferase were measured using an automatic biochemical analyzer (AU5821, Beckman Coulter, USA). Hemoglobin A1c was measured by high-performance liquid chromatography (BC-6800, Mindray, Shenzhen, China). Cardiac troponin I was measured by chemiluminescence immunoassay, using DXI800 (Beckman Coulter, USA).β-hydroxybutyrate was measured using colorimetric enzymatic reaction (D-3-hydroxybutyrate kit, Ranbut, Randox Laboratories). White blood cells, neutrophils, lymphocyte, and platelet were determined by an automatic blood cell analyzer (BC-6800, Mindray, Shenzhen, China).
There were 41 input variables incorporated into this study to determine the prediction models, including demographic features, clinical and at-admission laboratory data, and comorbidities. The data were collected by reviewing electronic medical records (EHRs). 9 patients with missing values of variables greater than $30\%$ and 14 patients who were lost to follow-up were excluded. The participants were divided into two groups: data from two tertiary teaching hospitals were used as the training set for model training, and data from the two other tertiary teaching hospitals were used as the test set for external validation.
## Statistical analyses
Statistical analysis was applied among the groups. Continuous variables are presented as the mean ± standard deviation when data followed a normal distribution or as the median and interquartile range when the continuous variables were not found to be normal, and categorical variables were expressed as numbers (%). To evaluate significant differences between groups, the t test, Kruskal‒Wallis test, and chi-squared test were used for normal continuous, nonnormal continuous, and categorical variables, respectively. A P value < 0.05 was considered statistically significant.
## Model development
The data were preprocessed before training the prediction model. Box diagrams was used to eliminate outliers from the raw dataset, the k-nearest neighbour algorithm was used for filling in the missing values, and the Z Score was utilized to normalize continuous variables.. To construct the prediction model, five algorithms were used: logical regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and deep neural networks (DNNs). The DNN architecture used in this study was a feed-forward neural network. LR and SVM are classical machine learning algorithms commonly used in previous studies [22, 23]. RF and LightGBM are powerful algorithms in the machine learning area and are currently considered state-of-the-art algorithms for prediction with tabular data [24–26]. DNNs belong to an important branch of machine learning algorithms, achieving excellent performance in many fields, such as pattern recognition and natural language processing [18, 27]. Therefore, the above five algorithms were used for model building. Afterwards, tenfold cross-validation and Bayesian hyperparameter optimization were utilized for model training, internal validation and hyperparameter tuning. *The* generalization capacity of hyperparameter combinations was improved by tenfold cross-validation, and the efficiency of finding the optimal hyperparameter combination was improved by Bayesian hyperparameter optimization. Additional details on hyperparameter setting and model architecture are provided in Additional file 1: Fig. S1 and Tables S1–S6.
After model training was performed, the prediction ability of the models was evaluated and compared in the test set according to the evaluation metrics, including area under the receiver-operating-characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Confidence intervals (CIs) were obtained by resampling the test set 1000 times (bootstrapping) and averaging the performance.
The best performing model was selected as the prediction model, and Platt scaling was further applied to calibrate the predicted probability of the best performing model to make it close to the observed probabilities. The test set was further divided in a 1:1 ratio into a calibration set for model calibration and a new test set for evaluating the performance of model calibration. The calibration curve and Brier score were used to assess the coherence between the predicted and observed probabilities of the prediction model.
## Model explanation
The Shapley additive explanations (SHAP) algorithm was applied to the calibrated model to obtain explanations of the predictions of the model. The SHAP algorithm is one of the most popular model-agnostic algorithms for interpreting black-box model predictions [28, 29]. SHAP values were obtained by the SHAP algorithm, which provides interpretation of individual predictions. A SHAP value represents, given a set of feature values, how much a single feature value influences the difference between the actual prediction and the average prediction in its interaction with other feature values. Therefore, the mean prediction of the model plus the sum of the SHAP values for all features are consistent with the predicted result. Importantly, the SHAP value for a feature is not isolated but obtained by interacting with other features, which makes it different from the feature weight in the traditional generalized linear model.
To verify the rationality of the interpretation of the model predictions acquired by the SHAP algorithm, we first utilized the SHAP algorithm to obtain and visualize the overall effect of features on the predictions, that is, the contribution and relative importance ranking of each feature to the predictions. For further validation and comparison, we divided the patients in the training set into a survival group and a nonsurvival group according to their clinical outcomes and analysed the differences in features between the groups by statistical methods. In addition, based on proving the rationality of the explanations obtained by the SHAP algorithm, we further linearly mapped SHAP values to the probability of increased or decreased mortality and proposed a personalized mortality risk factor analysis method specific to patients with hyperglycaemic crisis, which visualized the contribution of each feature to the prediction in probability.
## Results
For model development, 257 patients with hyperglycaemic crisis from two hospitals were enrolled. The baseline characteristics of these patients are depicted in Table 1. In the training set, the median age was 56 years (IQR 40.3–70.0), and 152 ($59.1\%$) were male. Death occurred in 31 ($12.1\%$) patients within the study period. To evaluate the external validity, the models were applied in the external test set, comprising 80 patients with hyperglycaemic crisis from two hospitals that were independent of the training set. In the test set, the receiver operating characteristic curve and AUC of the five models are shown in Fig. 1A (AUC = 1 indicates perfect prediction; AUC = 0 indicates random prediction). The other five evaluation metrics, including accuracy, sensitivity, specificity, NPV and PPV, for the five models are presented in Table 2. Overall, the findings demonstrated that the LightGBM model performed best among the five prediction models, with an AUC of 0.89 ($95\%$ CI 0.77, 0.97). The corresponding accuracy was 0.83 (0.74, 0.90), sensitivity was 0.74 (0.47, 0.94), specificity was 0.85 (0.76, 0.93), PPV was 0.52 (0.31, 0.74), and NPV was 0.94 (0.87, 0. 99). Therefore, the LightGBM model was selected as the best predictive model. The prediction probability of the LightGBM model was calibrated to make it close to the observed probability. The calibration plot indicated good agreement between the predicted and observed probabilities of the LightGBM model with a curve close to the 45° line, and the Brier score was 0.10 (0.05, 0.17) (Fig. 1B).Table 1Baseline characteristics of patients with hyperglycaemic crisis in the training set and test setVariablesTraining set($$n = 257$$)Test set($$n = 80$$)Demographic dataAge, years56.0 (40.3, 70.0)51.0 (35.0, 61.0)Sex, % Male152 (59.1)46 (57.5) Female105 (40.9)34 (42.5)Body mass index, kg/m222.7 (20.1, 25.0)22.8 (20.6, 26.1)Diabetes type, % Type 137 (14.4)14 (17.5) Type 2220 (85.6)66 (82.5)Clinical and laboratory dataBlood glucose, mmol/L33.1 (21.6, 33.6)27.3 (20.6, 40.2)β-hydroxybutyrate, mmol/L3.80 (1.50, 6.00)5.60 (3.40, 6.85)Hemoglobin A1c, %11.6 (10.0, 13.5)12.8 (11.0, 14.3)Triglyceride, mmol/L1.95 (1.33, 3.02)1.62 (1.10, 3.30)Total cholesterol, mmol/L4.80 (3.80, 6.27)4.50 (3.53, 5.51)LDL-C, mmol/L2.57 (1.78, 3.37)2.53 (1.66, 3.19)Serum creatinine, umol/L82.0 (58.0, 144.1)79.5 (57.6, 120.8)*Blood urea* nitrogen, mmol/L7.86 (5.40, 15.40)7.81 (5.15, 16.10)Cystatin C, mg/L1.16 (0.71, 2.20)0.74 (0.61, 1.21)Creatine kinase, U/L95.0 (58.0, 208.0)83.0 (46.6, 123.6)Cardiac troponin I, μg/L0.01 (0.00, 0.04)0.01 (0.01, 0.03)CKMB, U/L14.0 (8.3, 20.8)16.6 (11.2, 25.6)Alanine aminotransferase, IU/L19.0 (13.0, 30.0)18.0 (12.1, 29.6)Aspartate aminotransferase, IU/L18.0 (13.0, 28.3)17.8 (13.2, 26.8)C-reactive protein, mmol/L9.10 (4.20, 55.66)4.56 (0.66, 14.30)Procalcitonin, ng/ml0.47 (0.08, 2.25)0.15 (0.10, 2.20)White blood cells, × 109/L10.6 (6.8, 14.8)7.24 (10.89, 15.71)Percentage of neutrophils, %84.0 (74.0, 90.0)86.5 (78.6, 91.4)Lymphocyte, × 109/L1.06 (0.62, 1.59)1.23 (0.76, 1.91)Platelet, × 109/L196.0 (153.0, 259.0)214.5 (169.8, 284.3)Serum sodium, mmol/L142.3 (135.2, 149.5)136.5 (133.0, 142.0)Serum potassium, mmol/L4.03 (3.66, 4.66)4.15 (3.64, 4.92)Serum chloride, mmol/L100.1 (95.8, 106.0)104.5 (97.8, 112.0)PH7.31 (7.22, 7.38)7.30 (7.19, 7.36)Base excess, mmol/L− 7.30 (− 15.70, − 2.70)− 9.90 (− 17.83, − 6.55)HCO3−, mmol/L14.9 (9.2, 18.0)14.5 (8.3, 16.9)*Effective serum* osmolality, mOsm/kg314.0 (293.0, 335.5)299.0 (289.0, 327.2)Medical historyInfection, n (%)147 (57.2)30 (37.5)Septic shock, n (%)7 (2.7)0 (0.0)Hypertension, n (%)66 (25.7)16 (20.0)Coronary heart disease, n (%)29 (11.3)8 (10.0)Heart failure, n (%)12 (4.7)0 (0.0)Cerebral infarction, n (%)44 (17.1)4 (5.0)Dementia, n (%)5 (1.9)0 (0.0)Diabetic nephropathy, n (%)43 (16.7)11 (13.8)Acute kidney injury, n (%)8 (3.1)6 (7.5)Tumor, n (%)3 (1.2)3 (3.8)Death, n (%)31 (12.1)15 (18.8)LDL-C low-density lipoprotein cholesterol, CKMB creatinine kinases MB isoenzyme, PH hydrogen ion concentration index, HCO3− serum bicarbonate ion concentrationFig. 1Discrimination and calibration performance of the models. A Receiver operating characteristic curves for the LR, SVM, RF, LightGBM, and DNN models. B Calibration curve for the LightGBM modelTable 2The values of the evaluation metrics of the models in the test setAUCAccuracySensitivitySpecificityPPVNPVLR0.64 (0.47, 0.79)0.76 (0.69, 0.85)0.20 (0.00, 0.44)0.89 (0.81, 0.96)0.29 (0.00, 0.60)0.83 (0.74, 0.92)SVM0.70 (0.52, 0.86)0.76 (0.65, 0.85)0.47 (0.22, 0.73)0.83 (0.73, 0.91)0.39 (0.18, 0.63)0.87 (0.77, 0.95)RF0.87 (0.78, 0.95)0.80 (0.70, 0.88)0.67 (0.42, 0.91)0.83 (0.73, 0.91)0.48 (0.28, 0.69)0.92 (0.85, 0.98)LightGBM0.89 (0.77, 0.97)0.83 (0.74, 0.90)0.74 (0.47, 0.94)0.85 (0.76, 0.93)0.52 (0.31, 0.74)0.94 (0.87, 0. 99)DNN0.64 (0.54, 0.87)0.81 (0.73, 0.89)0.26 (0.06, 0.53)0.94 (0.88, 0.99)0.50 (0.11, 0.88)0.85 (0.76, 0.92)LR logical regression, SVM support vector machine, RF random forest, LightGBM light gradient boosting machine, DNN deep neural network algorithm, NPV negative predictive value, PPV positive predictive value The contribution of each of the 41 features in the calibrated LightGBM model is shown in Fig. 2. The features were ranked by their relative importance to mortality prediction according to the SHAP values of the model predictions. It is not surprising that age was ranked as the most important feature for the prediction model, followed by blood glucose and blood urea nitrogen. In addition, taking the effect of age on the prediction as an example, older age was associated with a higher risk of death, and younger age drives the predictions towards survival. A similar explanation can be applied to other features, and most of the interpretation of features was consistent with clinical experience and previous evidence. Of note, features can drive the prediction in either direction (increase or decrease mortality prediction) in our explainable prediction model, which is different from the previous mortality risk scoring system based on a generalized linear model in which features can only drive mortality prediction in a single direction. As shown in Table 3, the results of statistical analysis revealed that the 9 most important features for the LightGBM model were significantly different between the survival group and the nonsurvival group in the training set ($P \leq 0.05$). Compared to the survival group, age, blood glucose, serum creatinine, blood urea nitrogen, cystatin C, effective serum osmolality, CK-MB, alanine aminotransferase, serum sodium, PH, HCO3 − and cardiac troponin I were significantly higher in the nonsurvival group ($P \leq 0.05$). However, hemoglobin A1c level was surprisingly significantly lower in the nonsurvival group than survival group ($P \leq 0.05$). An increasing trend of β-hydroxybutyrate was unexpectedly indicated in the survival group ($$P \leq 0.045$$). Therefore, the traditional statistical test results and the model interpretation results corroborated each other, which proved the rationality and accuracy of the interpretation of features acquired by the SHAP algorithm. Based on this evidence, we mapped SHAP values and proposed a personalized risk factor analysis tool for explaining the mortality prediction for a particular patient with hyperglycaemic crisis, which is a scale from 0 to 1, visualizing the contribution of each feature to the prediction in probability. We showed the application of the personalized risk factor analysis method in one deceased and one surviving patient with hyperglycaemic crisis during the follow-up period in the test set (Fig. 3). In the case of the deceased patient, the patient was an 88-year-old female with a history of septic shock and acute kidney injury. The model predicted that the risk of mortality of the patients was 0.623. Advanced age (88 years) drove a 0.58 increase in the risk of mortality, while relatively low hemoglobin A1c reduced the risk of mortality by 0.32. A similar explanation can be applied to other features. The prediction was driven by 41 features used for model training. The sum of the SHAP values for all features plus the baseline risk equals the predicted risk of mortality. The baseline risk (E[f(X)]) was obtained by calculating the average predicted risk of mortality among all patients in the training set. Thus, SHAP algorithm made our model explainable both in terms of the relative importance of individual features for survival of patient with hyperglycaemic crisis and those at patient level. Fig. 2The impact of the input features on predictions. Each dot represents the effect of a feature on the prediction for one patient. The redder the colour of the dots, the higher the value of the features, and the bluer the colour of the dots, the lower the value of the features. Dots to the left x-axis represent patients with values of the features decreasing mortality prediction, and dots to the right x-axis represent patients with values of the features increasing mortality predictionTable 3Baseline characteristics of patients with hyperglycaemic crisis in the training set by clinical outcomesVariablesSurvival group($$n = 226$$)Nonsurvival group($$n = 31$$)Demographic dataAge, years54.5 (37.0, 67.3)79.5 (65.0, 86.3) < 0.001Sex, %0.194 Male137 (60.6)15 (48.4) Female89 (39.4)16 (51.6)Body mass index, kg/m222.8 (20.2, 25.0)17.5 (20.7, 25.0)0.076Diabetes type, %0.015 Type 137 (16.4)0 (0.0) Type 2189 (83.6)31 (100.0)Clinical and laboratory dataBlood glucose, mmol/L30.1 (20.2, 33.3)33.3 (33.3, 38.4) < 0.001β-hydroxybutyrate, mmol/L4.13 (1.51, 6.20)3.05 (0.48, 4.60)0.045Hemoglobin A1c, %11.8 (10.2, 13.6)10.6 (8.6, 12.5)0.038Triglyceride, mmol/L1.89 (1.31, 3.11)2.10 (1.40, 2.71)0.386Total cholesterol, mmol/L4.80 (3.78, 6.26)4.39 (3.80, 6.30)0.675LDL-C, mmol/L2.52 (1.73, 3.35)2.86 (2.01, 3.74)0.159Serum creatinine, umol/L75.5 (56.8, 131.3)165.0 (87.2, 334.8) < 0.001Blood urea nitrogen, mmol/L7.50 (5.18, 13.31)19.5 (11.0, 28.2) < 0.001Cystatin C, mg/L1.10 (0.69, 1.88)1.88 (1.28, 3.64) < 0.001Creatine kinase, U/L50.0 (25.0, 85.1)32.8 (5.8, 139.8)0.306Cardiac troponin I, μg/L58.5 (30.3, 109.0)44.0 (20.5, 177.8)0.477CKMB, U/L87.0 (55.0, 187.0)150.0 (76.5, 571.3)0.008Alanine aminotransferase, IU/L0.01 (0.00, 0.03)0.04 (0.00, 0.10)0.021Aspartate aminotransferase, IU/L18.0 (12.0, 28.0)19.0 (14.0, 31.0)0.200C-reactive protein, mmol/L7.90 (3.81, 47.45)19.1 (4.8, 145.0)0.048Procalcitonin, ng/ml0.40 (0.05, 2.14)0.71 (0.11, 6.80)0.063White blood cells, × 109/L10.5 (6.7, 14.6)12.7 (7.1, 17.8)0.271Percentage of neutrophils, %83.7 (73.9, 89.7)84.1 (76.7, 93.4)0.136Lymphocyte, × 109/L1.08 (0.63, 1.60)1.00 (0.54, 1.50)0.513Platelet, × 109/L202.0 (153.0, 261.5)179.0 (133.5, 207.3)0.085Serum sodium, mmol/L140.3 (134.7, 148.2)149.0 (142.5, 159.4)0.001Serum potassium, mmol/L4.03 (3.68, 4.66)4.02 (3.62, 4.75)0.759Serum chloride, mmol/L99.9 (95.6, 105.5)102.5 (96.8, 111.7)0.220PH7.30 (7.22, 7.37)7.38 (7.25, 7.41)0.030Base excess, mmol/L− 7.50 (− 2.70, − 16.43)− 7.00 (− 2.50, − 12.00)0.682HCO3−, mmol/L14.6 (7.8, 18.0)17.6 (13.8, 21.2)0.031Effective serum osmolality, mOsm/kg310.2 (292.0, 330.3)327.0 (322.0, 353.0) < 0.001Medical historyInfection, n (%)122 (54.0)25 (80.6)0.005Septic shock, n (%)5 (2.2)2 (6.5)0.174Hypertension, n (%)57 (25.2)9 (29.0)0.649Coronary heart disease, n (%)24 (10.6)5 (16.1)0.363Heart failure, n (%)10 (4.4)2 (6.5)0.616Cerebral infarction, n (%)32 (14.2)12 (38.7)0.001Dementia, n (%)3 (1.3)2 (6.5)0.053Diabetic nephropathy, n (%)34 (15.0)9 (29.0)0.050Acute kidney injury, n (%)6 (2.7)2 (6.5)0.254Tumor, n (%)3 (1.3)0 (0.0)0.519LDL-C low-density lipoprotein cholesterol, CKMB creatinine kinases MB isoenzyme, PH hydrogen ion concentration index, HCO3− serum bicarbonate ion concentration. P value < 0.05 was considered statistically significantFig. 3Examples of personalized risk factors. A An example of personalized risk factor analysis for a patient in the test set (clinical outcome was death). B An example of personalized risk factor analysis for a patient in the test set (actual clinical outcome was survival)
## Discussion
Experiencing a hyperglycaemic crisis is associated with a short- and long-term increased risk of mortality [1, 6, 7]. However, due to the complex pathogenesis of hyperglycaemic crisis, available international guidelines for the diagnosis and treatment of hyperglycaemic crisis are not consistent [4, 30]. In addition, there is a lack of strong indicators to assess the risk of mortality in patients with hyperglycaemic crisis. Therefore, the development of more effective methods to predict the risk of mortality, create individualized risk and benefit evaluations in patients with hyperglycaemic crises at initial diagnosis, which are particularly important to identify the best therapeutic strategies and improve the prognosis.
Here, we developed an explainable risk prediction model providing predictions and individualized risk factor assessment of the 3-year mortality of patients with hyperglycaemic crisis after admission. In the model building process, we selected five representative machine learning algorithms, including LR, SVM, RF, LightGBM, and DNN, to obtain the best prediction model. The LightGBM model performed the best of the five models evaluated in an external test set, with an AUC of 0.89. We further calibrated the LightGBM model to obtain a more reliable model. The SHAP algorithm was used to interpret the calibrated LightGBM model to obtain how each feature drives the prediction of the model. On the basis of verifying the effectiveness of the analytical method by comparing with the statistical test results, we further proposed a personalized mortality risk factor assessment method specific to patients with hyperglycaemic crisis. In the interpretation obtained by SHAP algorithm, the influence of each feature on the predictions is not isolated, but interacts with other features, which is related to the calculation method of SHAP value, and makes it different from the feature weight in the traditional generalized linear model. Thus, the developed explainable model can not only predict mortality but also provide a personalized risk factor assessment tool. Such an explainable model is a more useful tool than scoring systems based on generalized linear models that are currently implemented.
Most of the prediction tools constructed in past studies are based on generalized linear models, such as logistic regression models and Cox proportional hazard models [10, 11, 31]. However, the rapid development of information technology brings high-dimensional and nonlinear data, which challenges the traditional generalized linear model. Machine learning provides a powerful and novel method to extract information from complex medical data and develop more accurate predictions. That is, we can only obtain the input of the model and the output of the predictions. It is difficult to understand the details of how machine learning models analyse data and make decisions, which limited the application of the models at the individual level. A representative score called PHD was developed based on a generalized linear model by Huang et al. [ 10], which could be used to predict 30-day mortality risk and classify risk and disposition in patients with hyperglycaemic crisis. Since the variables we selected were different from the PHD score, the model we developed predicted the 3-year mortality of patients with hyperglycaemic crisis after admission. Therefore, we could not directly compare our model with the PHD score. However, an external validation study revealed that the AUC of the PHD score ranged from 0.357 to 0.727 [9]. In comparison, the AUC of the models developed in this study ranged from 0.63 to 0.89 in an external validation dataset. In addition, the developed LightGBM model also outperformed the conventional logistic regression model constructed in our study in the external test data (Fig. 1A, AUC of 0.89 vs. 0.63). We thus consider our model superior to traditional methods.
In addition, we used the SHAP algorithm to explain the black-box model to quantify and visualize the features that drive the predictions so that it not only had better prediction ability but also had transparency similar to that of the simple linear model. The tools established in this study combined the advantages of the complex machine learning model and simple linear model, solving the problems of insufficient prediction ability of the generalized linear model and black box nature of the machine learning model. The model we developed provided explanations of the risk factors that drive the model prediction, both in terms of the importance of individual features to the overall mortality prediction and contribution at the patient level. A comprehensible model allows clinicians to combine the predictions with their expertise to facilitate decision-making and assist clinicians in interventions [32, 33].
The effect of most features on the prediction is consistent with clinical experience and previous evidence. For example, advanced age, metabolic disorders, and impaired renal and cardiac function can predict for nonsurvival. Advanced age was the most important risk factor for mortality. There is substantial evidence that the physical function and resistance of patients decrease with age, which is more likely to increase the risk of mortality [12, 34, 35]. Severe metabolic disorders (elevated levels of blood glucose, effective serum osmolality, and serum sodium) may lead to confusion and even coma, which is associated with an increased risk of mortality [5, 36]. Likewise, there is consistent evidence that impaired renal (elevated levels of blood urea nitrogen, cystatin C, and serum creatinine,) and cardiac function (elevated levels of cardiac troponin I) increase the risk of mortality [1, 34, 37–39]. In addition, reduced levels of HbA1c drove the prediction towards nonsurvival. The effect of HbA1c on the prediction did not seem to live up to expectations. One reason for this counterintuitive issue might be that patients in the survival group had significantly better renal function than those in the nonsurvival group, and there is evidence that patients with chronic renal failure generally had a lower red blood cell (RBC) survival rate [40]. In addition, after treatment with erythropoietin, the newly generated RBCs lead to a further decrease in HbA1c [41]. An increasing trend of β-hydroxybutyrate was unexpectedly found in the survival group. It seems that it is a protective factor for patients with hyperglycaemic crisis. Previous studies evidence supports that blood β-hydroxybutyrate can reduce renal ischemia and reperfusion injury by increasing the upstream regulator forkhead transcription factor O3 and reducing caspase-1 and pro-inflammatory cytokines, thereby reducing cell death [42, 43].
Age was ranked as the most important feature for the model, followed by features related to metabolic disorders, cardiac and renal dysfunction. In a recent study, acute hyperglycaemic crisis episode impact on survival in individuals with diabetic foot ulcer using a machine learning approach, which also revealed that individual characteristics evaluated by Charlson Comorbidity Index (CCI) and acute organ injury played a vital role in disease prognosis [44]. The nine most important features for the prediction were significantly different between the survival group and nonsurvival group in the training set. Therefore, the effect of features on the predictions is consistent with the traditional statistical test results. Importantly, the developed explainable model can provide the relative importance of individual features for survival of patient and those at patient level, which makes it superior to traditional statistical tests that can only test for significant differences between groups. Admittedly, there are some limitations in our study. First, although multicentre data were used, due to the low morbidity of hyperglycaemic crisis, the amount of data was relatively small, which may lead to bias in the model. Second, in order to enable the models to obtain more comprehensive information and improve the performance of the tree-based models, our models contained up to 41 features. However, due to the limitation of data acquisition, the number of variables selected for the study is limited.. Third, the SHAP algorithm cannot address model bias, and the influence of features on the predictions is not equal to the association in the causal chain. Finally, Although the model is explainable, some features, such as age, cannot be manipulated by physicians. However, these insights into the relationship between features and predictions may guide our search for causality.
## Conclusions
In summary, we developed an explainable machine learning model for predicting 3-year mortality and providing individualized risk factor assessment of inpatients with hyperglycaemic crises as well as hospital discharge, and the model was externally validated in an independent dataset. The interpretation results of the model revealed that more attention should be given to the variables related to metabolism and renal and cardiac function in the treatment of hyperglycaemic crisis, which played an important role in mortality through the model prediction. Transparent and explainable model predictions would help gain the trust of clinicians and facilitate decision-making by allowing physicians to evaluate whether the decision-making process of the model is consistent with scientific evidence and clinical experience. However, before this kind of tool used in the clinic, prospective studies are needed to be verified in the future.
## Supplementary Information
Additional file 1: Table S1. The hyperparameter optimization range and results of LR. Table S2. The hyperparameter optimization range and results of SVM. Table S3. The hyperparameter optimization range and results of RF. Table S4. The hyperparameter optimization range and results of LightGBM. Figure S1. Schematic diagram of DNN network structure. Table S5. The hyperparameter of the network structure of DNN. Table S6. The hyperparameter optimization range and results of DNN.
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|
---
title: 'Total knee arthroplasty and bariatric surgery: change in BMI and risk of revision
depending on sequence of surgery'
authors:
- Perna Ighani Arani
- Per Wretenberg
- Erik Stenberg
- Johan Ottosson
- Annette W-Dahl
journal: BMC Surgery
year: 2023
pmcid: PMC10007771
doi: 10.1186/s12893-023-01951-6
license: CC BY 4.0
---
# Total knee arthroplasty and bariatric surgery: change in BMI and risk of revision depending on sequence of surgery
## Abstract
### Background
Patients with obesity have a higher risk of complications after total knee arthroplasty (TKA). We investigated the change in weight 1 and 2 years post-Bariatric Surgery (BS) in patients that had undergone both TKA and BS as well as the risk of revision after TKA based on if BS was performed before or after the TKA.
### Methods
Patients who had undergone BS within 2 years before or after TKA were identified from the Scandinavian Obesity Surgery Register (SOReg) and the Swedish Knee Arthroplasty Register (SKAR) between 2007 and 2019 and 2009 and 2020, respectively. The cohort was divided into two groups; patients who underwent TKA before BS (TKA-BS) and patients who underwent BS before TKA (BS-TKA). Multilinear regression analysis and a Cox proportional hazards model were used to analyze weight change after BS and the risk of revision after TKA.
### Results
Of the 584 patients included in the study, 119 patients underwent TKA before BS and 465 underwent BS before TKA. No association was detected between the sequence of surgery and total weight loss 1 and 2 years post-BS, − 0.1 ($95\%$ confidence interval (CI), − 1.7 to 1.5) and − 1.2 ($95\%$ CI, − 5.2 to 2.9), or the risk of revision after TKA [hazard ratio 1.54 ($95\%$ CI 0.5–4.5)].
### Conclusion
The sequence of surgery in patients undergoing both BS and TKA does not appear to be associated with weight loss after BS or the risk of revision after TKA.
## Background
Obesity is a global epidemic [1] and the prevalence has increased extensively over the past decades [2]. Obesity is also associated with dramatically increased morbidity and mortality [3] and is one of the most prominent risk factors for developing osteoarthritis (OA) [4]. The most effective method to counteract severe obesity, with its related comorbidities, is bariatric surgery (BS) [3].
Previous studies have described an increased overall risk of revision after total knee arthroplasty (TKA) in obese patients [5–7].
Both obesity and knee OA are prevalent conditions and as obesity rates continue to rise, the risk of developing knee OA also increases. As such, BS and TKA are important and potentially life-changing interventions, but it is important to determine the best sequence of action when both interventions are indicated. Given the difficulty of managing obesity, it would be beneficial to determine whether undergoing TKA prior to BS could aid in weight loss. Potentially, TKA prior to BS may facilitate improved postoperative rehabilitation and physical activity, thereby contributing to superior weight outcomes for patients. Additionally, performing BS prior to TKA could be hypothesized to reduce the risk of revision after TKA, given that obesity is associated with an increased risk of revision. Therefore, the aim of this study is to investigate if the sequence of surgery affects weight loss, measured 1 and 2 years post-BS, as well as the risk for revision after TKA.
## Methods
The Scandinavian Obesity Surgery Register (SOReg) was used to identify patients who underwent BS, defined as gastric bypass or sleeve gastrectomy, between 2007 and 2019. These patients were linked to the Swedish Knee Arthroplasty Register (SKAR) using patients' personal identification number (PIN), which are unique to every citizen in Sweden. Patients were eligible for inclusion if they had undergone a primary TKA due to OA between 2009–2020 and BS within 2 years before (BS-TKA) or after their TKA (TKA-BS).
Body Mass Index (BMI), age, the American Society of Anesthesiologists (ASA) classification at the time of TKA, information pertaining to later revisions, date of death, and emigration status were obtained from SKAR. BMI (height and weight), age, sex, type of surgery, and weight 1 and 2 years post-BS were obtained from SOReg. Patients with a missing BMI prior to BS or TKA were excluded. In patients who had staged bilateral TKA, with both being performed prior to the BS, the second TKA was included. In patients who had undergone staged bilateral TKA with both TKAs being performed after the BS, the first TKA was included.
When analyzing the change in weight, patients who had undergone TKA on both knees, where one was performed before the BS and the other one was performed after the BS, were only included in the BS-TKA group, as their knee OA could not be considered definitively treated until the second TKA was performed. When analyzing the risk of revision, both TKAs were included.
The outcome measures for this study were weight change after BS and revision after TKA. Weight change, assessed 1 and 2 years post-BS, was evaluated using the following parameters: change in BMI, total weight loss (TWL), and excess BMI loss (EBMIL). Additionally, revision was defined as a surgical procedure performed for any reason on an already resurfaced knee, where one or more of the components are exchanged, removed, or added, including arthrodesis and amputation.
## Statistics
The patients were divided into two groups depending on if they underwent TKA before or after BS: TKA-BS and BS-TKA. When comparing demographics and clinical characteristics between the cohorts; categorical variables were reported as counts and percentages while continuous variables were reported as means and standard deviations (SDs) or medians and interquartile ranges (IQRs). To evaluate the statistical significance of differences between the groups, Pearson’s Chi-squared test was used for categorical variables. For continuous variables, the Student’s t-test was used for normally distributed data, otherwise the Mann–Whitney U-test was applied.
The outcome measures of interest were weight change 1 and 2 years post-BS and revision after the primary TKA. In order to adjust for potential confounding, multilinear regression analysis was employed to determine the change in BMI, TWL (%), and EBMIL (%), based on the sequence of surgery. The regression models were adjusted for type of BS, sex, age and BMI at the time of BS. A Cox proportional hazards model was used to estimate the risk of revision for any reason and adjusting for sex, age, and BMI. Age and BMI at the time for the primary TKA were used in the adjustment in the Cox proportional hazards model.
Results of the multilinear regression models were reported as the average change in BMI, TQL, and EBMIL while the results of the Cox proportional hazards model were reported using a hazard ratio (HR). All values were presented with corresponding $95\%$ confidence intervals (CI). Statistical significance was defined as a two-sided p value of less than 0.05. All analyses were performed using Statistical Package for the Social Sciences.
## Results
Of the 570 patients included in the analyses investigating the change in weight, 105 patients had undergone TKA for OA prior to BS and were included in the TKA-BS group while 465 patients underwent TKA for OA following BS and were included in the BS-TKA group (Fig. 1). The majority of the patients were women in both groups and the patients in the BS-TKA group were on average 2 years younger (Table 1). The median time between TKA and BS was 13 months in both groups. Fig. 1Flow chart of the study populationTable 1Patient characteristics at BSTKA-BSN = 105BS-TKAN = 465p valueAge in years, mean (SD)57 (5.8)55 (6.8)0.015Sex, n (%)0.807 Female80 ($76\%$)349 ($75\%$) Male25 ($24\%$)116 ($25\%$)BMI, mean (SD)40.5 (4.5)43.1 (4.9) < 0.001N number; SD standard deviation; BMI Body Mass Index; TKA total knee arthroplasty; BS bariatric surgery No statistically significant differences were detected in TWL or EBMIL 1 year post-BS, while an increased BMI-loss was observed in the BS-TKA group compared to the TKA-BS group [BMI-loss of 12.5 and 11.3 (Beta 1.3, $95\%$ CI 0.4–2.1)]. However, the difference was no longer significant when adjusting for the potential confounders (Beta − 0.06, $95\%$ CI − 0.8 to 0.6). 2 years post-BS, no statistically significant difference was found in BMI-loss, TWL or EBMIL (Table 2).Table 2Weight change after bariatric surgery depending on sequence of surgeryBS-TKATKA-BSUnivariate regressionAdjusted regressionNMean ± SDNMean ± SDB ($95\%$ CI)p-valueB1 ($95\%$ CI)p-value1 year BMI-loss43112.5 ± 3.99611.3 ± 3.31.257 (0.414 to 2.1)0.004− 0.059 (− 0.760 to 0.642)0.868 TWL, %43128.7 ± 7.59627.6 ± 7.01.2 (− 0.5 to 2.8)0.170− 0.1 (− 1.7 to 1.5)0.873 EBMIL, %43170.4 ± 19.49574.6 ± 19.8− 4.2 (− 8.5 to 0.1)0.058− 1.2 (− 5.2 to 2.9)0.5702 years BMI-loss35112.4 ± 4.47511.4 ± 3.90.994 (− 0.095 to 2.082)0.073− 0.152 (− 1.038 to 0.734)0.737 TWL, %35128.8 ± 8.97528.0 ± 8.40.7 (− 1.5 to 2.9)0.522− 0.4 (− 2.5 to 1.6)0.697 EBMIL, %35171.3 ± 22.47476.0 ± 23.7− 4.7 (− 10.4 to 1.0)0.103− 2.3 (− 7.5 to 3.0)0.399N number; SD standard deviation; CI confidence interval; BMI Body Mass Index; TWL total weight loss; EBMIL excess BMI-loss; TKA total knee arthroplasty; BS bariatric surgery1 = Based on linear regression model, adjusted for age at bariatric surgery, sex, preoperative BMI and surgical method; Beta value for BS first compared to TKA first The median weight change between TKA and BS in the TKA-BS group was a 5 kg (IQR 2–10) gain in weight and the median change in TWL was a $4.2\%$ (IQR 1.6–9.7) increase in TWL. The mean weight change between BS and TKA in the BS-TKA group was a 33 kg (SD 13) loss in weight and the mean change in TWL was a $27\%$ (SD 8) decrease in TWL.
When analyzing the risk of revision, 119 patients were included in the TKA-BS group and 465 patients were included in the BS-TKA group (Table 3). The median follow-up time was 39 months in the TKA-BS group and 24 months in the BS-TKA group. Five patients in the TKA-BS group ($4.2\%$) underwent a TKA revision, while 26 patients in the BS-TKA group ($5.6\%$) underwent a revision. No statistically significant difference in the risk of revision was detected when comparing the cohorts [HR 1.5 ($95\%$ CI 0.5–4.5)] (Table 4).Table 3Patient characteristics at TKA surgeryTKA-BSN = 119BS-TKAN = 465p valueAge in years, mean (SD)56 (5.7)57 (6.8)0.516Sex, n (%)0.749 Female91 ($76\%$)349 ($75\%$) Male28 ($24\%$)116 ($25\%$)ASA-classification, n (%)0.12 110 ($8\%$)55 ($12\%$) 274 ($62\%$)312 ($67\%$) ≥ 335 ($30\%$)98 ($21\%$)BMI, mean (SD)38 (4.6)31 (4.4) < 0.001N number; SD standard deviation; TKA total knee arthroplasty; BS Bariatric surgery; ASA American Society of Anaesthesiologists; BMI Body Mass IndexTable 4Adjusted HR for the risk of revision after primary TKANHR ($95\%$ CI)p-valueTKA-BS119ReferenceReferenceBS-TKA4651.540 (0.526 to 4.509)0.430HR hazard ratio; N number; CI confidence interval; TKA total knee arthroplasty; BS bariatric surgery
## Discussion
This is the first study evaluating weight change after BS in patients who have undergone TKA either before or after BS. Furthermore, this is the largest study evaluating the association between the sequence of surgery and the risk of revision after TKA. The analyses were not able to identify an association between the sequence of surgery and weight change up to 2 years post-BS. Moreover, the sequence of surgery did not appear to be related to the risk of revision following TKA.
Previous studies investigating weight change after TKA have presented varying results [8–10]. Teichtal et al. observed 29 patients who had undergone TKA for 6 months postoperatively, with a mean BMI of 31.5 at the time of TKA. The majority of the patients ($59\%$) lost weight (> 0 kg). When using $5\%$ as a threshold for a clinically significant change in weight, $38\%$ of the TKA patients had lost weight after the procedure. Furthermore, $35\%$ of the patients had gained weight after the TKA (> 0 kg). The mean BMI change was − 1.07 (SD 1.80) corresponding to $3.3\%$ (SD 5.7) reduction in weight [8]. In the current study, the median change of weight after TKA was found to be a gain of 5 kg in the TKA-BS group. Ast et al. reviewed 3,036 patients who underwent TKA with a mean BMI of 30.2 at the time of TKA. 2 years postoperatively, no change in BMI was seen in $69\%$ of the patients [9]. In the current study, the patients in the TKA-BS group had a median weight gain of $4\%$. Inacio et al. assessed the change in weight before and after TKA or total hip arthroplasty (THA). They demonstrated that most of the patients who underwent TKA ($68.5\%$ of 20,060) exhibited an unchanged weight after the procedure, when defining a change of $5\%$ as clinically significant. Nevertheless, these studies investigated patients with a lower BMI compared to our study, since all of the patients who underwent TKA prior to BS in our study where candidates for BS [10]. Nearing et al. [ 2017] evaluated the outcomes after TKA/THA in patients who had undergone BS either before or after their TKA/THA. This study included 102 patients who received a TKA/THA. TKA /THAs were performed at a mean of 4.9 years before and 4.3 years after BS. Obesity-related co-morbidities were similar between the two groups. Patients who underwent TKA/THA before BS demonstrated an average increase in BMI of 2.6 between the TKA/THA and BS, which is in line with our results. However, they found that patients who underwent TKA/THA after BS had a statistically significant lower BMI 1 year after the TKA/THA, compared to patients who underwent TKA/THA before BS [11]. However, in our study, we compared patients' weight 1-year post-BS instead of post-TKA. In a recently published randomized controlled trial analyzing the change in BMI and weight 1 year after TKA, the intervention group who received BS prior to TKA had a significantly greater BMI loss (− 6) and weight loss (− 16.5 kg) compared to the patients who underwent “treatment as usual” before TKA. However, $\frac{2}{41}$ patients did not undergo BS prior to their TKA, and $\frac{12}{41}$ did not undergo any TKA in the intervention group, but were still included in the intention to treat analysis. Furthermore, the majority of the patient in the intervention group underwent gastric banding which differs from the BS performed in the current study [12].
In a relatively recent systemic review, obesity was shown to increase the risk of revision following TKA [7]. Sezgin et al. found that obesity was associated with an increased overall risk of revision and revision due to infection, but could not show the same relationship for revision for reasons other than infection [13]. Since BS is an effective method of obtaining long-term weight loss [3], it is reasonable to believe that BS prior to TKA could reduce the risk of revision. However, in a previous study we did not find any association between a reduced risk of revision in patients undergoing BS prior to TKA [14]. Risk of revision based on the sequence of surgery has also been studied, demonstrating similar results to the current investigation [11, 15]. Nearing et al. [ 2017] did not detect any difference in the risk of revision, regardless of the timing of the TKA/THA in relation to the BS. The mean follow-up time after TKA/THA was 3.2 years in those who underwent TKA/THA after BS and 9.2 years in those who underwent TKA/THA before BS. The current investigation was limited to a follow-up time of 2 years between the surgeries [11]. In a retrospective study, Kulkarni et al. [ 2011] evaluated the risk of revision after 1 year in 53 patients who underwent TKA/THA before BS and 90 patients who underwent TKA/THA after BS. No patients who underwent TKA, whether before or after BS, were reported to have required a revision within 1 year [15].
Despite these negative results, there are other factors that bear consideration. A recent retrospective cohort study investigated the risk of medical complications after the second operation in patients who underwent both BS and TKA/THA. When adjusting for comorbidities, their results indicated that BS before TKA/THA was associated with improved postoperative outcomes. However, they did not include revision of TKA in their outcomes [16].
Although the present study carries the benefits of using a nationwide cohort based on prospectively collected data from two high-quality sources [17, 18] it is not without limitations.
The current study is an observational study, thus it cannot making any claims about causality. Additionally, another significant limitation is the absence of data on comorbidities which were not available and thus not included in the analyses. Although the study utilizes the American Society of Anesthesiologists classification obtained from SKAR and the obesity surgery mortality risk score (OS-MRS) obtained from SOReg, these variables were not adjusted for in the regression model due to their interaction with BMI. [ 19, 20] Despite these limitations, it should be noted that the patients included in the study underwent elective surgeries and were optimized prior to the procedures by both the surgeon and anesthesiologist. The majority of the patients in both groups were classified as ASA 2 prior to the TKA. Additionally, to reduce the risk of confounding health factors affecting the outcome, the cut-off time between the two surgical procedures was set to 2 years. Finally, it is important to note that the $95\%$ CIs are relatively wide, which may suggest a potential problem with achieving sufficient statistical power.
## Conclusion
The sequence of surgery in patients undergoing both BS and TKA does not appear to affect the weight loss after BS or the risk of revision after TKA. However, further study is required before changes can be made to relevant guidelines.
## References
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|
---
title: 'Mediating effect of BMI on the relation of dietary patterns and glycemic control
inT2DM patients: results from China community-based cross-sectional study'
authors:
- Jinbo Wen
- Dandan Miao
- Zhongming Sun
- Dianjiang Li
- Enchun Pan
journal: BMC Public Health
year: 2023
pmcid: PMC10007773
doi: 10.1186/s12889-022-14856-5
license: CC BY 4.0
---
# Mediating effect of BMI on the relation of dietary patterns and glycemic control inT2DM patients: results from China community-based cross-sectional study
## Abstract
### Objective
To analyze the effects of different dietary types on in type 2 diabetes mellitus (T2DM) and determine the mediating effects of Body Mass Index (BMI) on dietary type with Fasting Plasma Glucose (FPG), Glycosylated Hemoglobin (HbA1c) on the associations in T2DM.
### Methods
Data of community-based cross-sectional study with 9602 participants including 3623 men and 5979 women were collected from the project ‘Comprehensive Research in prevention and Control of *Diabetes mellitus* (CRPCD)’ conducted by Jiangsu Center for Disease Control and Prevention in 2018. The dietary data were collected from a food frequency qualitative questionnaire (FFQ) and dietary patterns were derived through Latent Class Analysis (LCA). Then, Logistics regression analyses were used to evaluate the associations of FPG, HbA1c with different dietary patterns. The BMI (BMI = height/weight2) was used as a moderator to estimate the mediating effect. Mediation analysis was performed using hypothetical variables, the mediation variables, to identify and explain the observed mechanism of association between the independent and dependent variables while the moderation effect was tested with multiple regression analysis with interaction terms.
### Results
After completing Latent Class Analysis (LCA), the dietary patterns were divided into three categories: TypeI, TypeII, TypeIII. After adjusting for confounding factors such as gender, age, education level, marital status, family income, smoking, drinking, disease course, HDL-C, LDL-C, TC, TG, oral hypoglycemic drugs, insulin therapy, Hypertension, Coronary heart disease, Stroke, Type III were all significantly associated with HbA1c compared to those with Type I ($P \leq 0.05$), and the research showed the patients with Type III had High glycemic control rate. Taking type I as the reference level, the $95\%$ Bootstrap confidence intervals of the relative mediating effect of TypeIII on FPG were (-0.039, -0.005), except 0, indicating that the relative mediating effect was significant (αIII = 0.346*, βIIIFPG = -0.060*). The mediating effect analysis was performed to demonstrate that BMI was used as a moderator to estimate the moderation effect.
### Conclusions
Our findings demonstrate that consuming Type III dietary patterns associates with good glycemic control in T2DM and the BMI associations would be playing a two-way effect between diet and FPG in Chinese population with T2DM, indicated that Type III could not only directly affect FPG, but also affect FPG through the mediating effect of BMI.
## Introduction
Diabetes has become a serious public health problem worldwide, the International Diabetes Federation (IDF) reported about 463 million with diabetesandaccounting for $9.3\%$in 2019,and the prevalence would from to 578 million in 2030, and will continue to 700 million in 2040, representing $10.9\%$ of the global population [1]. As well as, evidence indicates thatobesity may influence human health through excessive energy intake [2]. Overfeeding, as major an unhealthy lifestyle of person, can have a strong impact on diabetes [3]. A study reported that diabetes treatment such as lifestyle changes and hypoglycemic drugs were associated with glucose decreases into the normal range [4], which these studies were conducted in worldwide and found that nutritional therapy were associated with the treatment of diabetic mellitus(DM) effectively [5]. In currently, vegetarian diet, Mediterranean diet and DASH diet have been advocated to play a role in treating diabetes mellitus [6, 7].
There are few studies on dietary patterns on glycemic of patients with type 2 diabetes [8, 9]. Before, a South China research found that fruits and whole grains were associated with lower risk of T2DM [10]. Previous studies targeted the morbidity of T2DM [11], to our knowledge, no such big-sample T2DM patients between dietary patterns and glycemic have been reported based on Chinese community. In addition, the associations between BMI and dietary type with glycemic remain unclear.
At present, this study was based on a large-scale community-based research, of which the participants were enrolled in basic public health service management in Huai'an city, Jiangsu Province in 2018. We conducted a survey based on the dietary habits of 9602 T2DM patients in the past year. We aimed to examine: [1]Explore the dietary status of T2DM population in Chinese community. Dietary pattern is the summary of people's food intake in a period of time, which is a relatively rigorous dietary measurement standard at present. [ 2] Whether such associations that between diet and glycemic are affected by the regulation of BMI from the T2DM of Chinese. We hypothesized that different dietary patterns would be associated with Fasting Plasma Glucose(FPG), Glycosylated Hemoglobin (HbA1c)control in T2DM, and such association would be have a mediating affections by BMI.
## Study object and data source
The project “Comprehensive Research in Prevention and Control of Diabetes Mellitus (CRPCD)” was a community-based, large, ongoing study aiming to exploring an applicable technology for comprehensive intervention in people with T2DM, which was included at baseline investigation of T2DM patients in community chronic disease health management from in Huai'an city, Jiangsu Province. The CRPCD baseline data was collected in December 2013 to January 2014 and demographic characteristics, physical examination (height, weight, waist circumference, blood pressure), daily dietary lifestyle and disease history were self-reported by T2DM patients by investigated under the guidance of professionals. After excluding those who were in poor physical condition and refused to participate in the survey, unqualified questionnaires, some food groups and glycemic deficiency values, a total of 9602 study subjects were enrolled in the data analyses. And extracted fasting venous blood from the patients, what is it used for examining fasting plasma glucose (FPG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), glycosylated hemoglobin (HbA1c), total cholesterol (TC), triglyceride (TG) and etc. This study was reviewed by the ethics Committee of Jiangsu Center for Disease Control and Prevention (No.2013026), and all subjects signed informed consent. The specific measurement methods are shown in the published papers of our research group [12].
## Description of variables
FPG and HbA1c cutoff points according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2013 Edition) [13], abnormal glycemic: FPG ≥ 7.0 mmol/L; HbA1c ≥ $6.5\%$ mmol/L. Daily dietary intake was obtained using a semi-FFQ (food frequency questionnaire) by trained workers, which presents the consumption of each food item among T2DM, who were asked to rate whether they eating or not of rice, grains, fried foods, livestock and poultry aquatic products, egg, dairy products, fresh fruits, vegetable, Bean Products, nuts and cakes. Cumulative smoking of more than 100 cigarettes was defined as smoking, drinking was defined by whether or not they were currently drinking alcohol.
## Statistical analysis
All analyses were carried out using the IBM SPSS Statistics version 20 with the exception of the mediation analyses that used the SPSS PROCESS V3.4, the dietary pattern analysis that performed using Mplus Version 7 [14].
Latent Class Analysis (LCA) was conducted to identify the dietary of113 categories of food that reflect underlying optimal number of dietary types including category probability and latent probability [15, 16], which hypothesizes the existence of dietary patterns with food groups, indicated a general dietary pattern of theT2DM patients.
Demographic characteristics were reported across the latent cluster-3, and Mean ± SD as used to describe the continuous variables with the normal distribution, inter-quartile rangewas used to represent the non-normal distribution data and the frequency (composition ratio) was used to describe the characteristics. Chi-square test for categorical variables and ANOVA for continuous variables were carried out to analyze differences between groups, statistically significance was set at $p \leq 0.05.$
## Dietary pattern assessment
We used Latent Category Analysis(LCA) to assigned food groups into the class corresponding to the maximum posterior probability of cluster membership. It can be seen from the Table 1 that AIC, BIC and adjusted BIC continued to decline as the number of categories increased. However, the likelihood-ratio difference test finds that the Cluster—4 is greater than 0.05 and is not meaningful, and thinks that the Cluster—3 model is due to the fourth type. Therefore, considering comprehensively, the 3-cluster is the ideal model. Finally, concluded that when food groups were divided into three modes, the model had the most moderate AIC and BIC, and the BLRT test $P \leq 0.001$, which was statistically significant, manifesting that our class was successful. Table 1Fitting indexes of models of different potential categoriesClustersAICBICaBICEntropyLoglike- lihoodP for LMRP for BLRTClassificationClass probability297,754.09798,105.41497,949.6990.605-31,$\frac{320.0460.0000.0004244}{53580.442}$/0.558396,245.02496,775.58496,540.4240.633-48,$\frac{828.0490.0000.0002714}{5479}$/$\frac{14090.283}{0.571}$/0.147495,979.10096,688.90396,374.2960.672-48,$\frac{048.5120.0920.000251}{1343}$/$\frac{5274}{27340.026}$/$\frac{0.120}{0.549}$/0.285AIC Chachi information criterion, BIC Bayesian information criterion, ABIC Bayesian information criterion for corrected samples, LMR Likelihood ratio test index, BLRT Likelihood ratio test based on Bootstrap Based on the best models identified for the 3 potential categories, we obtained conditional probability and category probability of 13 explicit variables related to dietary patterns (Fig. 1). It is not difficult to find that in the three categories, rice, miscellaneous grains, vegetables and so on are the necessities of daily diet intake. The Cluste-3 of scores generated through the LCA were labeled “poor diet”, “moderate diet” and “balanced diet”, and the number of people corresponding to the three dietary types was,1409, 5479 and 2714 respectively. ( Table 1).Fig. 1Conditional probabilities of food groups on three potential categories [1] Type I: it showed the mainly characterized by a high consumption of rice, grains and vegetables and accounting for $14.7\%$.
[2] Type II: this group accounts for $57.1\%$ and their dietary intake is mainly poultry, livestock, aquatic products, eggs and soy products on the basis of necessities such as rice, cereals and vegetables, and the number of the moderate diet category remained the most through all dietary patterns.
[3] Type III: it is mainly consumed poultry meat, livestock meat, aquatic products, eggs, dairy products, fruits and soy products on the basis of necessities such as rice, cereals and vegetables, which accounted for $28.3\%$.
## Baseline characteristics
The baseline characteristics of the three potential category groups are shown in Table 2, 9602 type 2 diabetic population including 3623 males and 5979 females. The mean age for study subjects were (61.58 ± 10.09) years, and for the Type I was the highest (63.59 ± 9.69) years. Significant differences were found in gender, age, FPG,HbA1c, educational level, marital status, annual family income, smoking, alcohol consumption, disease course, hypertension and stroke among different dietary patterns ($P \leq 0.05$). Table 2Baseline characteristics of different dietary patterns in T2DMVariableDietary typeχ2PIIIIIIGender[n(%)]110.46 < 0.001 Men355($25.2\%$)2183($39.8\%$)1085($40.0\%$) Women1054($74.8\%$)3296($60.2\%$)1629($60.0\%$) BMI(kg/m2)25.69 ± 3.8625.89 ± 3.6026.04 ± 3.494.340.013 Age(x ± s)63.59 ± 9.6961.57 ± 9.8460.56 ± 10.6311.68 < 0.001 FPG(mmol/L)9.02 ± 4.258.98 ± 4.288.58 ± 3.769.57 < 0.001 HbA1c(mmol/L)7.90 ± 2.107.80 ± 2.007.70 ± 2.007.72 < 0.001Literacy[n(%)]490.10 < 0.001 No formal education789($56.4\%$)2356($43.3\%$)805($29.8\%$) Primary school391($27.9\%$)1616($29.7\%$)685($25.4\%$) Junior high school159($11.4\%$)1052($19.3\%$)659($24.4\%$) High school and above61($4.4\%$)417($7.7\%$)549($20.3\%$)Marital condition[n(%)]50.67 < 0.001 Married1138($81.5\%$)4661($85.7\%$)2409($89.4\%$) Unmarried259($18.5\%$)775($14.3\%$)285($10.6\%$)Family income ([n(%)],Ten thousand Yuan)224.80 < 0.001 < 10,000 yuan458($32.7\%$)1426($26.2\%$)462($17.2\%$) 1–30,000 yuan527($37.6\%$)2092($38.4\%$)937($34.8\%$) 3–100,000 yuan378($27.0\%$)1770($32.5\%$)1131($42.0\%$) > 100,000 yuan39($2.8\%$)154($2.8\%$)163($6.1\%$)Drinking[n(%)]81.31 < 0.001 Yes123($8.7\%$)980($17.9\%$)522($19.2\%$) No1286($91.3\%$)4495($82.1\%$)2191($80.8\%$)Smoking[n(%)]21.62 < 0.001 Yes383($27.2\%$)1714($31.3\%$)727($26.8\%$) No1026($72.8\%$)3765($68.7\%$)1987($73.2\%$)Course of disease[n(%)]30.96 < 0.001 Less than 2 years424($30.1\%$)1704($31.1\%$)723($26.6\%$) 2–4 years417($29.6\%$)1601($29.2\%$)757($27.9\%$) 5 to 9 years298($21.1\%$)1241($22.7\%$)664($24.5\%$) 10 years or more270($19.2\%$)933($17.0\%$)570($21.0\%$)Hypertension[n(%)]30.89 < 0.001 No362($25.7\%$)1525($27.9\%$)893($33.0\%$) Yes1044($74.3\%$)3938($72.1\%$)1817($67.0\%$)Coronary heart disease ([n(%)],year)4.450.108 Yes158($11.2\%$)535($9.8\%$)303($11.2\%$) No1165($82.7\%$)4663($85.1\%$)2286($84.2\%$) Unclear86($6.1\%$)281($5.1\%$)125($4.6\%$)Stroke[n(%)]21.64 < 0.001 Yes236($16.7\%$)696($12.7\%$)327($12.0\%$) No1143($81.1\%$)4601($84\%$)2320($85.5\%$) Unclear30($2.1\%$)182($3.3\%$)67($2.5\%$)Oral hypoglycemic drugs[n(%)]37.67 < 0.001 Yes980($70.4\%$)3669($67.3\%$)1670($61.8\%$) No413($29.6\%$)1780($32.7\%$)1034($38.2\%$)Insulin therapy[n(%)]0.110.949 Yes162($11.6\%$)618($11.3\%$)306($11.3\%$) No1231($88.4\%$)4831($88.7\%$)2398($88.7\%$) HDL-C(mmol/L)1.50 ± 0.501.48 ± 0.481.45 ± 0.446.580.001 LDL-C(mmol/L)3.35 ± 1.143.34 ± 1.143.36 ± 1.080.160.848 TC(mmol/L)5.42 ± 1.475.37 ± 1.435.32 ± 1.362.840.058 TG(mmol/L)2.08 ± 1.812.03 ± 1.641.93 ± 1.614.580.010
## Effect of dietary patterns on glycemic control and Glycemic outcome
Different indexes of glycemic control in women with study subjects for different dietary patterns. Participants with the Type I had the highest value of HbA1c and the FPG. Data analysis showed that the control rate of FPG andHbA1c was different among three dietary patterns. There were421 ($29.9\%$), 1798 ($32.8\%$) and 948 ($34.9\%$) FPG controls in the Type I, Type II, Type III, respectively. Moreover,531 ($37.7\%$), 2073 ($37.8\%$) and 1143 ($42.1\%$) had HbA1c control, respectively.
The results of the effect of dietary patterns on different indexes of glycemic control are presented in Table 3 and Fig. 2. After adjusting gender, age, education level, marital status, family income, smoking, drinking, disease course, Hypertension, Coronary heart disease, Stroke. Logistic regression analysis showed that compared to patients with Type I, Type III had a higher control rate of FPG andHbA1c. The OR was 0.75(0.64,0.87),0.76(0.65,0.89), respectively. Table 3Effects of different dietary types on glycemic controlIndexes of glycemic controlDietary typeSampleControlledOR0($95\%$CI)POR1($95\%$CI)PI1409531––––FPGII547920730.99(0.88,1.12)0.910.93(0.82,1.07)0.31III271411430.83(0.73,0.95)0.010.75(0.64,0.87)0.00I1409421––––HbA1cII547917980.87(0.77,0.99)0.040.87(0.76,1.00)0.06III27149480.79(0.69,0.91)0.000.76(0.65,0.89)0.00OR0 Unadjusted for confounders. OR1: Adjusted for confounding factors such as gender,age,BMI, education level, marital status, family income, smoking, drinking,diseasecourse,HDL-C,LDL-C,TC,TG,oral hypoglycemic drugs, insulin therapy, Hypertension, Coronary heart disease, StrokeFig. 2Logistic regression analysis of dietary pattern and FPG and HbA1c. Note:*:$p \leq 0.05.$VS:Type I.**: $p \leq 0.001.$VS:Type I.OR0: Unadjusted for confounders. OR1: Adjusted for confounding factors such as gender,age,BMI, education level, marital status, family income, smoking, drinking,disease course,HDL-C,LDL-C,TC,TG,oral hypoglycemic drugs, insulin therapy, Hypertension, Coronary heart disease, Stroke
## Mediating effect analysis
As shown in Table 4 and Fig. 3, when Type I was used as a reference, the mediating effect analysis showed that dietary Type III had a statistically total effect on FPG (γ*II = 0.442, $P \leq 0.001$). After the addition of BMI, the mediating effect of BMI on FPG in group Type III was -0.021, and the $95\%$ Bootstrap confidence interval was (-0.039,-0.005), which did not include 0, indicating significant mediating effect. The direct effect of Type II on FPG was 0.422, and the $95\%$ Bootstrap confidence interval was (-0.687,-0.156), indicating that the direct effect was also significant. These results indicated that Type III could not only directly affect FPG, but also affect FPG through the mediating effect of BMI. However, the $95\%$ Bootstrap confidence interval of the Type II group was (-0.028,0.002), indicating that the mediating effect was not significant in the group. Table 4Analysis of the mediating effect of body mass index on diet type and glycemiccontrolMediating effect path(compared type I)EffectSE$95\%$CIFPG TypeII——BMI——FPG0.012-0.007(-0.028,0.002) TypeII——FPG-0.0240.123(-0.265,0.218) TypeIII——BMI——FPG-0.021*-0.009(-0.039,-0.005) TypeIII——FPG-0.422*0.135(-0.687,-0.156)HbA1c TypeII——BMI——HbA1c-0.0020.001(-0.003,0.001) TypeII——HbA1c-0.1130.060(-0.232,0.005) TypeIII——BMI——HbA1c-0.0030.001(-0.005,0.000) TypeIII——HbA1c-0.245*0.066(-0.375,-0.114)$P \leq 0.05$, which was statistically significantFig. 3The mediating effect model of BMI on dietary type and FPG and HbA1c The total effect between BMI and Type III and HbA1c was -0.248 (-0.378, -0.118), $p \leq 0.001$, the difference was significant. The direct effect of BMI was 0.245(-0.375,-0.114) and the mediating effect of BMI was -0.003(-0.005,0.000) in TypeIII and HbA1c. The difference has some significance, but the significance is very weak. No mediating effect of BMI was found in TypeII.
## Discussion
We classified T2DM patients into three dietary patterns classes based on their self-reported diet, and indicate. It is found that Type, Type II and Type III of the classes, our results indicate that rice, grains and vegetables were the main food components of each dietary. The trait should be a primary focus in Type I, which we initially thought seemed that related to the economic level, it is more in line with the current low-income population in China, and these rice, grains and vegetables seem to be just the necessary food sources only to maintain the human body. Based on the TypeI, TypeII people will take poultry, livestock, aquatic products, eggs and soy products as the sources of daily dietary intake. Different from the first two groups, in TypeII, people have consumed rice, grains, vegetables, poultry, livestock, aquatic products, eggs and soy products, dairy products, fruits, and so on (Fig. 1).
The Chinese Community research demonstrated that a correlation between different dietary patterns and glycemic, which the balanced diet was a consistent protective factor of FPG and HbA1camong 9602 T2DM patients enrolled in this study,3747 ($39.0\%$) of FPG and 3167 ($33.0\%$) of HbA1c were within the control range. Latent Class Analysis need to be taken into account in the research, in order to accurately explore which diet was more beneficial to control the glycemic in the study subjects. American Diabetes Association with higher adherence to reduce the intake of high-energy and high-fat foods about in 2013 [17]. It is well known that nutritional balance is an important factor in maintaining human health and well-being throughout the lifecycle [18]. Ensuring a balanced diet means having food diversity, which is the reason of food contains a variety of nutrients, and a variety of nutrients in different foods [19].
Our study was able to identify three dietary patterns associated with glycemic control in T2DM patients. Among the three dietary types, TypeIII had a higher glycemic control rate and had a greater reduction in glycemic risk than TypeI. TypeIII significantly reduced FPG (0.75(0.64,0.87)) and HbA1c (0.75(0.65,0.89), which the main feature of dietary intake is greater diversity, suggesting that study subjects should maintain a diversified diet as much as possible in order to better improve the glycemic control rate of T2DM patients.
The relationship between Type III and glycemic may have the following explanations: First, Dietary diversity is considered by nutritionists to be an important factor in improving dietary quality, which can improve the nutritional status of the human body. The richer the diet type, the more balanced the nutrition [20, 21]. Secondly, only when the dietary intake is more abundant, the interaction of various nutrients in the body can maintain balance [22]. For example, dairy products were the main source of calcium and vitamin D, which could help the body better and protect its function [23]. And that fresh fruits are important sources of antioxidants, which can be consumed in moderation to prevent functional damage by reducing inflammation and oxidative stress [24]. Therefore, this suggests that study subjects should be encouraged to intake more and pay attention to their daily diet and eat a variety of foods within the economic range to ensure a balanced diet.
BMI is widely used to measure general obesity and is currently recognized as the best indicator to measure systemic obesity [25]. Our study showed that using TypeI as a control, FPG and HbA1c were associated with TypeIII through BMI, with a mediating effect of -0.021(-0.039,-0.005). A possible explanation is that there may be close relationship between BMI and diet. Besides, there is previous evidence for the associations between each of these factors (dietary-glycemic、dietary-BMI, BMI-glycemic) [26–31]. And in this study, we find that TypeIII can significantly reduce glycemic.
Long-term development of type 2 diabetes is associated with inadequate glycemic control [32]. Previous studies have confirmed that the effects of FPG and HbA1c are different, and study subjects who vegetarian dietary pattern can effectively reduce HbA1c, but has little effect on FPG [33]. Before adjusting confounding factors, TypeII could reduce HbA1c in this study ($$P \leq 0$$,04), but had little effect on FPG, compared with TypeI as a control. At the same time, the results of mediation analysis showed that BMI accounted for about $4.98\%$ of the influence between TypeIII and FPG. However, the effect of BMI between TypeIII and HbA1c was small, accounting for about $1.22\%$, and our mediation analysis found that the mediation effect was also close to the critical value, which may be limited by our sample size and study population.
## Advantages of the research
First, the sample size was large ($$n = 9602$$ T2DM patients) and the results were reliable, for which dietary data collected over the past year better reflect participants' daily dietary intake status than the three-day weighting method [34]. To our knowledge, this is the first study of analyzing the role of BMI in dietary patterns and glycemic associated with type 2 diabetes in Chinese which has a very novel characteristics.
## Limitations of the research
The current study has several limitations. First, this study is a cross-sectional study, and it is difficult to infer causality. Frequency of self-reported dietary intake by the food frequency questionnaire was prone to recall bias. Second, Analysis of dietary patterns is subject to geographic, cultural, ethnic and economic differences, which is subjective. It is also important to note the limitations of statistical methods. The assignment of food groups to latent classes is based on their highest estimated probability to the identified pattern. Therefore, these underlying patterns should not be considered as actual dietary patterns, but as approximations of more complex patterns. Therefore, prospective studies are needed to further explore the potential mechanism between study subjects diet and glycemic.
## Conclusion
TypeIII contains poultry meat, livestock meat, aquatic products, eggs, dairy products, fruits, soy products, rice, cereals and vegetables, are a relatively diverse diet structure. This study found that dietary diversity can significantly reduce FPG and HbA1c in T2DM patients, which firstly identified and BMI significantly mediated the association between diet and fasting glucose. The findings provided new insights in understanding the possible mechanisms involved in the association between dietary and BMI.
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|
---
title: 'Proteomics of high-density lipoprotein subfractions and subclinical atherosclerosis
in type 1 diabetes mellitus: a case–control study'
authors:
- Marcos Tadashi K. Toyoshima
- Monique F. M. Santana
- Amanda R. M. Silva
- Gabriela B. Mello
- Daniele P. Santos-Bezerra
- Marisa F. S. Goes
- Adriana A. Bosco
- Bruno Caramelli
- Graziella E. Ronsein
- Maria Lucia Correa-Giannella
- Marisa Passarelli
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10007776
doi: 10.1186/s13098-023-01007-y
license: CC BY 4.0
---
# Proteomics of high-density lipoprotein subfractions and subclinical atherosclerosis in type 1 diabetes mellitus: a case–control study
## Abstract
### Background
Subclinical atherosclerosis is frequently observed in type 1 diabetes (T1D) although the mechanisms and markers involved in the evolution to established cardiovascular disease are not well known. High-density lipoprotein cholesterol in T1D is normal or even high, and changes in its functionality and proteomics are considered. Our aim was to evaluate the proteomics of HDL subfractions in T1D and control subjects and its association with clinical variables, subclinical atherosclerosis markers and HDL functionality.
### Methods
A total of 50 individuals with T1D and 30 matched controls were included. Carotid-femoral pulse wave velocity (PWV), flow-mediated vasodilation (FMD), cardiovascular autonomic neuropathy (CAN), and ten-year cardiovascular risk (ASCVDR) were determined. Proteomics (parallel reaction monitoring) was determined in isolated HDL2 and HDL3 that were also utilized to measure cholesterol efflux from macrophages.
### Results
Among 45 quantified proteins, 13 in HDL2 and 33 in HDL3 were differentially expressed in T1D and control subjects. Six proteins related to lipid metabolism, one to inflammatory acute phase, one to complement system and one to antioxidant response were more abundant in HDL2, while 14 lipid metabolism, three acute-phase, three antioxidants and one transport in HDL3 of T1D subjects. Three proteins (lipid metabolism, transport, and unknown function) were more abundant in HDL2; and ten (lipid metabolism, transport, protease inhibition), more abundant in HDL3 of controls. Individuals with T1D had higher PWV and ten-year ASCVDR, and lower FMD, Cholesterol efflux from macrophages was similar between T1D and controls. Proteins in HDL2 and HDL3, especially related to lipid metabolism, correlated with PWV, CAN, cholesterol efflux, HDLc, hypertension, glycemic control, ten-year ASCVDR, and statins use.
### Conclusion
HDL proteomics can be predictive of subclinical atherosclerosis in type 1 diabetes. Proteins that are not involved in reverse cholesterol transport may be associated with the protective role of HDL.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-023-01007-y.
## Background
Type 1 diabetes (T1D) represents $5\%$ to $10\%$ of all diabetes cases [1] with an overall cardiovascular disease (CVD) risk—mainly manifested by coronary heart disease, cerebrovascular and peripheral artery disease—increased up to eight times in comparison to people without diabetes [2]. Good glycemic control in individuals with T1D is associated with a lower risk of microvascular complications and cardiovascular disease. Glycated hemoglobin (HbA1c) has been used as a standard measure for long-term (two to three months) glucose control, and serum fructosamine can be used to assess the glycemic control over the past two to three weeks [3]. The pathophysiology underlying CVD in T1D is not well understood, but its prevalence is related to increased HbA1c levels, diabetes duration, age, sex, and possibly to race and ethnicity. Besides, abnormal vascular function with increased artery calcification, endothelial dysfunction, and cardiovascular autonomic neuropathy (CAN) increase the susceptibility to macrovascular atherosclerotic disease [4].
Plasma lipids and lipoproteins are not classically altered in T1D as usually observed in type 2 diabetes (T2D) [5]. Nonetheless, changes in lipoprotein functionality are considered as putative contributors to CVD risk. HDL cholesterol (HDLc) plasma levels that are inversely related to CVD risk are normal or even higher in T1D [4, 6]. In this sense, alterations in HDL particle functionality ascribed to chemical modifications by glycation, oxidation, and autoantibodies and in its lipidomics, and proteomics have been described as pro-atherosclerotic by impairing its antiatherogenic properties [6, 7]. HDL are heterogeneous particles varying from 7 to 10 nm with different subpopulations. Pre-beta nascent HDL and lipid poor apolipoprotein A-I (apoA-I) remove excess cholesterol from peripheral cells, including arterial macrophages, by interacting with ATP binding cassette transporter A-1 (ABCA-1) in the first step of the reverse cholesterol transport (RCT). After esterification by the lecithin cholesterol acyltransferase, larger HDL particles are formed also removing cell cholesterol via ATP binding cassette transporter G-1 (ABCG-1). Cholesterol is driven to the liver by the HDL subfraction 2 (HDL2) that interacts with the scavenger receptor class B type 1 (SR-B1) or by the uptake of apoB-containing lipoproteins after receiving esterified cholesterol from HDL by the cholesteryl ester transfer protein (CETP). Cholesterol can be eliminated in the bile as free cholesterol, or after conversion into bile acids, which allows its excretion in feces [6, 8]. Besides its role along the RCT, HDL has antioxidant, anti-inflammatory, antiplatelet aggregation, and vasodilation actions that contribute to cardiovascular protection [8–10]. HDL is a cargo lipoprotein for microRNAs, several active lipid species, and proteins that do not directly relate to atherosclerosis but can modulate HDL function and even be a marker of its functionality [9]. Proteomics evidenced around 251 proteins associated with HDL with functions related to lipid metabolism, activation of the complement system, modulation of proteases, immunogenicity, and others [11]. It is conceivable that alterations in HDL´s protein cargo may interfere with its antiatherogenic properties that cannot be seen by simply measuring HDLc or apoA-I [12]. Nonetheless, there are only a few studies dealing with the proteome of HDL in T1D, all of them dealing with total HDL fraction and mostly with subjects with DM categorized according to glycemic control [13–15]. In the present investigation, the proteome of HDL subfractions (HDL2 and HDL3) was quantified by mass spectrometry using the parallel reaction monitoring (PRM) quantitative methodology. Subsequently, the proteome was related to HDL capacity in removing cell cholesterol, indicators of subclinical atherosclerosis—pulse wave velocity (PWV) and flow-mediated vasodilation (FMD) and ten-year atherosclerotic cardiovascular disease risk (ASCVDR) estimation in subjects with T1D in comparison to non-diabetes individuals.
This study aims to evaluate the proteomics of HDL subfractions in T1D subjects and controls and its association with clinical variables, subclinical atherosclerosis markers, and HDL functionality.
## Material and methods
This was a case–control study that enrolled 50 T1D and 30 non-diabetes control individuals matched by age, gender, and body mass index (BMI). The convenience sampling method was used in the study for the recruitment of individuals with T1D. Snowball sampling was used to recruit controls. The inclusion criteria were based on age ≥ 18 years, T1D diagnosis ≥ 5 years. Individuals with a personal history of clinically evident atherosclerotic macrovascular disease (coronary disease, peripheral arterial disease, or cerebrovascular disease), active smoking, or those who stopped smoking more than ten years ago, and triglycerides (TG) concentration > 400 mg/dL were not included. Participants signed an informed written consent form previously approved by The Ethical Committee for Human Research Protocols of the Hospital das *Clinicas da* Faculdade de *Medicina da* Universidade de São Paulo (#3.796.622; $\frac{01}{09}$/2020), in accordance with the Declaration of Helsinki. The reporting of this study conforms to STROBE guidelines [16].
Peripheral blood was drawn after overnight fasting and plasma was immediately separated in a refrigerated centrifuge (4 °C). Plasma lipids [TG, total cholesterol, (TC), and HDLc], and plasma glucose were determined by enzymatic techniques (Labtest do Brasil, Minas Gerais, Brazil). HDLc was determined after precipitation of apoB-containing lipoproteins with $0.2\%$ dextran sulfate/3 M magnesium chloride (v/v). Low-density lipoprotein (LDL) cholesterol (LDLc) was calculated by the *Friedewald formula* [17] and the estimated glomerular filtration rate (eGFR), using the CKD-EPI equation [18]. Fructosamine was determined by an automated colorimetric enzymatic method (Labtest do Brasil, Minas Gerais, Brazil). HbA1c was determined by high-performance liquid chromatography, certified by the National Glyco Hemoglobin Standardization Program (NGSP-USA).
## Vascular function tests
A subgroup of 30 subjects with T1D and 30 controls, matched by age, gender, and body mass index (BMI), underwent PWV and FMD tests. The PWV was determined in the carotid-femoral segment using a validated device (Complior®; Gonesse, France) [19, 20]. FMD tests were performed by a single researcher in accordance with the guidelines of the International Brachial Artery Reactivity Task Force (version 2002) [21]. Endothelium-dependent FMD and vascular smooth muscle response to the vasodilator isosorbide dinitrate (independent of the endothelium) were sequentially evaluated in the brachial artery. The brachial artery was accessed above the elbow crease and its diameter was verified by two independent observers, with the inter-observer correlation equal to 0.90 ($p \leq 0.001$). An ultrasound device (Sequoia Echocardiography System, version 6.0, Acuson Siemens®, Malvern, USA) equipped with a multifrequency linear transducer (7-12 MHz) and coupled to a computer specifically programmed to record and analyze this type of data was used. Reactive hyperemia (RH) was induced by inflating the sphygmomanometric cuff in a suprasystolic pressure (50 mmHg above systolic pressure), leading to transient ischemia due to occlusion of the brachial artery for two minutes, and subsequent cuff deflation. Data were obtained under baseline conditions, after induction of RH, and 5 min after oral administration of isosorbide dinitrate 10 mg. FMD(%) after RH was expressed as: [(RH Diameter − Basal Diameter)/Basal Diameter × 100]. Endothelium-independent vasodilation (EID;%) was expressed as: [(Post-nitrate Diameter − Pre-nitrate Diameter)/Pre-nitrate Diameter]. Measurement of basal and after RH indution brachial artery diameter were performed in the T1D individuals and controls, but FMD after nitrate was not performed in controls, considering the risk of hypotension in individuals with supposedly full production of nitric oxide. Subclinical atherosclerosis was defined as the stage that precedes clinical atherosclerotic vascular disease. As the results of the PWV and FMD vascular tests were considered continuous variables, the results were evaluated by comparing the T1D group and the controls regarding the difference in vascular parameters.
## Cardiovascular autonomic tests
Sympathetic and parasympathetic cardiac function assessment was performed only in individuals with T1D, by analyzing seven tests that include Ewing's standardized tests and spectral analysis performed by the Poly-Spectrum software (version 4.8.143; Neurosoft®, Ivanovo, Russia). Three or more altered tests indicated the presence of CAN [22, 23].
## Cardiovascular risk estimate
The validated tools T1 Risk Engine [24] and QRISK3 [25] (available on the internet) were used to estimate ten-year ASCVDR in T1D.
## Lipoproteins isolation
HDL subfractions [HDL2; density (d) = 1.063–1.125 g/mL, and HDL3; $d = 1.125$–1.21 g/mL] were isolated by discontinuous density gradient ultracentrifugation (100.000 g, 4 °C, 24 h, SW40 rotor; Beckman ultracentrifuge) [26]. Samples were dialyzed against phosphate-buffered saline containing EDTA (PBS) and kept frozen at − 80 °C. LDL ($d = 1.019$–1.063 g/mL) was isolated from a pool of healthy plasma donors and acetylated as previously described by Basu et al. [ 26], followed by extensive dialysis.
## HDL proteolytic digestion
Ten micrograms of HDL protein were digested with trypsin according to the study of Silva et al. [ 27]. Robustness of the PRM methodology was controlled by using angiotensin peptide (DRVYIHPFHL, 0.2 pmol/µL) spiked in each sample as global internal standard. A coefficient of variance of $13\%$ was attained.
## Targeted proteomic analyses
Fifty nanograms of digested HDL proteins were quantified by parallel reaction monitoring (PRM), as described by Silva et al. [ 27]. An inclusion list containing m/z of precursor peptides of interest (3-min window) and corresponding retention times was created by the Skyline software [28]. MS proteomics data have been deposited to the Mass Spectrometry Interactive Virtual Environment (MassIVE) [29].
Sixty-seven proteins were identified, but reduced to 45 after eliminating proteins that could be potential contaminants or in low abundance. Peptides susceptible to ex vivo modification (e.g., methionine-containing peptides) were also avoided, and only peptides satisfactorily detected (with a good chromatographic peak, containing at least four coeluted transitions, and with mass error < 10 ppm) were included in the final analysis.
## Determination of 14C-cholesterol efflux from macrophages
Procedures with mice were approved by the Institutional Animal Care and Research Advisory Committee of Faculdade de *Medicina da* Universidade de Sao Paulo (COBEA-CEUA FMUSP $\frac{071}{17}$) and were performed following the U.S. National Institutes of Health Guide for the Care and Use of Laboratory Animals. Mice were euthanized with an intraperitoneal overdose of ketamine hydrochloride (300 mg/kg of body weight) and xylazine hydrochloride (30 mg/kg of body weight). Six-week-old male C57BL/6J mice were utilized for the isolation of bone marrow-derived cells as previously described [30]. Bone marrow-derived macrophages were overloaded with acetylated LDL (50 µg/mL DMEM) and 14C-cholesterol (0.3 µCi/mL), for 48 h. HDL subfractions (50 µg/mL) were utilized as cholesterol acceptors in 6-h incubations. The percentage of cholesterol efflux was calculated as: [14C-cholesterol in the medium/(14C-cholesterol in the medium + 14C-cholesterol in cells) × 100]. Control incubations were performed in the absence of HDL, and results subtracted from those obtained in the presence of HDL, as previously described [31].
## Statistical analysis
Descriptive analyses were expressed as mean ± SD or median (25th-75th percentiles), and qualitative variables expressed as absolute (n) and relative (%) frequencies. Pearson's chi-square test was used for categorical variables, and Student's t-test or Mann–Whitney for continuous variables. Data normality was accessed by the Shapiro–Wilk and Kolmogorov–Smirnov tests. Proteins in HDL were differentiated by the Wilcoxon independent and paired nonparametric test. Peptide abundances were log10 transformed and p values corrected by the Benjamini–Hochberg method. A corrected p-value threshold was calculated, and only proteins above that were considered significantly different. The odds ratio ($95\%$ confidence interval) was calculated for the association of proteins and discriminant analysis was performed to determine the discriminatory capacity of proteomics data for the both groups. Correlations for linear continuous or monotonous relationships between continuous or ordinal variables were determined, respectively, by Pearson's or Spearman's correlation. No imputation method was used to address missing data. R Studio version 1.1.463 (RStudio. Inc.), and SPSS version 21.0 (SPSS Inc., IBM) were used for analysis. A $p \leq 0.05$ was considered significant.
## Results
Clinical data from 50 T1D and 30 controls are presented in Table 1. Groups were similar regarding age, sex, BMI, abdominal circumference, HDLc, and eGFR. HbA1c and fructosamine levels were higher in T1D, and plasma TC, LDLc, and TG in controls. Retinopathy ($48\%$) and CAN ($30\%$) were the most prevalent complications in T1D. Forty percent of T1D used statins, and $15\%$ had concomitant hypertension. Subjects with T1D and hypertension had longer duration of diabetes, and presence of albuminuria, retinopathy, and statins use. Glycemic control (HbA1c < $8.5\%$ and ≥ $8.5\%$) was only statistically associated with albuminuria. Table 1Clinical, antropometric data and vascular function from subjects with type 1 diabetes and controlsT1Dn = 50Controlsn = 30pFemale sex (%)5050–Age (years)34 (29–40)34 (30–46)0.474BMI (kg/m2)22.9 (20.3–26.6)24.4 (23.2–26.60)0.077Abdominal circumference (cm)89 (80–96)86 (79–95)0.530Time of T1D (years)22 (16 – 30)NAAge at T1D diagnosis (years)11.5 (6 – 18)NAHypertension, n (%)15 ($30\%$)0HbA1c (%)8.3 (6.9–9.3)5.4 (5.0–5.5) < 0.001Fructosamine (µmol/L)362 (336–421)226 (215–237) < 0.001TC (mg/dL)163 (145–184)186.5 (153–199)0.017HDLc (mg/dL)57 (48–67)56.5 (46–68)0.709LDLc (mg/dL)86.5 (75–104)97 (86–120)0.021TG (mg/dL)69 (57–93)86 (68–134)0.036eGFR (mL/min/1.73m2)103.5 (90.2–117.2)101.0 (89.7–100.1)0.538Albuminuria (mg/g creatinine)6.1 (4.2–16.4)NARetinopathy. n (%)24 ($48\%$)Albuminuria. n (%) A1: < 30 mg/g creatinine40 ($80\%$) A2: 30–300 mg/g creatinine8 ($16\%$) A3: > 300 mg/g creatinine2 ($4\%$)Stages of kidney function—eGFR (mL/min/1.73m2) G1: ≥ 9038 ($76\%$) G2: 60–898 ($16\%$) G3a: 45–592 ($4\%$) G3b: 30–441 ($2\%$) G4: 15–290 G5: < 151 ($2\%$)Peripheral neuropathy, n (%)6 ($12\%$)CAN, n (%)15 ($30\%$)Medications, n (%)Statins20 ($40\%$) Atorvastatin10 ($20\%$) Simvastatin10 ($20\%$)ACEi12 ($24\%$)ARB7 ($14\%$)Thiazide Diuretics7 ($14\%$)Spironolactone1 ($2\%$)Beta blocker2 ($4\%$)CCB1 ($2\%$)Hydralazine1 ($2\%$)Ten-year ASCVDR estimate (%) Steno T1 Engine6.9 (4.2–15.6) QRISK34.5 (2.0–7.6)$$n = 30$$$n = 30$PWV (m/s)7.7 (6.9—8.4)6.7 (6.1–7.3)0.008Brachial artery diameter (mm)3.8 (3.2–4.3)3.7 (3.3–4.5)0.781FMD after RH (%)2.7 (− 0.1 to 8.6)7.4 (2.7–9.7)0.047FMD after nitrate (%)18.4 (14.1–4.6)–Categorical variables: Absolute (n) and relative (%) frequency, Pearson’s Chi-square test. Continuous variables: Median (25th-75th percentile), Mann Whitney TestACEi angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, ASCVDR atherosclerotic cardiovascular disease risk, BMI body mass index, CAN cardiovascular autonomic neuropathy, CCB calcium channel blockers, eGFR estimated glomerular filtration rate, FMD flow-mediated vasodilation, HDLc HDL cholesterol, LDLc LDL cholesterol, NA not available, PWV pulse wave velocity, RH reactive hyperemia, T1D: type 1 diabetes, TG triglycerides, TC total cholesterol Forty-five proteins were selected and quantified in association with HDL2 and HDL3 of T1D and controls. From those, 18 were primarily associated with lipid metabolism [apo(a), apoA-I, apoA-II, apoA-IV, apoA-V, apoB, apoC-I, apoC-II, apoC-III, apoC-IV, apoD, apoE, apoF, apoM, CETP, LCAT, PCSK9, and PLTP], five related to acute inflammatory response (SAA1, SAA4, HP, HPHPR, and Orm1), one with complement system (C3), one antithrombotic (apoH), four antioxidants (APMAP, clusterin, PON1, and PON3), six transport proteins (ALB, GC, IGFALS, RBP4, TF, and transthyretin), five protease inhibitors (AHSG, AMBP, CST3, A1AT, and vitronectin), two enzymes not related with lipid metabolism (GPLD1 and PCYOX) and three with not well-known function (apoL-I, A1BG, and HBB) (Table 2). The differences between the abundance of proteins detected in HDL2 between T1D and controls are shown in the Volcano Plot (Fig. 1A). Ten proteins (APMAP, apoB, apoC-I, apoC-II, apoE, apoF, apoM, C3, GPLD1 and SAA4) were more abundant in HDL2 from T1D, and three (A1BG, apoC-III and HBB) in controls. The T1D was more associated with greater abundance of seven proteins (C3, apoE, APMAP, apoC-II, apoB, GPLD-1, and PLTP) (Fig. 1B). In the controls, the association was with four proteins (A1BG, transthyretin, HBB, and apoC-III) (Fig. 1C). Six proteins (APMAP, apoC-II, apoC-III, C3, HBB, and PLTP) were found to discriminate the HDL2 proteomics between T1D and controls. Thirty-three proteins were differentially expressed in HDL3 from T1D and controls (Fig. 1D), being 23 (APMAP, apoA-I, apoA-II, apoA-V, apoB, apoC-I, apoC-II, apoC-III, apoC-IV, apoD, apoE, apoL-I, apoM, CETP, GPLD1, HPHPR, PCSK9, PLTP, PON1, PON3, SAA1, SAA4, and TF) more abundant in T1D, and ten (A1BG, AHSG, ALB, apoF, apoH, CST3, GC, HBB, RBP4, and transthyretin) in controls. T1D was more associated with the abundance of 17 proteins (apoM, apoC-I, apoC-III, SAA4, apoC-II, HPHPR, PLTP, PCSK9, GPLD1, SAA4, apoB, TF, CETP, apoA-I, apoC-IV, LCAT, and APMAP) (Fig. 1E), and the controls with seven proteins (HBB, ALB, apoF, GC, A1BG, apoH, and CST3) (Fig. 1F). Nonetheless, only apoA-I, HBB, APMAP, LCAT, and PCSK9 had discriminatory capacity in HDL3 proteomics between groups. Table 2Proteins evaluated in HDL in type 1 diabetes and controls, divided by their main functionsAbbreviationProtein nameGeneLipid metabolism Apo(a)Apolipoprotein (a)LPA ApoA-IApolipoprotein A-IAPOA1 ApoA-IIApolipoprotein A-IIAPOA2 ApoA-IVApolipoprotein A-IVAPOA4 ApoA-VApolipoprotein A-VAPOA5 ApoBApolipoprotein BAPOB ApoC-IApolipoprotein C-IAPOC1 ApoC-IIApolipoprotein C-IIAPOC2 ApoC-IIIApolipoprotein C-IIIAPOC3 ApoC-IVApolipoprotein C-IVAPOC4 ApoDApolipoprotein DAPOD ApoEApolipoprotein EAPOE ApoFApolipoprotein F or lipid transfer inhibitor proteinAPOF ApoMApolipoprotein MAPOM CETPCholesteryl ester transfer proteinCETP LCATLecithin-cholesterol acyltransferaseLCAT PCSK9Proprotein convertase subtilisin/kexin type 9PCSK9 PLTPPhospholipid transfer proteinPLTPAccute inflammatory response SAA1Serum amyloid A type 1SAA1 SAA4Serum amyloid A type 4SAA4 HPHaptoglobinHP HPHPRHaptoglobin or haptoglobin related proteinHP/ HPR Orm1Alpha-1 glycoprotein 1 or orosomucoidORM1 VTNVitronectinVTNComplement system C3Complement C3C3Antithrombosis ApoHApolipoprotein H or beta-2-glycoprotein 1APOHAntioxidant APMAPAdipocyte plasma membrane-associated proteinAPMAP ApoJ/ CLUClusterin, apolipoprotein J or sulfated glycoprotein 2CLU Pon1Paraoxonase-1PON1 Pon3Paraoxonase-3PON3Transport proteins AlbAlbuminALB GCVitamin D binding protein or Group-specific componentGC HBBHemoglobin subunit betaHBB IGFALSAcid-labile subunitIGFALS RBP4Retinol binding protein type 4RBP4 TFSerotransferrinTF TTRTransthyretinTTRProtease inhibitors A1ATAlpha-1 antitrypsinSERPINA1 AHSGAlpha-2-Heremans-Schmid glycoprotein or fetuin-AAHSG AMBPAlpha-1 microglobulin bikunin precursorAMBP CST3Cystatin CCST3 VTNVitronectinVTNEnzymes (do not involved in lipid metabolism) GPLD1Phosphatidylinositol-glycan-specific phospholipase DGPLD1 PCYOXPrenylcysteine oxidase 1PCYOX1Other proteins ApoL-IApolipoprotein L-IAPOL1 A1BGAlpha-1-B glycoproteinA1BGForty-five proteins were selected and quantified in HDL2 e HDL3 in T1D and controls and were divided by major functionsFig. 1Differences in HDL2 and HDL3 protein abundance between T1D and controls. Volcano Plot indicating differences of protein abundance (log2 fold change) detected in HDL2 (A) and HDL3 (D) (significance in −log10) and odds ratio with $95\%$ confidence interval for the association of HDL2 and HDL3 proteomics in T1D (B for HDL2 and E for HDL3) and controls (C for HDL2 and F for HDL3). Proteins are indicated by gene name abbreviation. AHSG alpha-2-Heremans-Schmid glycoprotein or fetuin-A, ALB albumin, APMAP adipocyte plasma membrane-associated protein, apoH apolipoprotein H or beta 2 glycoprotein 1, A1BG glycoprotein alpha-1B, CETP cholesteryl ester transfer protein, CST3 cystatin C, GC vitamin D binding protein or group-specific component, GPLD1 phosphatidylinositol-glycan-specific phospholipase D, HBB hemoglobin subunit beta, HPHPR haptoglobin or haptoglobin related protein, LCAT lecithin cholesterol acyltransferase, n.s. not significant, PCSK9 proprotein convertase subtilisin/kexin type 9, PLTP phospholipid transfer protein, PON1 paraoxonase-1, PON3 paraoxonase-3, RBP4 retinol binding protein type 4, SAA1 serum amyloid A1, SAA4 serum amyloid A4, TF Transferrin, TTR transthyretin In HDL2 of T1D, the amount of apoB and Lp(a) was negatively correlated, while PLTP, PON1, and A1AT were positively correlated with plasma HDLc. In controls, apoF presented a negative correlation, and apoA-I, GPLD1, PLTP, and PON1 had a positive association with HDLc. Only two proteins presented a similar correlation in both groups (PLTP and PON1). For HDL3, the abundance of three proteins (C3, IGFALS, and PON1) was positively correlated with HDLc of T1D. In controls, three proteins in HDL3 (AMBP, apoF and apoH) were negatively correlated with HDLc; while five proteins (C3, clusterin, IGFALS, LCAT, and PON1) were positively correlated. Only two proteins presented a similar correlation in both groups (C3 and IGFALS). PON1 correlated well in controls, but not in T1D (data not shown).
APMAP, apoB, apoC-I, C3, and SAA4 in HDL2 was greater in T1D with HbA1c ≥ $8.5\%$, while IGFALS was less abundant in those subjects. In HDL3, 12 proteins (APMAP, apoA-I, apoA-II, apoA-V, apoC-I, apoC-II, apoC-III, apoD, apoM, HPHPR, PCSK9, and SAA4) were more abundant in individuals with HbA1c ≥ $8.5\%$, while the content of eight proteins (A1AT, A1BG, Alb, apoF, GC, HBB, RBP4, and transthyretin) was greater in T1D with HbA1c < $8.5\%$ (Additional file 1: Table S1). Regarding the categorization by statin use, a higher abundance of C3, HBB and apo(a), and a lower abundance of apoM, IGFALS, and PON3 in HDL3 was observed (Additional file 1: Table S1).
The presence of CAN, assessed in T1D, was associated with a higher abundance of AMBP, ApoB, Lp(a), Orm1, and transthyretin in HDL2, and lower content of clusterin, HBB, and PON3 in HDL3 (data not shown). A positive correlation was observed between A1BG content in HDL2 with ten-year ASCVDR assessed by the QRISK3; AMBP was positively, and apoE in HDL2 negatively correlated with both QRISK3 and Steno T1 Engine (Additional file 1: Table S1). Also, the ten-year ASCVDR by Steno T1 Engine showed a positive correlation with AHSG, AMBP, and apoH in HDL3, and a negative correlation with apoC-I, apoE, apoL-I, and HPHPR. The ten-year ASCVDR calculated by QRISK3 presented a positive correlation with eight proteins in HDL3 (A1BG, AHSG, ALB, AMBP, apoA-IV, apoH, RBP4, and transthyretin) and negative with apoC-I, apoE, apoL-I, and apoM (data not shown).
Thirty individuals in T1D and 30 in controls underwent PWV and post-reactive hyperemia FMD tests. T1D presented higher PWV than controls. In the assessment of endothelial function, the basal diameter of the brachial artery was similar, but FMD after reactive hyperemia was lower in the T1D than controls (Table 1). In both groups, PWV, but not FMD, correlated with ten-year ASCVDR by Steno T1 Engine Risk and QRISK3. CAN and HDLc were not associated with changes in vascular function.
In T1D, six proteins in HDL2 (apoL-I, A1BG, apoA-II, apoB, apo(a),and SAA4) positively correlated with PWV while PCSK9 and clusterin presented an inverse association. Post-RH FMD was positively correlated with apoA-I and PCYOX. In the controls, HBB and Orm1 showed a negative correlation with PWV, and post-RH FMD was not related to any protein associated with HDL2. After nitrate, A1AT, apoA-IV, apoF, clusterin, PCSK9, and vitronectin negatively correlated with FMD, while IGFALS directly related to FMD in T1D. In the controls, a negative association was observed between HBB and Orm1 with PWV, with no association of any protein with FMD (Table 3).Table 3Correlation between vascular function and HDL2 and HDL3 proteomics in subjects with T1D and controlsPWVFMD after reactive hyperemiaFMD after nitrateHDL2HDL3HDL2HDL3HDL2HDL3T1D A1AT− 0.38 (0.04)− 0.47 (0.01) A1BG0.49 (< 0.01) ApoA-I− 0.38 (0.04)0.41 (0.02) ApoA-II0.49 (< 0.01) ApoA-IV− 0.40 (0.04) ApoB0.41 (0.03) ApoE− 0.40 (0.03) ApoF− 0.44 (0.02) ApoL-I− 0.60 (< 0.01)− 0.44 (0.02) C3− 0.48 (0.01) CLU− 0.39 (0.04)− 0.51 (< 0.01)− 0.39 (0.04) HBB− 0.38 (0.04) GPLD1− 0.42 (0.02) IGFALS− 0.47 (0.01)0.43 (0.03)LCAT− 0.49 (< 0.01) Lp(a)0.46 (0.01) PCSK90.51 (< 0.01)− 0.43 (0.03) PCYOX0.36 (0.05) PON1− 0.47 (0.01) SAA40.43 (0.02) VTN− 0.39 (0.04)Controls ApoC- IV− 0.36 (0.05)CETP0.38 (0.04)− 0.37 (0.05) HBB− 0.38 (0.04) Orm1− 0.39 (0.03) RBP40.38 (0.04)Spearman’s correlation, expressed as a correlation coefficient (r) and the significance of the correlation is expressed as a p value, in parentheses. A1AT alpha-1 antitrypsin, A1BG A1BG alpha-1-B glycoprotein, Apo apolipoprotein, C3 complement C3, CETP cholesteryl ester transfer protein, CLU clusterin or apolipoprotein J, FMD flow-mediated vasodilation, GPLD1 phosphatidylinositol-glycan-specific phospholipase D, HBB hemoglobin subunit beta, IGFALS acid-labile subunit, LCAT lecithin cholesterol acyltransferase, Lp(a) apolipoprotein(a), Orm1 alpha-1 glycoprotein 1 or orosomucoid, PCSK9 proprotein convertase subtilisin/kexin type 9, PCYOX prenylcysteine oxidase 1, PON1 paraoxonase-1, PWV pulse wave velocity, RBP4 retinol binding protein type 4, SAA4 serum amyloid A type 4, T1D type 1 diabetes, VTN vitronectin In HDL3 of T1D, nine proteins (clusterin, A1AT, apoA-I, apoE, apoL-I, complement C3, HBB, IGFALS, LCAT, and PON1) were inversely related to PWV. FMD after RH and nitrate were inversely correlated with GPLD1. In the controls, apoC-IV was negative, while CETP and RBP4 positively correlated with PWV. Regarding post-RH FMD, a negative association was observed with CETP (Table 3). CAN was associated with the higher abundance of AMBP, apoB, Lp(a), Orm, and transthyretin in HDL2, and lower content of clusterin, HBB, and PON3 in HDL3. In HDL2, A1BG presented a positive correlation with QRISK3; while and AMBP and apoE, respectively, had a positive and a negative correlation with QRISK3 and Steno 1 Engine. Regarding HDL3, AHSG, AMBP, and apoH positively correlated, while apoC-I, apoE, apoL-I e HPHPR, negatively correlated with Steno T1 Engine. For QRISK3, the positive association was observed with A1BG, AHSG, ALB, AMBP, apoA-IV, apoH, RBP4, and transthyretin) and a negative correlation with apoC-I, apoE, apoL-I, and apoM.
The percentage of 6-h cholesterol efflux from macrophages was similar between T1D [HDL2: $24.1\%$ (18.0–30.2); HDL3: $18.8\%$ (14.8–24.4)] and controls [HDL2: $22.1\%$ (18.5–25.7); HDL3: $18.5\%$ (16.1–22.6)]. It was not observed association between clinical variables with cholesterol efflux capacity, except for an inverse correlation between the percentage of HDL2-mediated efflux with albuminuria in T1D (r = − 0.339; $$p \leq 0.02$$). Three proteins in HDL3 of T1D negatively correlated with cholesterol efflux: A1AT (r = − 0.33; $$p \leq 0.03$$), GC (r = − 0.32; $$p \leq 0.03$$), and TF (r = − 0.31; $$p \leq 0.04$$). In HDL3 of controls, eight were negatively correlated with cell cholesterol efflux: A1BG (r = − 0.41; $$p \leq 0.03$$), AHSG (r = − 0.48; $$p \leq 0.01$$), apoF (r = − 0.46; $$p \leq 0.01$$), apoH (r = − 0.46; $$p \leq 0.01$$), CST3 (r = − 0.38; $$p \leq 0.04$$), GC (r = − 0.46; $$p \leq 0.01$$), RBP4 (r = − 0.434; $$p \leq 0.02$$), and transthyretin (r = − 0.60; $$p \leq 0.01$$). Only RBP4 in HDL2 of controls positively correlated with cell cholesterol removal ($r = 0.46$; $$p \leq 0.02$$).
## Discussion
Subclinical atherosclerosis is more frequently observed in T1D subjects including increased intima-media thickness, artery calcification, and abnormal vascular function, namely PWV and FMD [32–35]. Nonetheless, the mechanisms involved in the evolution to clinical established CVD and possible markers are not well known. HDL protein signature may provide new insights on the modulation of HDL functionality that may not be evident by the classical metrics of this lipoprotein in plasma. In the present investigation, it was demonstrated a remodeling in HDL proteomics in T1D as compared to controls which related to markers of subclinical atherosclerosis.
T1D presented similar concentrations of plasma HDLc and lower levels of TG, LDLc, and TC as compared to controls. On the other hand, subclinical atherosclerosis markers, such as greater PWV and reduced FMD, were greater in T1D. Most studies that evaluated arterial stiffness in DM were conducted in T2D and those with T1D were carried out mainly in children, with different results and assessment methods [14, 34, 36]. Increased arterial stiffness observed in T1D corroborates other studies carried out with individuals of similar ages that also showed an association of higher PWV with inflammatory biomarkers in T1D [37]. The concomitance of other cardiovascular risk factors, such as hypertension and dyslipidemia, was associated with greater arterial stiffness (PWV), but not with endothelial dysfunction (FMD). Interestingly, glycemic control was not associated with abnormal vascular function, although it is noted to mention that the current investigation is a cross-sectional study, and HbA1c levels represent the mean glycemic rate for the last three months. Both the increased values of HbA1c and serum fructosamine in individuals with T1D suggest that there were no acute changes in glycemic control. CAN that may affect up to $30\%$ T1D individuals [38, 39] was not associated with vascular test results, although Liatis et al. [ 40] demonstrated that cardiovascular autonomic function, especially parasympathetic activity, is related to arterial stiffness in individuals with T1D. The two cardiovascular risk assessment tools validated in T1D—Steno T1 Engine and QRISK3—indicated an association between increased PWV and reduced FMD with higher ten- year ASCVDR [37].
HDLc were not associated with vascular dysfunction in T1D reinforcing the hypothesis that HDLc may not represent the best prediction for CVD in DM. The proteome analysis revealed differences in abundance of 13 peptides in HDL2, and 33 in HDL3 between the T1D and controls. A study demonstrated a distinct profile of proteins carried by total HDL fraction in T1D as compared to controls, including apoA-IV, apoE, and apoD, fibrinogen, albumin, and others, together with irreversible post-translational modifications such as oxidation, deamidation, and glycation. Those changes were related to a reduced ability of serum from subjects with T1D in removing cholesterol from macrophages as well as a diminished antioxidant capacity of HDL from subjects with T1D, independently of the glycemic control and HDLc [13]. In another case–control study, a labeled-free SWATH peptide quantification revealed 78 proteins bound to HDL that were differentially expressed in youth people with T1D in comparison to controls although no association with HDL functionality (3H-cholesterol efflux analysis) was made [14]. In a subgroup of subjects enrolled in Diabetes Control and Complications Trial/Epidemiology of Diabetes Intervention and Complications Study (DCCT/EDIC), 46 proteins were quantified in HDL by isotope dilution tandem-mass spectrometry, and after stringent analysis, eight proteins were associated with albuminuria. Particularly, PON1 in HDL was inversely correlated with coronary calcium score, and positively with albumin excretion rate [41]. Nine proteins in HDL2, and 22 in HDL3 whose difference in abundance were observed in the present investigation were not reported in the two case–control studies described above. The AMBP, albumin, and apoA-II were in agreement with at least one other study. ApoA-IV, apoH, A1BG, and C3 were also found in at least one of the two studies and had a significant difference between the groups, but the result was not in agreement regarding the group with greater abundance [14, 15].
Many proteins directly involved in lipid metabolism presented a greater content in HDL subfractions in T1D and may represent interplay of metabolic pathways that modulate lipid metabolism in T1D. Noteworthy that the abundance of PLTP, apoA-I, LCAT and PCSK9 had a discriminatory power between T1D and controls. PLTP is responsible for HDL remodeling and elevated PLTP activity is reported in T1D being considered as a contributor for enhancing apoA-I in fused HDL particles. ApoA-I mediates many atheroprotective actions of HDL, including RCT, antioxidative and anti-inflammatory activities, although increased modification of apoA-I by glycation, oxidation and others impairs its functionality contributing to HDL loss of function in diabetes [42]. The LCAT activity in T1D is reported as increased according to hyperglycemia [43] or unchanged [44] and its role in atherogenesis is controversial. Shao et al. [ 41] demonstrated that LCAT was inversely related to coronary calcium score in T1D individuals which agrees with the negative correlation observed between this enzyme with PWV observed in the present investigation. Interestingly, PCSK9 was more abundant in HDL3 of T1D especially in those with HbA1c values ≥ $8.5\%$ and its amount in HDL2 was associated with endothelial dysfunction. Plasma levels of PCSK9 were found increased in T1D subjects, which correlated with glycemic control [45]. Moreover, PCSK9 was associated with smaller HDL particles in T1D individuals with a poor glycemic control, although its role in HDL is not well established [46]. Other proteins related to lipid metabolism abundant in HDL subfractions of T1D included apoA-II, apoC-III, apoA-V, and apoD apoC-I, and apoC-II, that correlated with glycemic control, and apoA-IV, inversely correlated with PWV. Interestingly, apoC-III was greatly expressed in HDL2 of controls and HDL3 of T1D subjects. This apolipoprotein reflects insulin sensitivity and negatively modulates LPL activity driving plasma levels of triglycerides and being a target of drugs for hypertriglyceridemia control. Moreover, it drives VLDL secretion and relates to inflammatory processes in the vasculature and in the pancreas. In the present investigation, the differential expression of apoC-III in HDL was not correlated to HDL functionality (14C-cholesterol efflux analysis) or vascular tests, which deserves further exploration.
Acute inflammatory proteins such as SAA1, SAA4 that presented a discriminatory power between both groups were related by others to the impairment in RCT and anti-inflammatory activity of HDL [6, 47, 48]. Complement C3 in HDL3 that in the present study was inversely related to PWV, was described as enhanced in the HDL proteome of subjects with established CVD [49] and elevated in the HDL proteome in T1D [11]. PON1 and PON3 were higher in HDL3 being PON1 inversely related to PWV. In the DCCT/ EDIC those enzymes were inversely related to calcium coronary score [43].
Curiously, the hemoglobin subunit beta (HBB) in both HDL2 and HDL3 had a discriminatory capacity for T1D and controls, being HBB reduced in T1D and with a negative correlation with PWV. APMAP was also discriminatory but more abundant in T1D with worse glycemic control. Finally, the clusterin content in HDL2 and HDL3 was positive and negative correlated with, respectively, PWV and FMD. Moreover, clusterin was negatively correlated with CAN, agreeing with its role in preventing familial amyloidotic polyneuropathy [50].
The PRM analysis is a very sensitive methodology making detectable the presence of very small amounts of proteins such as apoB and apo(a) in HDL, as the hydrated density of large HDL2 is similar to the densities of lipoprotein (a) and LDL [27].
Reduced cholesterol efflux was shown in T1D [13, 14], although Vaisar et al. [ 49] did not find a difference in cholesterol removal comparing subjects with T1D with and without vascular complication. One study showed that improved glycemic control is associated with increased cholesterol efflux in T1D [51]. In this investigation, both HDL2 and HDL3-mediated cholesterol efflux was similar between T1D and controls and not related to vascular function tests.
## Study strengths and limitations
This is the first demonstration of an individualized and robust proteomic analysis in HDL subfractions with a large number of individuals in a case–control study dealing with T1D subjects. Results can help to better explore HDL proteomics and its predictive role in subclinical atherosclerosis in T1D. Limitations include the fact that the cross-sectional design makes it difficult to draw conclusions about the cause-effect relationship among variables. Considering the heterogeneity of HDL composition and different pathways involved in its generation and catabolism, it is conceivable that many endogenous and exogenous factors may modulate this lipoprotein structure and ultimately function. Hypertension and use of statins were present only in the T1D that could interfere in the case–control study. Moreover, cholesterol efflux to HDL subfractions was determined in a single period of time, which may compromise the inference of HDL functionality over time.
## Conclusions
This study detected differences in HDL proteomics between individuals with T1D and controls that had not been found in previous studies. Nine proteins in HDL2, and 22 in HDL3 were detected, whose observed abundance differences were not reported in other case–control studies. Vascular tests corroborate previous studies in individuals with T1D that indicated worse markers of subclinical atherosclerosis and higher estimated cardiovascular risk than individuals without diabetes, even with an apparently better serum lipid profile in individuals with T1D. The association of clinical and laboratory variables, HDL proteomics data and results of vascular function tests and HDL functionality made it possible to explore the various atheroprotective functions of HDL. In addition to proteins involved in lipid metabolism, HDL carries acute-phase inflammatory, complement system, antithrombotic, antioxidant, protease inhibitor and transporter proteins, with significant differences between T1D and controls.
## Supplementary Information
Additional file 1: Table S1. Proteomics of HDL2 e HDL3 according to A1c, statin use and cardiovascular autonomic neuropathy in type 1 diabetes.
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|
---
title: Glycyrrhizic acid inhibits myeloid differentiation of hematopoietic stem cells
by binding S100 calcium binding protein A8 to improve cognition in aged mice
authors:
- Xue Gong
- Haitao Shen
- Liuling Guo
- Ce Huang
- Tingting Su
- Hao Wang
- Shengyu Feng
- Shanshan Yang
- Fenjiao Huo
- Haifeng Liu
- Jianbo Zhu
- Jian-Kang Zhu
- Hongbin Li
- Hailiang Liu
journal: 'Immunity & Ageing : I & A'
year: 2023
pmcid: PMC10007777
doi: 10.1186/s12979-023-00337-9
license: CC BY 4.0
---
# Glycyrrhizic acid inhibits myeloid differentiation of hematopoietic stem cells by binding S100 calcium binding protein A8 to improve cognition in aged mice
## Abstract
### Background
Glycyrrhizic acid (GA), a saponin compound often used as a flavoring agent, can elicit anti-inflammatory and anti-tumor effects, and alleviate aging. However, the specific mechanism by which GA alters immune cell populations to produce these beneficial effects is currently unclear.
### Results
In this study, we systematically analyzed single-cell sequencing data of peripheral blood mononuclear cells from young mice, aged mice, and GA-treated aged mice. Our in vivo results show that GA reduced senescence-induced increases in macrophages and neutrophils, and increased numbers of lymphoid lineage subpopulations specifically reduced by senescence. In vitro, GA significantly promoted differentiation of Lin−CD117+ hematopoietic stem cells toward lymphoid lineages, especially CD8+ T cells. Moreover, GA inhibited differentiation of CD4+ T cells and myeloid (CD11b+) cells by binding to S100 calcium-binding protein 8 (S100A8) protein. Overexpression of S100A8 in Lin− CD117+ hematopoietic stem cells enhanced cognition in aged mice and the immune reconstitution of severely immunodeficient B-NDG (NOD.CB17-Prkdcscid/l2rgtm1/Bcgen) mice.
### Conclusions
Collectively, GA exerts anti-aging effects by binding to S100A8 to remodel the immune system of aged mice.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12979-023-00337-9.
## Background
Aging manifests itself in cells, tissues, organs, and the whole animal body, ultimately leading to a decline in body function and dysregulation of homeostasis. Senescence contributes to cell cycle arrest and produces a senescence-associated secretory phenotype (SASP) [1]. SASP factors recruit immune cells including macrophages, neutrophils, natural killer (NK) cells, and T cells to promote inflammatory responses [2]. SASP transmits senescence to adjacent cells and some upregulated inflammatory factors have a significant destructive effect on health organisms [3], or even trigger fatal degenerative diseases, such as Alzheimer's disease [4], diabetes [5], and cancer [6]. Mice exhibited signs of premature aging when only the immune system was allowed to age, and transplantation of young immune cells to prematurely aged mice produced a correspondingly delayed aging phenotype [7]. Release of the calreticulin-like protein grancalcin by senescent immune cells promotes skeletal aging, which reduces the quality of life of elderly individuals [8]. Moreover, the accumulation of age-related senescent cells shortens the lifespan and health of mice [9]. Both healthy long-lived seniors and long-lived mice exhibit good immune function, with immune cells displaying adequate oxidative and stress responses, and low expression of pro-inflammatory genes [10, 11]. Substantial evidence indicates that the immune system has important roles in aging and is inextricably linked to age-related diseases.
Glycyrrhizic acid (GA), an oleanolane-type pentacyclic triterpenoid saponin compound [12], is often used as a flavoring in chocolate, chewing gum, some alcoholic beverages, and cigarettes. It can constitute up to $15\%$ of licorice root and is one of the most important active ingredients in licorice [13]. GA has anti-inflammatory, anti-tumor, antioxidant, antiviral, antibacterial, and immunomodulatory activities [14–17]. In addition, GA has good anti-inflammatory effects on inflammatory responses caused by age-related diseases. Postoperative cognitive impairment produces neuroinflammatory and Alzheimer's disease-related pathological conditions. Cognitive impairment of rats was improved by pretreatment with GA before surgery, as were neuroinflammation [expression of interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and nuclear factor κB (NF-κB)] and Alzheimer's disease-related pathology (Tau phosphorylation of AT-8, Ser369, and Aβ40–42) in the hippocampal region [18]. GA has shown good neuroprotective effects in neurodegenerative diseases and can improve cognition. So, is there some inextricable link between GA and enhancement of healthy aging? *Our previous* transcriptomic profiling of the blood of GA-treated aging female C57BL/6 mice showed that GA slowed aging associated with hematopoietic cell lineages. Moreover, GA could improve the cognitive ability of mice by increasing numbers of B and T cells, as verified in severely immunodeficient mice [19]. Therefore, this study further investigated the molecular mechanism by which GA improves cognition through the immune system to retard aging. Our findings provide a theoretical basis for applying small molecules and Traditional Chinese Medicine to the development of anti-aging drugs.
## Glycyrrhizic acid improved senescence-induced lymphocytopenia and myeloid cell increases
To investigate the effects of GA on the immune system-especially immunosenescence-during aging, we performed single-cell sequencing analysis of peripheral blood mononuclear cells (PBMCs) from 8-week-old (young), 16-month-old (aged), and GA-administered 16-month-old (aged-GA, 5 mg/kg every 2 d, tail vein injection, lasts 30 days) mice (Fig. 1A). Sequencing data were processed by CellRanger software. Cells from individual samples were collected, and a total of 30,124 single cells (Young, 7577; Aged, 10981; Aged-GA, 11566) were collected for subsequent analysis. Using uniform manifold approximation and projection (UMAP), different immune cell lineages were distinguished and identified according to expression of typical marker genes, namely: T cells, B cells, NK cells, macrophages, neutrophils, brush cells (columnar cells without wireless hairs), erythrocytes, and cup cells, whereby subpopulations of the same cell type were divided according to varying expression of different marker genes. PBMCs from young and aged mice were subjected to single-cell clustering analysis to evaluate changes in immune cell proportions during aging (Fig. S1A-D). Compared with young mice, aged mice had fewer T cells (Igfbp4 and Cd8b1 as marker genes), basal cells (Dapl1), B cells (Iglc1), NK cells (Gzma), and macrophages (Ifit3); and more erythrocytes (Hba-a1 and Hbb-bt), neutrophils (S100A8), macrophages (Lyz2, Cst3, and S100A8), brush cells (Ccl4 and Cst3), and cupped cells (Igha). Moreover, the results show increased proportions of senescent myeloid cells and decreased proportions of senescent lymphocytes in aged mice. In particular, T cells expressing Igfbp4 and Cd8b1 as marker genes (Clusters 1 and 2, respectively), and basal cells with Dapl1 as a marker gene (Cluster 7) were mainly enriched for genes associated with the T-cell receptor signaling pathway; Th1, Th2, and Th17 cell differentiation; and gap junctions (Fig. S1E-G).Fig. 1Single-cell clustering analysis of PBMCs from mice to evaluate changes in immune cell proportions during GA treatment. A Schematic of the experimental design for single-cell sequences. PBMCs collected from young ($$n = 5$$), aged ($$n = 5$$), and aged + GA ($$n = 5$$) mice were processed by scRNA-seq using a 10 × genomics platform. B Clustering of single cells in aged mice. C Clustering of single cells in aged + GA mice. D Comparison of clustering of single cells in aged and aged + GA mice. E Relative population abundances of cell types in aged and aged + GA mice. F KEGG analysis of differentially expressed genes in Cluster 0 (B cells, Cd74 as marker gene). G KEGG analysis of differentially expressed genes in Cluster 8 (T cells, Ccl5 as marker gene). H KEGG analysis of differentially expressed genes in Cluster 9 (T cells, Cd8b1 as marker gene) Single-cell clustering of PBMCs from aged and aged-GA mice to evaluate changes in immune cell proportions revealed that GA treatment increased T cells (Cd8b1 and Igfbp4 as marker genes), B cells (Slfn, Zbtb20, and Cd74), and NK cells (Gzma); and decreased macrophages (Lyz2 and Gngt2), brush cells (Ccl4, Cst3, and IL1b), erythrocytes (Hbb-bt), cupped cells (Igha), and neutrophils (Ngp) (Fig. 1B-E). Major pathways enriched in B cells (Cd74, Cluster 0), T cells (Ccl5, Cluster 8; and Cd8b1, Cluster 9) included B cell and T cell receptor signaling pathways; and Th1, Th2, and Th17 cell differentiation (Fig. 1F-H). Additionally, following GA treatment, cells were analyzed using Monocle to construct proposed temporal developmental trajectories (darker colors represent earlier development with more posterior temporal differentiation). Macrophages, neutrophils, and brush cells were proposed to develop with anterior temporal differentiation, T cells with intermediate developmental temporal differentiation, and B cells with the latest developmental differentiation (Fig. S2A-C). Moreover, S100A8 was mainly expressed in Stage 1 in Clusters 9 (Brush cells), 12 (macrophages), and 15 (neutrophils) (Fig. S2D). These results, together with the previous comparison between young and aged mice, suggest that GA reduces the increase in macrophages and neutrophils caused by senescence, and increases numbers of T and B cells specifically reduced by senescence.
## Glycyrrhizic acid promoted differentiation of HSCs toward lymphocytes in vitro
Single-cell data revealed that GA increased numbers of lymphatic lineage cells and decreased numbers of myeloid cells. On the basis of blood RNA sequencing in mice, the effect of GA was mainly associated with differential expression of genes in hematopoietic cell lineages, especially B and T cells [19]. First, the ability of GA to increase numbers of B and T cells was verified in vitro using spleen cells (Fig. S3), which proved consistent with our previous results [19]. Next, we further confirmed that GA increased numbers of B and T cells by promoting differentiation of hematopoietic stem cells (HSCs) toward the lymphoid lineage. Lin−CD117+ HSCs were purified and directed to differentiate toward lymphoid lineages (B and T cells) in vitro using an OP9/OP9-DL1 cell co-culture system. A directed B-cell differentiation model was established by co-culturing Lin−CD117+ HSCs with OP9 cells for 12 d (Fig. 2A). Subsequently, cells were analyzed by flow cytometry for markers of B cell (CD45R/B220+) and myeloid lineage cell (CD11b+) differentiation (Fig. 2B, C). The results show that GA promoted Lin−CD117+ HSC differentiation towards B cells; notably, although myeloid (CD11b+) cells were also produced in this co-culture system without the addition of myeloid differentiation factors, GA was able to inhibit myeloid cell production. A directed T cell differentiation model by co-culturing Lin−CD117+ HSCs with OP9-DL1 cells (Fig. 3A). T cells continued to differentiate into mature CD8+/CD4+ T cells after negative and positive selection for CD44 and CD25, respectively, and it was shown that a short period of directed T cell differentiation can only be observed in the DN phase [20] (Fig. 3B). Lin−CD117+ HSCs were co-cultured with OP9-DL1 cells for 12 d and then analyzed by flow cytometry for DN phase changes in T cells and myeloid (CD11b+) cell differentiation (Fig. 3C, D). The results show a significant decrease in the proportion of DN1-phase cells and significant increase in the proportion of DN2- and DN3-phase cells in the GA group, and no difference in DN4-phase cells between GA and control groups. These results suggest that GA drives DN phase changes of Lin−CD117+ HSCs during T-cell differentiation. Moreover, although myeloid (CD11b+) cells are also produced in this co-culture system without the addition of myeloid differentiation factors, the percentage of myeloid cells in the GA group was significantly reduced, indicating that GA suppressed myeloid cell production. The OP9/OP9-DL1 cell co-culture system inhibited production of myeloid (CD11b+) cells without the addition of myeloid differentiation factors. Fig. 2Glycyrrhizic acid promoted differentiation of Lin−CD117+ HSCs toward B cells in vitro. A Schematic design of Lin−CD117+ HSCs co-cultured with OP9 cells for directed differentiation to B cells. B Representative FACS plots of CD45R/B220+ B cells and CD11b+ cells following differentiation of Lin−CD117+ HSCs co-cultured with OP9 cells. C Bar graph of statistical results of CD45R/B220+ B cells and CD11b+ cells in co-cultures following differentiation of Lin−CD117.+ HSCs with OP9 cells. Data represent mean ± SEM. $$n = 3$$, **$P \leq 0.01$, *$P \leq 0.$ 05Fig. 3Glycyrrhizic acid promoted differentiation of Lin−CD117+ HSCs toward T cells in vitro. A Experimental design of Lin−CD117+ HSCs co-cultured with OP9-DL1 cells for short-term directed differentiation into T cells. B Schematic of the developmental maturation process of T cells by CD44 and CD25 negative–positive selection. C Representative FACS plots and bar graphs showing T-cell DN phase changes (DN1 phase, CD44+CD25−; DN2 phase, CD44+CD25+; DN3 phase, CD44−CD25+; DN4 phase, CD44−CD25−) following differentiation in co-cultures of Lin−CD117+ HSCs and OP9-DL1 cells. D Representative FACS plots and bar graphs of CD11b+ cells following co-culture differentiation of Lin−CD117+ HSCs and OP9-DL1 cells. Data represent mean ± SEM. $$n = 3$$, ***$P \leq 0.001$; ns, not significant vs. Ctrl To observe the differentiation of mature CD8+/CD4+ T cells, a long-term (28 d) Lin−CD117+ HSC directed T cell differentiation model was established in vitro (Fig. 4A). Lin−CD117+ HSCs were co-cultured with OP9-DL1 cells for 28 d and then analyzed by flow cytometry for T-cell DN phase and expression of mature T-cell markers (CD8+/CD4+) (Fig. 4B, C). The results show that there was no significant difference in DN1-phase cells between GA and control groups, the proportion of DN2-phase cells was significantly decreased in the GA group, and proportions of cells in DN3 and DN4 phases were significantly increased in the GA group. Moreover, among the mature CD4+/CD8+ T cells generated, GA was able to significantly increase the number of CD8+ T cells and significantly decrease the number of CD4+ T cells. These results suggest that GA promoted the differentiation of Lin−CD117+ HSCs toward B cells and CD8+ T cells, and inhibited their differentiation toward CD11b+ myeloid cells, consistent with the results of PBMC single-cell sequencing. Collectively, these findings demonstrate that GA can improve the differentiation bias of HSCs caused by aging. Fig. 4Glycyrrhizic acid promoted differentiation of Lin−CD117+ HSCs into CD8+ T cells in vitro. A Experimental design of Lin−CD117+ HSCs co-cultured with OP9-DL1 cells for long-term directed differentiation into T cells. B Representative FACS plots and bar graphs of T-cell DN phase changes (DN1 phase, CD44+CD25−; DN2 phase, CD44+CD25+; DN3 phase, CD44−CD25+; DN4 phase, CD44−CD25−; and DP phase, CD8+CD4+) in co-culture differentiation of Lin−CD117+ HSCs and OP9-DL1 cells. C Representative FACS plots and bar graphs of CD8+ T cells and CD4+ T cells in co-culture differentiation of Lin−CD117+ HSCs with OP9-DL1 cells. Data represent mean ± SEM. $$n = 3$$, *$P \leq 0.$ 05, **$P \leq 0.01$, ***$P \leq 0.001$; ns, not significant vs. Ctrl
## S100A8 protein is the target protein of GA
To determine which proteins are targeted by GA to act in immune cells, we performed a label-free quantitative (LFQ) proteomics study. We extracted and purified PBMC proteins and GA-interacting PBMC proteins, then analyzed them using nanoLC-MS/MS (mass spectrometry) (Fig. 5A). After subjecting mass spectrometry samples to quality control (Fig. 5B), we detected a total of 829 proteins, which were digested by trypsin to obtain 5319 peptides with an enzymatic efficiency of $97.37\%$. Among them, differentially expressed proteins were screened according to the following conditions: unique peptide ≥ 1, Foldchange > 1.2 or < 0.83, and P-value < 0.05. There were 300 differentially expressed proteins that met these criteria (Fig. 5C). The 50 most significant differentially expressed proteins were selected for heat map analysis. Some of these proteins are involved in activation of the natural immune system, including S100A8 (Fig. 5D). Because immune cells all develop from HSCs, RNA-seq analysis showed that S100A8 was one of several genes specifically upregulated in GA-treated Lin−CD117+ HSCs (Fig. S4A), which were mainly enriched in the IL-17 signaling pathway (Fig. S4B). Quantitative reverse transcription PCR (qRT-PCR) validation of differentially expressed genes S100A8 and S100A9 showed expression patterns consistent with RNA sequencing results of Lin−CD117+ HSCs (Fig. S4C, D). The results of single-cell sequencing of PBMCs shows that the mRNA expression level of S100A8 continuously increased with age and was reduced by GA treatment (Fig. S5). RNA sequencing results of the blood of mice treated with GA via tail vein injection showed an expression trend of S100A8 [19] consistent with that of single cells. All of these results suggest that S100A8 is a likely target protein of GA. To further verify that GA bound to S100A8 protein, we analyzed this binding by surface plasmon resonance (SPR) molecular interactions. The experimental results show binding between GA and S100A8 protein (Fig. 5E).Fig. 5S100A8 protein binds GA. A Label-free quantitative (LFQ) proteomics study. Schematic diagram of experimental design for nanoLC-MS/MS analysis of GA and PBMC protein interaction. B QC plots for mass spectrometry samples, with no dispersion for each of the control and GA samples. C Differentially expressed peptides were screened according to the criteria: unique peptide ≥ 1, Foldchange > 1.2 or < 0.83, and P-value < 0.05. Three hundred differentially expressed proteins met these criteria. D Heatmap showing 50 most highly significant differentially expressed peptides in mass spectral results of GA versus control; S100A8 is the proposed binding protein for GA. E Binding affinity of SPR COOH and S100A8-GA interactions. S100A8 was immobilized on COOH chips with GA concentrations (from top to bottom) of 500, 250, 125, 62.5, and 0 μM. F AutoDock simulation of GA-S100A8 binding visualization map with hydrogen bond binding sites K84 and E69. G CB-Dock simulation of GA-S100A8 binding visualization map with hydrogen bond binding sites Y30, N67, and N25 We also simulated GA binding to S100A8 protein using CB-Dock and AutoDock simulation software. The protein structure of S100A8 was first predicted by homology modeling using SWISS-MODEL. The simulated protein structure of S100A8 protein and molecular structure of GA were loaded into CB-Dock and AutoDock for molecular docking. CB-Dock ran the docking program AutoDock Vina, and simulated binding results with high scores were selected according to Vina scores. In addition, AutoDock was able to identify binding sites with the highest affinity (according to binding energy) for the substrate and target protein with minimal global energy. The binding results with the highest affinity were visualized with PyMOL (Fig. 5G). Combining the results of these two-prediction software, we selected two possible binding sites near each other, N67 and E69, for further study. RNA-seq expression profiling analysis of MEFs overexpressing S100A8 (OEa8) or S100A8 with N67 and E69 point mutations (OEa8-67 and OEa8-69, respectively) revealed that OEa8-67 and OEa8-69 had 332 shared differential genes compared with OEa8. Gene Set Enrichment Analysis (GSEA) indicated significant enrichment of IL-17 signaling pathway genes among these differentially expressed genes (Fig. S6A) and indicated that these two amino acid sites were important for the function of S100A8 protein.
We also mutated S100A8 in MEFs with lentivirus-borne CRISPR/Cas9 technology and verified its mRNA expression level showed downregulation (Fig. S6B). RNA-seq expression profiling performed 48 h after adding GA showed that differentially expressed genes were mainly downregulated compared with control cells (Fig. S6C). Both KEGG pathway analysis showed enrichment of TNF-⍺ and IL-17 signaling pathways (Fig. S6D), while GSEA also indicated significant enrichment of the IL-17 signaling pathway (Fig. S6E). These results indicate that GA binds to S100A8 protein and the amino acid sites N67 and E69 are very important for S100A8 protein function.
## Overexpression of S100A8 inhibited differentiation of HSCs toward myeloid cells in vitro
To explore the role of S100A8 in HSC differentiation, we induced differentiation of Lin−CD117+ HSCs toward lymphoid lineage cells in the presence of S100A8 overexpression. Virus-infected Lin−CD117+ HSCs were co-cultured with OP9 cells for directed differentiation toward B cells (Fig. 6A) and myeloid lineage (CD11b+) cells. Differentiation was analyzed by flow cytometry after 6 d of co-culture (Fig. 6B, C). Myeloid lineage (CD11b+) cells were produced in this co-culture system without the addition of myeloid differentiation factors; however, there was a tendency for GA to suppress this differentiation, although this difference was not significant. Moreover, overexpression of S100A8 inhibited myeloid (CD11b+) cell differentiation to some extent. Virus-infected Lin−CD117+ HSCs were co-cultured with OP9-DL1 cells for directed T-cell differentiation (Fig. 6D), and myeloid (CD11b+) differentiation was analyzed by flow cytometry (Fig. 6E, F). Myeloid (CD11b+) cells were also produced in this co-culture system without the addition of myeloid differentiation factors, and GA inhibited myeloid production, consistent with the results described above for directed T-cell differentiation. Overexpression of S100A8 further inhibited myeloid (CD11b+) cell production. These results demonstrate a correlation between S100A8 and myeloid differentiation in that increasing S100A8 expression inhibited the differentiation of Lin−CD117+ HSCs toward myeloid cells. Fig. 6Co-culture differentiation of modified Lin−CD117+ HSCs with OP9/OP9-DL1 cells. A Experimental design of co-culture differentiation of Lin−CD117+ HSCs with OP9 cells for 6 d in LV201, LV201 + GA, OEa8 (LV201 + S100A8), and OEa8 + GA (LV201 + S100A8 + GA) groups. B Representative FACS plots of CD11b+ cells in co-cultures of Lin−CD117+ HSCs with OP9 cells in LV201, LV201 + GA, OEa8, and OEa8 + GA groups. C Bar graph showing CD11b+ cells in co-cultures of Lin−CD117+ HSCs with OP9 cells in LV201, LV201 + GA, OEa8, and OEa8 + GA groups. D Experimental design of co-culture differentiation of Lin−CD117+ HSCs with OP9-DL1 cells for 6 d in LV201, LV201 + GA, OEa8, and OEa8 + GA groups. E Representative FACS plots of CD11b+ cells in co-cultures of Lin−CD117+ HSCs with OP9-DL1 cells in LV201, LV201 + GA, OEa8, and OEa8 + GA groups. F Bar graph showing CD11b+ cells in co-cultures of Lin−CD117+ HSCs with OP9-DL1 cells in LV201, LV201 + GA, OEa8, and OEa8 + GA groups. Data represent mean ± SEM, $$n = 3$$, *$P \leq 0.05$
## Glycyrrhizic acid binding to S100A8 improved cognition through the immune system in mice
To observe the effect of GA binding to S100A8 through the immune system on the cognition of mice, we used lentivirally packaged Lin−CD117+ HSCs for immune reconstitution (IR) in severely immunodeficient B-NDG mice, and assessed the cognitive level of mice using open field test (OFT) and new object recognition (NOR) experiments. Lentiviral-packed Lin−CD117+ HSCs were administered to B-NDG mice by tail vein injection, and the IR and treatment groups were set up as follows: non-IR, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA (Fig. 7A). We analyzed percentages of CD3+ T cells and CD45R/B220+ B cells by flow cytometry to observe the effect of IR. The results show that B-NDG mice were successfully reconstituted in that T cells and B cells were significantly increased in the IR + PBS group compared with those in the control group. The GA treatment group showed increased numbers of T cells and B cells compared with the reconstituted control group, and the IR-OEa8 + PBS group also displayed increased numbers of T cells and B cells; the IR-OEa8 (PM69) + GA group was similar to the reconstituted control group in T cells and B cells (Figs. 7B and S7). On the basis of the times mice spent in the central area during the 15-min OFT (Fig. 7C), there was no significant difference between experimental groups, indicating that the mice did not develop anxiety. NOR, which uses the instinctive exploration of novel objects to detect the fineness and sensitivity of a mouse’s recognition memory, was performed in mice without depression. Using a discrimination index to quantify the cognitive level of mice (Fig. 7D) revealed that reconstituting the immune system could improve the cognitive level of mice. Moreover, GA further improved the cognitive level of mice on the basis of immune reconstruction. The cognitive level of mice overexpressing S100A8 was substantially improved, even higher than that of the GA administration group. However, mice overexpressing S100A8 with a point mutation (OEa8-PM69) that were treated with GA displayed significantly impaired cognitive levels. Collectively, these results suggest that GA can bind S100A8 to improve cognition through the immune system in all groups of mice without anxiety, and this effect is partially eliminated by mutating the GA binding site in S100A8.Fig. 7GA binding S100A8 improved cognitive levels of mice via the immune system. A Schematic design of immune reconstitution experiment in B-NDG mice with Lin−CD117+ HSCs, divided into the following groups: non-IR + PBS, IR + PBS (LV201 + PBS), IR + GA (LV201 + GA), IR-OEa8 + PBS (LV201-S100A8 + PBS), and IR-OEa8 (PM69) + GA (LV201-S100A8 (PM69) + GA. B Representative FACS plots and bar graphs showing CD3+ T cells in the spleens of mice in non-IR + PBS, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA groups. C Walking paths tracked by software in non-IR + PBS, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA groups of mice in the OFT. D Bar graphs showing times spent in the central area of the OFT in non-IR + PBS, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA groups. E Experimental design of NOR. Bar graph of cognitive index statistics of mice in non-IR + PBS, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA groups. Data represent mean ± SEM. non-IR + PBS, $$n = 4$$; IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA groups, $$n = 5$$, *$P \leq 0.$ 05, **$P \leq 0.01$, ****$P \leq 0.0001$; ns, not significant vs. Ctrl
## Discussion
GA has medicinal activities such as anti-inflammatory, anti-tumor, and immunomodulatory effects. In a study conducted by our group in the last 2 years, GA was found to improve aging and enhance cognition in aging mice by promoting the proliferation of B and T cells [19]. The role of GA in anti-aging was exhibited, but the mechanism underlying the anti-aging action of GA was still unclear. Sequencing the blood of aging mice revealed that the pathway through which GA acts is enriched in hematopoietic cell lineages, so are these increased numbers of B and T cells caused by HSC differentiation? Thus, we directed B- and T-cell directional differentiation in vitro using OP9 and OP9-DL1 co-culture system [20–22], respectively, and found that GA drives cell differentiation toward lymphoid lineages at the level of HSC differentiation. In the absence of added cytokines and culture conditions for differentiation into myeloid lineages, myeloid (CD11b+) cells were still produced, and GA inhibited the production of these cells. With age, the function of HSCs decreases and their potential for differentiation into blood cells of different lineages is biased, showing increased differentiation of myeloid cells and decreased differentiation of lymphocytes [23–25]. This was similarly verified in our 10× single-cell sequencing, wherein populations of T, B, and NK cells in the lymphocyte lineage were reduced in senescent mice compared with those in young mice, while populations of neutrophils and macrophages (myeloid cell lineages) were increased. The observed decrease in T, B, and NK cells and increase in neutrophils and macrophages were ameliorated after tail vein injection of GA, complementing our in vitro findings. " Immunosenescence" has become a hot angle in the study of aging. Robinson et al. selectively deleted Ercc1, which encodes a DNA repair protein, in mouse HSCs to construct a mouse model with only immune cell senescence, in which mice entered adulthood healthy and showed premature aging [26], further demonstrating that GA is able to retard aging through the immune system.
S100A8, also known as MRP-8, is a small 12-kDa protein from the S100 protein family. The known regulator of S100A8 is P53, which plays a decisive role as an inflammatory factor in the development of inflammation and tumors [27, 28]. Under physiological conditions, S100A8 exerts its biological function by forming S100A8/S100A8 homodimers in the cytoplasm through covalent bonding, or S100A8/S100A9 heterodimers with S100A9 in a Ca2+-dependent manner. Although Ca2+-dependent tetramer formation of S100A8 and S100A9 are essential for biological activity, homodimers rarely exist because of stability. Both S100A8 and S100A9 have helix-loop-helix motifs of charged amino acid residues, resulting in their high affinity for divalent ions. Thus, divalent ions alter the conformation of S100A8/S100A9, allowing them to perform their corresponding functions. S100A8/S100A9 can stimulate TNF-⍺ and IL-6 production in BV-2 microglia through ERK/NF-κB and JNK/NF-κB signaling pathways [29]. Among the multiple inflammatory pathways mediated by Toll-like receptor 4 and receptor for advanced glycation end-products (RAGE), S100A8/S100A9 plays an important role in protecting individuals from curative infections [30]. However, our findings reveal that GA can also bind to S100A8 protein. First, S100A8 was immobilized on a COOH chip, which captured S100A9 binding to S100A8 (Fig. S8A). However, GA affected the binding of S100A8 to S100A9 to some extent after first binding to S100A8 (Fig. S8B). Therefore, there is a competitive binding relationship between GA and S100A9 in binding of S100A8, and GA may play a biological function by blocking the binding of S100A8 to S100A9, although the specific mechanism needs further study.
In conclusion, mRNA expression of S100A8 in GA-treated normal HSCs is elevated and increases CD8+ T cells while decreasing CD4+ T cells. The absence of S100A8 in hematopoietic stem progenitor cells in a specific tumor microenvironment significantly diminished the number of CD8+ T cells [31] and affected the function of CD4+ T cells to some extent, which has some similarity with our results. Additionally, S100A8 can induce T-cell immune tolerance in specific tumor-bearing microenvironments, but no relevant studies have been able to demonstrate an effect of S100A8 on CD8+ T-cell chemotaxis and function. Deletion of S100A8 significantly increased numbers of myeloid-derived suppressor cells in bone marrow and spleen, even when not in specific tumor-bearing microenvironments but the natural state. In the first known S100A8−/− mouse, there was a significant increase in numbers of myeloid cells, including neutrophils, monocytes, and dendritic cells, distributed in the peripheral blood and bone marrow [32]. In acute myeloid leukemia, S100A8 not only inhibits S100A9-induced terminal differentiation of acute myeloid leukemia (AML) cells, but also favors AML growth. Moreover, S100A8-knckout mice exhibit significant increases in myeloid cells (especially neutrophils, monocytes, and their precursors) in the bone marrow [33]. Furthermore, evidence suggests that downregulation of S100A8 restored erythroid lineage differentiation in a mouse model of myelodysplastic syndrome [34]. Overexpression of S100A8 in HSCs, whereby modified HSCs are subject to long-term differentiation and can therefore only differentiate for short periods of time, we observed changes in proportions of CD11b+ cells, and overexpression of S100A8 protein reduced CD11b+ cell production. This evidence suggests that S100A8 is inextricably linked to myeloid cell generation.
## Animals
C57BL/6 mice (8W) were purchased from Shanghai SLAC Laboratory Animal (Shanghai, China). Aged C57BL/6 mice (16 months) were from laboratory-reared reserves. B-NDG severely immunodeficient mice (8 weeks) were purchased from Beijing Biocytogen (Beijing, China). All mice used in experiments were female and housed in the specific-pathogen-free-grade laboratory animal center of Tongji University School of Medicine. Mice were housed five per cage, maintained on a 12-h light/dark cycle, at an appropriate temperature, with free access to water and food. Animal care and procedures for this study were in accordance with institutional guidelines and the Animal Welfare Act, and all protocols involving experimental animals were approved by Experimental Animal Ethics Committee of Tongji University School of Medicine. Mice of equal weights in each age group were randomly grouped. GA was administered every other day by tail vein injection, and the same dose of phosphate-buffed saline (PBS) was injected into the tail vein of the control group.
## 10 × Single-cell sequencing
PBMCs were isolated from the blood of mice. Cell viability was examined under a microscope with $0.4\%$ trypan blue staining. When the survival rate of cells reached $80\%$ or more, library construction experiments were performed. Single-cell libraries were constructed using Chromium™ Controller and Chromium™ Single Cell 3ʹ Reagent Version 3 Kit (10 × Genomics, Pleasanton, CA). Single cells, reagents, and gel beads were enclosed in “gel beads in emulsion” (GEMs). Lysis and barcoded reverse transcription of single-cell polyadenylate mRNA was performed within each GEM. RT-GEMs were cleaned up to amplify cDNA, which was subsequently fragmented. Fragmented ends were repaired and A-tailing was added at the 3' end. Aptamers were ligated to fragments that were screened by two-sided solid-phase reversible immobilization (SPRI). After sample index PCR, another two-sided SPRI screen was performed. The final library was checked for fragment size distribution using an Agilent 2100 Bioanalyzer (Santa Clara, CA) and the library was quantified using real-time quantitative PCR with TaqMan probes. Finally, sequencing was performed using the DNBseq platform (BGI Group, Shenzhen, China).
## Analysis of single-cell transcriptomics data
Single-cell FASTQ sequencing reads from each sample were processed and converted to digital gene expression matrices after mapping to the reference genome using the Cell Ranger Single Cell Software Suite (v3.1.0) [35] provided on the 10 × genomics website. Individual datasets were aggregated using the CellRanger aggr command without subsampling normalization. The aggregated dataset was then analyzed using the R package Seurat (v 3.1.0) [36]. First, the dataset was trimmed of cells expressing fewer than 200 genes. Next, the number of genes, UMI counts, and percentage of mitochondrial genes were examined to identify outliers. Because an unusually high number of genes can result from a ‘doublet’ event, in which two different cell types are captured together within the same barcoded bead, cells with > $90\%$ of maximum genes were discarded. Cells containing > $7.5\%$ mitochondrial genes were presumed to be of poor quality and also discarded. Next, gene expression values underwent library-size normalization, in which raw gene counts from each cell were normalized relative to the total number of read counts present in that cell. The resulting expression values were then multiplied and log-transformed. Subsequent analyses were conducted using only the most highly variable genes in the dataset. Principal component analysis was used for dimensionality reduction, followed by clustering in principal component analysis space using a graph-based clustering approach. Uniform Manifold Approximation and Projection (UMAP) was then used for two-dimensional visualization of the resulting clusters. For each clusters marker genes were identified using the FindConservedMarkers function as implemented in the Seurat package (logfc.threshold > 0.25 and minPct > 0.25). Next, clusters were annotated with known cell types according to the Cell Marker database [37]. Differently expressed genes across samples were identified using the FindConservedMarkers function in Seurat (logfc.threshold > 0.25, minPct > 0.25, and Padj ≤ 0.05). Finally, pseudotime trajectory analysis was conducted with the R package Monocle2 [38].
## Isolation and purification of Lin−CD117.+ hematopoietic stem cells (HSCs)
C57BL/6 mice (aged 8 weeks) were euthanized by cervical and sterilized in $75\%$ alcohol for 30 s, preferably allowing the fur of mice to be completely wetted. Next, the femur and tibia were removed and placed in a Petri dish containing pre-chilled PBS for cleaning. The attached muscle tissue was removed as cleanly as possible, and then the clean bone with muscle tissue removed was placed in pre-chilled PBS. Next, both ends of the tibia were cut and the bone marrow was flushed out with a 1-mL syringe until the bone turned white. Bone marrow cells were gently dispersed, and the resulting bone marrow monocyte PBS suspension was collected and filtered through a 40-µm cell sieve. Subsequently, the bone marrow was centrifuged at 440 × g for 5 min and the supernatant was discarded. Single bone marrow cells were resuspended in 5 mL of red blood cell lysate, and lysis was terminated with 5 mL of pre-cooled PBS after 1–2 min. Next, the cells were centrifuged at 440 × g for 5 min, the supernatant was discarded, and 5 mL of pre-cooled PBS was used to wash the cells. An aliquot was taken for counting. After negative selection of lineage cells using a mouse Lineage Cell Depletion Kit (Miltenyi Biotech), the cells were subjected to positive selection of CD117+ cells to obtain the target Lin−CD117+ HSCs.
## OP9/OP9-DL1 co-culture system
OP9/OP9-DL1 cells were co-cultured as previously described [20–22]. OP9 was co-cultured with Lin−CD117+ HSCs for directed differentiation into B cells, while OP9-DL1 was co-cultured with Lin−CD117+ HSCs for directed differentiation into T cells. Before preparing for differentiation, OP9/OP9-DL1 cells grown in log phase were seeded at a density of 3 × 104 cells/well in 12-well plates, and 105 Lin−CD117+ HSCs were added to each well after apposition. Lin−CD117+ HSCs were co-cultured with OP9/OP9-DL1 cells in $20\%$ fetal bovine serum (FBS), $5\%$ penicillin/streptomycin (P/S), 5 ng/mL recombinant mouse IL-7, and 5 ng/mL rmFit3-L in α-Minimum Essential Medium. After 12 d of differentiation into B cells, or 12 and 28 d of differentiation into T cells, control (Ctrl) and GA groups with a glycyrrhizic acid concentration of 3.125 µM were evaluated ($$n = 3$$ replicates per group).
## Cell preparation and flow cytometry analysis
Briefly, harvested mouse spleens were infiltrated with phosphate buffer, washed, lysed with lysis buffer, and filtered with a 70 µm cell strainer. Fresh blood samples were collected in heparin sodium tubes. Flow cytometry was used to directly quantify numbers of T cells (CD3 +) and B cells (CD45R/B220 +) in the spleen.
According to Roxanne Holmes' research [20], OP9-DL-1 cells are bone marrow-derived stromal cells that peripherally express the Notch ligand Delta-like 1 to support in vitro differentiation and development of T cells. T cell development proceeds through CD44 and CD25 yin-yang selection (DN phase changes) to yield CD44-CD25- (double-negative) cells, followed by CD4 + CD8 + double-positive cells, which ultimately mature into CD4 + /CD8 + single-positive cells. OP9 cells support in vitro differentiation and development of B cells [21]. At the end of co-culture, the upper cells were collected, rinsed 2–3 times with PBS, processed immunocytochemically, and quantified by flow cytometry (Fig. S9). Numbers of DN1 (CD44 + CD25-), DN2 (CD44 + CD25 +), DN3 (CD44-CD25-), DN4 (CD44-CD25-), T cells (CD4 + /CD8 +), B cells (CD45R/B220 +) and CD11b + cells were assessed. The optimal working concentration for all antibodies was 1 μg/mL. Data were analyzed with FlowJo software (Ashland, OR).
## Label-free quantitative (LFQ) proteomics
Following extraction of total protein from PBMCs, a bicinchoninic acid assay was used to quantify protein and divide it into six 100-µg aliquots. For each group of three aliquots, methanol was added to the control group and the small molecule GA was added to the treatment group. After incubating samples at 25 °C for 10 min, PK (1:100) was added for incubation at 25 °C for 5 min. Subsequently, samples were transferred to a water bath with a temperature greater than 95 °C to completely inactivate PK. Next, samples were cooled and placed at room temperature for 5 min. After adding $2\%$ Sodium deoxycholate (DOC) and 200 mM Ammonium bicarbonate (ABC), DL-Dithiothreitol (DTT) was added to samples and incubated at 37 °C for 30 min. Next, Iodoacetamide (IAA) was added and incubated at room temperature (protected from light) for 45 min. Subsequently, trypsin was added according to the ratio (1:50) and digested overnight in a 37 °C water bath. The following day, $50\%$ Trifluoroacetic acid (TFA) was added to terminate the enzymatic termination and precipitation, and the enzymatically cleaved peptides were eluted. After elution, peptides were digested and resuspended in 100 µL of $0.1\%$ Formic acid (FA) to yield a final concentration greater than 2 µg/µL. Finally, mass spectrometry was performed.
## nanoLC-MS/MS analysis
Two microliters of total peptide were taken from each sample and separated with an EASY-nLC1200 nano-UPLC Liquid Phase System (Thermo Fisher Scientific). Data were collected using a mass spectrometer (Q-Exactive HFX; Thermo Fisher Scientific) equipped with a nano-electrospray ion source. Separation was performed with a reversed-phase column (100 μm ID × 15 cm, Reprosil-Pur 120 C18-AQ, 1.9 μm). The mobile phase adopted the acetonitrile–water-formic acid system, in which mobile phase A was $0.1\%$ formic acid-$98\%$ aqueous solution ($2\%$ in acetonitrile) and phase B was $0.1\%$ formic acid-$80\%$ acetonitrile ($20\%$ in water). After the column was equilibrated with a $100\%$ A phase, the sample was directly loaded onto the column and passed through the column ladder degree separation with a flow rate of 300 nL/min and gradient length of 120 min. Mobile phase B ratios were applied as follows: $5\%$ for 2 min, $5\%$–$22\%$ for 98 min, $22\%$–$45\%$ for 16 min, $45\%$–$95\%$ for 2 min, and $95\%$ for 2 min. Mass spectrometry analysis used a data-dependent acquisition mode with a total analysis time of 120 min and positive ion detection mode.
## Molecular docking
The binding of GA and S100A8 protein was simulated with CB-Dock (http://clab.labshare.cn/cb-dock/php/blinddock.php) and AutoDock software. Homology modeling was performed in SWISS-MODEL to predict the S100A8 protein structure with and without GA secondary structures. The CB-Dock computing program for AutoDock Vina was used to select the structure with highest score, as predicted by a total of 50 simulation positions according to the binding affinity sorting. The highest affinity group from the predicted results was put into PyMOL for visualization. The hydrogen bond formed between GA and S100A8 protein is the proposed binding site.
## Surface plasmon resonance (SPR) analysis of GA with S100A8
The binding ability of GA to S100A8 protein was measured by SPR on a BIAcore3000 system (GE Healthcare, Chicago, IL). The S100A8 protein was immobilized on a CM5 chip by its amine group. A 10-µL aliquot of 0.1 mg/mL S100A8 was injected into the flow cell at a rate of 5 µL/min. Successful immobilization of S100A8 was verified by adding approximately 5000 RU to the sensor chip. No S100A8 was injected into the first flow cell as a control. GA was diluted in buffer containing 20 mM Tris–HCl, 150 mM NaCl, and 1 mM TCEP (pH 7.2). After fixation, varying dilutions of GA were injected at 30 µL/min for 3 min. After sample injection, the flow buffer was allowed to dissociate by passing over the sensor for 3 min. The sensor surface was regenerated by injecting 20 µL of 10 mM glycine–HCl solution (pH 2.25). Generated data were analyzed at 25 °C using Bioassessment Software 4.1.1 (GE Healthcare).
## RNA sequencing (RNA-seq)
Total RNA from samples was extracted to create an RNA-seq library, which was analyzed by BGI USA (Cambridge, MA) using a BGISEQ-500 sequencer. Raw sequencing reads were cleaned by removing reads containing aptamer or poly-N sequences, and reads with low-quality base ratios. Afterwards, clean reads were obtained and stored in FASTQ format. Clean reads were mapped to the reference genome using the HISAT2 (v2.0.4) [39]/Bowtie2 (v2.2.5) [40] tool. Gene expression levels were calculated using RESM software [41] and normalized to determine gene expression. Heat maps were plotted using Graphpad Prism 8 (GraphPad, San Diego, CA). To gain insight into phenotypic variation, Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) enrichment analysis of annotated differentially expressed genes was performed by Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) based on hypergeometric tests. Terms and pathways were corrected for significance levels by Bonferroni's strict threshold (Q value ≤ 0.05).
## qRT-PCR
Total RNAs were extracted from cells using TRIzol (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. cDNA was prepared using reverse transcriptase (Takara, Kusatsu, Japan) according to the manufacturer's protocol. qRT-PCR reactions were performed using SYBR green-fluorescent dye and the primer sequences below.
## sgRNA and CRISPR/Cas9 vector construction, virus generation, and transduction
sgRNA of S100A8 (sgRNA1F, 5'-caccgGACATCAATGAGGTTGCTCA-3'; sgRNA1R, 5'-aaacTGAGCAACCTCATTGATGTCc-3'; sgRNA2F, 5'-caccgGTCCTCAGTTTGTGCAGGTG-3'; and sgRNA2R, 5'-aaacCACCTGCACAAACTGAGGACc-3'; http://www.e-crisp.org/E-CRISPR) were cloned into the Lenti-CRISPR v2 vector following linearization by BsmB1 enzyme. Lentiviruses were generated by transiently transfecting 293 T cells with lentiviral plasmids VSVG and REV, and Pmdl packaging plasmids using Lentifit™ (HanBio Technology, Shanghai, China). Lentiviral particles were collected using ultracentrifugation. Lin−CD117+ HSCs were cultured in Ham's F-12 Nutrient Mixture with 1 × Penicillin–streptomycin–glutamine (PSG), 10 mM (4-(2-hydroxyethyl)-1-piperazineethanesulphonic acid) (HEPES), 1 mg/mL Polyvinyl alcohol (PVA), 1 × Insulin–transferrin–selenium–ethanolamine (ITSX), 100 ng/mL thrombopoietin (TPO), and 10 ng/mL stem cell factor for 24 h before adding lentiviral particles. Particles were incubated at room temperature for 15–30 min and then added to cells in culture dishes for incubation at 37 °C overnight. Mouse embryonic fibroblasts (MEF) cultured in Dulbecco’s Modified Eagle Medium with $10\%$ FBS and $5\%$ P/S were infected with virus by the $\frac{1}{2}$ small-volume infection method. After 4 h of infection, the medium was replenished with complete culture volume. After 24 h of infection, the culture medium was replaced with fresh complete medium for continued culture. Transfected cells were positively screened with 2 μg/mL of puromycin.
## Immune Reconstruction (IR)
B-NDG mice are severely immunodeficient with severe deficiencies of T, B, and NK cells, which could be reconstituted with Lin−CD117+ HSCs modified with lentiviral particles in vitro. Lin−CD117+ HSCs were infected with LV201 and OEa8 lentiviral particles, and 105 particles/mouse were intravenously injected into the tail of B-NDG mice. One week after IR, mice in good health were administered GA (5 mg/kg every 2 d, lasts 30 days) by tail vein injection. Experiments were divided into five groups: non-IR, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA.
## Open field test (OFT)
A square box (50 × 50 × 30 cm) was used to evaluate the anxiety of mice. Anxious mice prefer to stay in the corner of the box and make stereotyped movements along the sides. At the beginning of the experiment, mice were placed in the same corner of the box, and the trajectory of mice in the box was tracked and recorded for 15 min. The anxiety of mice was judged by observing the time mice spent in the central area.
## New object recognition (NOR)
Novel object recognition experiments use the rodent's natural instinct to approach and explore novel objects to test the behavioral sensitivity of the animal's recognition memory. The NOR experiment was divided into three phases: the adaptation period, familiarization period, and constant-temperature test period. Adaptation period: The new object was a square black box, and mice were put into one corner of the black box and allowed to explore the box freely for 10 min to adapt and reduce the stress of mice to the unfamiliar environment. Familiarization period: Two identical objects (A1 and A2) were placed diagonally across the bottom of the black box at a certain distance from the side, and mice were put into one corner of the black box and allowed to freely explore and familiarize with these two objects for 10 min. After familiarization, mice were put back into the cage for familiarization and tested after a certain period of time. Test period: A1 of the two identical objects was replaced in the black box with new object B. Mice were placed into the same corner of the black box they were put into during adaptation and familiarization, and allowed to explore the new object freely for 5 min. Video recordings were used to track the exploration times of mice for A2 and B. Preference for the new object was quantified by "discrimination index", expressed as D2 and calculated according to the formula: D2 = N/(FN + F), where "N" is the exploration time of mice for B, "FN" is the exploration time of mice for B, and "F" is the exploration time of mice for A2.
## Statistical analysis
Data were statistically analyzed using Graphpad Prism 8.0 and expressed as mean ± standard error of the mean (SEM). Statistical comparisons between two groups were performed using an unpaired t-test. Probability values less than 0.05 were considered statistically significant.
## Supplementary Information
Additional file 1: Figure S1. Single-cell clustering analysis of PBMCs from mice to evaluate changes in immune cell proportions during aging. ( A) Clustering of single cells in 8-week-old (young) mice. ( B) Clustering of single cells in 16-month-old (aged) mice. ( C) Comparison of clustering of single cells in young and aged mice. ( D) Relative population abundances of cell types in young and aged mice. ( E) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes in Cluster 1 (T cells, Igfbp4 as marker gene). ( F) KEGG analysis of differentially expressed genes in Cluster 2 (T cells, Cd8b1 as marker gene). ( G) KEGG analysis of differentially expressed genes in Cluster 7 (basal cells, Dapl1 as marker gene).Additional file 2: Figure S2. Proposed temporal developmental trajectory of PBMCs from single-cell sequencing of GA-treated aged mice. ( A) Temporal developmental trajectory constructed from PBMCs of GA-treated aged mice. a indicates the temporal developmental trajectory, whereby the darker color represents the earlier developmental stage after temporal differentiation; b indicates the position of the developmental stage on the developmental trajectory; and c indicates the distribution of each cell taxon on the developmental trajectory after clustering of single-cell sequencing results. ( B) Clustering of single cells in aged + GA mice. ( C) Cell clusters were divided into three stages according to the proposed temporal developmental stage. ( D) Expression of S100A8 in each cell cluster. Additional file 3: Figure S3. GA increased the proliferation of T- and B-cell subsets in vitro. ( A) Representative FACS plots of CD3+ T cells and CD45R/B220+ B cells in spleen cells of GA-treated C57BL/6 mice in vitro. ( B) Bar graph of T- and B-cell statistics of spleen cells from GA-treated C57BL/6 mice in vitro. Data represent mean ± SEM. $$n = 9$$, ****$P \leq 0.0001.$Additional file 4: Figure S4. RNA-seq of GA-treated Lin−CD117+ HSCs in vitro. ( A) Differentially expressed genes in GA versus control; S100A8 and S100A9 were upregulated. ( B) KEGG pathway enrichment map of GA and control differentially expressed genes. ( C) Bar graph showing upregulated S100A8 expression in GA-treated Lin−CD117+ HSCs in vitro by qRT-PCR. ( D) Bar graph showing upregulated S100A9 expression in GA-treated Lin−CD117+ HSCs in vitro by qRT-PCR. Data represent mean ± SEM. $$n = 3$$, *$P \leq 0.05.$Additional file 5: Figure S5. GA reduced S100A8 expression levels in PBMCs. ( A) Cell clustering maps of single-cell sequencing for PBMCs of young, aged, and aged-GA mice. ( B) Annotated cell clustering maps of single-cell sequencing for PBMCs in young, aged, and aged-GA mice. ( C) Expression levels of S100A8 in young, aged, and aged-GA mice. Additional file 6: Figure S6. RNA-seq of MEFs with point mutation (E69 and N67) and knockdown of S100A8. ( A) Venn diagram of differentially expressed genes for S100A8 point mutations E69 and N67 compared with control. ( B) Bar graph showing qRT-PCR validation of S100A8 knockdown in MEFs. ( C) Differentially expressed genes in GA-treated S100A8-knockdown MEFs. ( D) KEGG pathway enrichment map of differentially expressed genes in GA-treated S100A8-knockdown MEFs. ( E) Enrichment map of IL-17 signaling pathway gene expression profiles with barcodes indicating the location of genes in each gene set. NES, normalized enrichment score. Data represent mean ± SEM. $$n = 3$$, *$P \leq 0.05.$Additional file 7: Figure S7. Representative FACS plots and bar graphs showing CD45R/B220+ B cells in the spleens of B-NDG mice in non-IR + PBS, IR + PBS, IR + GA, IR-OEa8 + PBS, and IR-OEa8 (PM69) + GA groups. Additional file 8: Figure S8. GA is competitively bound to S100A9 in binding S100A8. ( A) Binding affinity of SPR COOH and S100A8-S100A9 interactions. S100A8 was immobilized on a COOH chip with S100A9 concentrations (from top to bottom) of 800, 400, 200, 100, and 50 μM. (B) GA affects the binding affinity of S100A9 in 400 nM S100A8. Concentrations of GA (from top to bottom) were 1 mM, 500 μM, 250 μM, and 125 μM; additionally, 400 nM S100A8 was evaluated. Additional file 9: Supplementary Fig. 9. T cell differentiation development flow cytometry gating strategy map. APC-CD25, PE-CD44, PE-CY7 Tm-CD11b. DN1 (CD44 + CD25-), DN2 (CD44 + CD25 +), DN3 (CD44-CD25-), DN4 (CD44-CD25-), and CD11b + cell.
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|
---
title: Clinical significance and immune landscape of a novel ferroptosis-related prognosis
signature in osteosarcoma
authors:
- Liyu Yang
- Jiamei Liu
- Shengye Liu
journal: BMC Cancer
year: 2023
pmcid: PMC10007778
doi: 10.1186/s12885-023-10688-7
license: CC BY 4.0
---
# Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma
## Abstract
### Background
Osteosarcoma is a malignant tumor that usually occurs in adolescents aged 10–20 years and is associated with poor prognosis. Ferroptosis is an iron-dependent cell death mechanism that plays a vital role in cancer.
### Methods
Osteosarcoma transcriptome data were downloaded from the public database TARGET and from previous studies. A prognostic risk score signature was constructed using bioinformatics analysis, and its efficacy was determined by analyzing typical clinical features. The prognostic signature was then validated with external data. Differences in immune cell infiltration between high- and low-risk groups were analyzed. The potential of the prognostic risk signature as a predictor of immunotherapy response was evaluated using the GSE35640 (melanoma) dataset. Five key genes expression were measured by real-time PCR and western blot in human normal osteoblasts and osteosarcoma cells. Moreover, malignant biological behaviors of osteosarcoma cells were tested by modulating gene expression level.
### Results
We obtained 268 ferroptosis-related genes from the online database FerrDb and published articles. Transcriptome data and clinical information of 88 samples in the TARGET database were used to classify genes into two categories using clustering analysis, and significant differences in survival status were identified. Differential ferroptosis-related genes were screened, and functional enrichment showed that they were associated with HIF-1, T cells, IL17, and other inflammatory signaling pathways. Prognostic factors were identified by univariate Cox regression and LASSO analysis, and a 5-factor prognostic risk score signature was constructed, which was also applicable for external data validation. Experimental validation indicated that the mRNA and protein expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly, though meanwhile MUC1 increased in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. Cell proliferation and migration ability of SAOS-2 were affected based on alterations of signature genes.
### Conclusions
Significant differences in immune cell infiltration between high- and low-risk groups indicated that the five ferroptosis-related prognostic signature was constructed and could be used to predict the response to immunotherapy in osteosarcoma.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-023-10688-7.
## Background
Osteosarcoma is a malignant tumor originating from mesenchymal tissue that accounts for $20\%$ of primary malignant bone tumors. It occurs in the epiphysis of blood-rich bone tissue in children and adolescents and has a very poor prognosis, causing significant physical and psychological trauma in patients [1]. The combination of neoadjuvant chemotherapy (NACT), surgical resection, and adjuvant chemotherapy is currently the accepted treatment in China and abroad [2]. However, the 5-year survival rate for patients with osteosarcoma remains at 60–$70\%$. Many patients exhibit chemoresistance, and the high metastatic and high recurrence rates of osteosarcoma have not been addressed by increasing doses or optimizing treatment regimens [3], resulting in nearly half of the patients ultimately declaring treatment failure [4].
The identification of tumor prognostic signatures and the characterization of relevant molecules have led to considerable progress in our understanding of the responses to treatment in malignant tumors [5, 6]. Therefore, comprehensively investigating the pathogenesis of osteosarcoma is essential to construct effective prognostic signatures and find potential therapeutic targets for guiding clinical treatment decisions in osteosarcoma.
Ferroptosis is a form of cell death caused by the loss of intracellular iron-dependent glutathione peroxidase activity and lipid peroxide accumulation, and mitochondria play a key role in the process of cellular ferroptosis [7, 8]. Ferroptosis is a novel form of programmed cell death that differs from cell necrosis, autophagy, and apoptosis, and plays a role in diseases such as ischemic organ injury and cancer. Ferroptosis inducers such as sorafenib are currently in clinical use, and several studies have shown that ferroptosis-related mechanisms may be useful for the design of cancer treatments [9, 10]. It has become a breakthrough point in tumor treatment in recent years. Ferroptosis is closely related to the occurrence, progression, and prognosis of osteosarcoma, as well as its sensitivity to chemotherapy [11–14]. Here, we constructed a risk score signature based on five ferroptosis-related genes for the prognostic risk classification of osteosarcoma.
Previous studies have revealed that ferroptosis and immune cell infiltration are closely correlated with the development and progression of cancer [15–17]. However, no systematic investigation or research has been conducted to elucidate the communicative functions of ferroptosis and immune infiltration in osteosarcoma mechanisms, we comprehensively analyzed the potential mechanisms of ferroptosis-related hub genes and immune infiltration cells in osteosarcoma, which has rarely been the foci of prior studies. Significant differences in immune infiltration were found between the different risk groups based on ferroptosis-related risk score signature. The efficacy of immunotherapy for the treatment of cancer has been demonstrated, and its indications are continually increasing, including osteosarcoma [18, 19]. Based on the theory above, we used the ferroptosis-related signature to identify targets of immunotherapy for osteosarcoma.
## Data download and processing
Transcriptome data were downloaded from the public database TARGET (https://xenabrowser.net/datapages/), which contains 88 samples of osteosarcoma, including clinical information such as age, gender, stage, survival time, and presence or absence of metastasis. Gene expression profile data were obtained by gene annotation. Ferroptosis-related genes were identified using the online database FerrDb (http://www.zhounan.org/ferrdb/index.html) and published articles [9, 10].
## Clustering identification and differential expression analysis
Based on the ferroptosis-related gene set, the non-negative matrix factorization (NMF) method was used to cluster the samples into two categories. R package Survminer was used to evaluate prognosis. T-test differential expression analysis was used to filter differentially expressed genes between clusters.
## Construction of a prognostic risk score signature
Univariate Cox regression analysis was used to identify prognostic factors based on ferroptosis-related genes with inter-cluster differences, followed by least absolute shrinkage and selection operator (LASSO) analysis to remove redundant factors. The prognostic risk score signature was generated, and typical clinical features were included to validate the prognostic risk score signature. The risk signature showed better predictive efficacy than clinical features such as age and gender.
## Efficacy of the prognostic risk score signature
The GEO dataset GSE21257 was used for external data validation of the prognostic risk score signature. Classification efficacy was assessed by prognostic survival analysis, ROC, AUC, and univariate and multivariate Cox regression analyses. Collograms were constructed with clinical features to demonstrate that it can be used as a clinical feature to classify samples.
## Evaluation of immunotherapy predictors in the prognostic risk score signature
Considering the immunological differences between the high- and low-risk ferroptosis factor-related clusters, differences in immune cell infiltration between the high- and low-risk groups were analyzed by CIBERSORT. The GSE35640 dataset (melanoma dataset) was used to evaluate whether the prognostic risk signature could be used as a predictor of immunotherapy, and efficiency was evaluated.
## Analysis of the prognostic risk signature combined with clinical characteristics
The Sankey diagram can intuitively show the proportion of patients with metastasis and those without metastasis in the high and low-risk groups. We constructed a Sankey chart by combining the high- and low-risk prognostic groups, clustering classification, and clinical characteristics.
## Biological experimental validation on quantification of gene expression by real-time PCR
Relative mRNA expression level of five genes were measured among hFOB1.19, MG-63 and SAOS-2 cell lines. Human osteoblast cell line hFOB1.19 was maintained in DMEM/F12 medium at 34◦C with $5\%$ CO2 in a humidified atmosphere. Human osteosarcoma cell line MG-63 and SAOS-2 cells were cultured in MEM and Macoy’5A medium, respectively. Medium were all supplemented with a $10\%$ FBS, 100 µg/ml streptomycin and 100 U/ml penicillin at 37◦C with $5\%$ CO2 in a humidified atmosphere.
Total RNA was extracted with TRIzol reagent then used to synthesize cDNA using SuperScript II reverse transcriptase (Invitrogen; Thermo Fisher Scientific, Inc.) with 5 μg oligo (dT) primers per sample. By using SYBR Green PCR master mix (Applied Biosystems; Thermo Fisher Scientific, Inc.), qPCR was performed in a total volume of 20 μL in a 7500 Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.) as follows: 95˚C for 5 min, and 40 cycles of 95˚C for 30 s and 60˚C for 45 s. Melt-curve analysis was used to confirm the specificity of the amplification and GAPDH served as the endogenous control for normalization of amount of total RNA in each group. The relative levels of gene expression were performed as ΔCq = Cqgene – Cqreference, and the gene expression was calculated in fold change according to the 2−ΔΔCq method while repeated independently in triplicate.
The primer sequences were designed as follows: forward, 5'-AGGGCTCCTGGGTAGAACT-3' and reverse, 5'-CTCCATTATAAATAGAAACCGAGGC-3' for BNIP3; forward, 5'-CACAGTGCTTACAGTTGTTACG-3' and reverse, 5'-TGGTCATACTCACAGCATTCTT-3' for MUC1; forward, 5'-GGAGAAAGAGATGTCAAGGGAA-3' and reverse, 5'-CAATTTTGTCTTGGTCTTCCGT-3' for MAP3K5; forward, 5'-CTGGACACGTTGGCGGATGATG-3' and reverse, 5'-CGCTTGTGTAGTGCCTGTGAGTC-3' for LURAP1L; forward, 5'-CCTCCCTGTACCACATCTATGT-3' and reverse, 5'-GCTCTTCTGGGAAGTAGACAG-3' for HMOX1; forward, 5'-TCAAGGCTGAGAACGGGAAG-3' and reverse, 5'-TGGACTCCACGACGTACTCA-3' for GAPDH.
## Biological experimental validation on quantification of gene expression by Western blot analysis
Total proteins from each well were harvested in ice-cold radioimmunoprecipitation (RIPA) lysis buffer (Thermo Fisher Scientific, Inc.) supplemented with phenylmethanesulfonyl fluoride for 1 h. The protein concentration was quantified by bicinchoninic acid protein assay kit (Sigma-Aldrich) according to the manufacturer’s instructions. Equal proteins of each treatment were separated on $12\%$ sodium dodecyl sulfate polyacrylamide (SDS-PAGE) gels (Beyotime Institute of Biotechnology, Haimen, China) and electrophoretically transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, Bedford, MA, USA). The membranes were soaked in $5\%$ skimmed milk as blocking buffer for 1 h, then washed in Tris-buffered saline Tween-20 [TBST;150 mmol/l NaCl (PH 7.5), 20 mmol/l Tris–HCl and $0.1\%$ Tween 20] at room temperature for 3 times. The membranes were incubated with primary monoclonal antibodies against MUC1 (ab182560, abcam), MAP3K5 (ab131506, abcam), LURAP1L (LURAP1L antiody, Santa Cruz Biotechnology), HMOX1 (ab68477, abcam), and BNIP3 (ab109362, abcam) at 1:1000 dilution overnight at 4 °C followed by hybridized with horseradish perosidase (HRP)-conjugated secondary antibody (Santa Cruz Biotechnology) and visualized by using enhanced chemiluminescence as the HRP substrate. The relative protein levels were calculated based on β-actin (MD6553, MDL) as the loading control.
## Biological experimental validation on influence of five key gene expression on malignant biological behavior of osteosarcoma cells.
Small interfering RNA (siRNA) transfection methods were applied to knock down key gene expression. *Key* genes siRNA and a negative control (siRNA-NC) were purchased from GenePharma (Shanghai, China). Si-NC and siRNA were transfected into SAOS-2 cells cultured in six-well plates by using Lipofectamine 3000 (Invitrogen), following the manufacturer’s instructions. The knockdown effect of siRNA was measured by westernblot after transfected for 48 h as the manufacturer’s instructions. Cell viability was carried out using CCK-8 toolkit. The specific experimental procedure is described as previously. Relative cellular viability was recorded. Furthermore, wound healing assay was used to assess migration ability of osteosarcoma cells. SAOS-2 cells were transfected with siRNA in six-well plates and cultured until reaching $90\%$ confluence. A wound was created with a pipette tip, the cells were washed twice, and cultured in medium without FBS. The wound was observed and photographed at 0 h and 24 h using an inverted microscope (Nikon, Japan). Cell migration ability was described as the number of cells that migrated into the wound. All assays were performed in triplicate. All the presented data and results were processed using GraphPad Prism 9 software and expressed as mean ± standard deviation of at least three independent experiments. T-test was used to determine statistical significance. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ were considered to indicate statistically significant differences.
## Osteosarcoma transcriptome and ferroptosis-related gene processing
Osteosarcoma transcriptome data were retrospectively downloaded from the public database TARGET. A total of 88 samples were analyzed including clinical information such as age, gender, stage, survival time, and the presence or absence of metastasis. Gene expression profile data were obtained by gene annotation (Supplementary table 1). We identified 268 ferroptosis-related genes from the online database FerrDb and previous research (Supplementary table 2). The workflow of this study is displayed in Fig. 1.Fig. 1The workflow of this study
## Clustering survival difference analysis based on the ferroptosis dataset
The NMF method was used to cluster the samples into two categories based on the TARGET ferroptosis-related gene data (Fig. 2). The R package Survminer was used for survival prognosis, which showed a significant prognostic value for each cluster ($$P \leq 0.039$$). The R package Pheatmap was used to generate the heatmap. Fig. 2A NMF cluster analysis results. The horizontal axis represents the classification category. B Kaplan–Meier survival curve of two clusters. The horizontal axis represents survival time, and the vertical axis represents survival rate. C Heatmap of differentially expressed ferroptosis-related genes in the two lables. The horizontal and vertical axes represent the classification category, the vertical axis represents the gene, and the depth of color represents gene expression
## Ferr-DEG recognition and enrichment analysis
According to T-test differential expression analysis, 31 differentially expressed ferroptosis-related genes (Ferr-DEGs) between different clusters were screened ($P \leq 0.05$). Functional enrichment analysis indicated that these genes were associated with HIF-1, T cells, and IL17 inflammatory signaling pathways (Fig. 3).Fig. 3Enrichment analysis of differentially expressed ferroptosis-related genes
## Identification of prognostic factors
Univariate Cox regression analysis was performed to identify prognostic factors based on ferroptosis-related genes associated with inter-cluster differences. *Eleven* genes were screened out according to COX P-values < 0.05; the results of the prognostic analysis are shown in Fig. 4.Fig. 4Differential expression and prognostic analysis of 11 ferroptosis-related genes
## Prognostic risk score signature construction
LASSO regression analysis was used for dimension reduction to remove redundant factors, and five related factors were selected to build a prognostic risk score signature (Fig. 5). Risk score = 0.11735 × MUC1—0.23479 × MAP3K5—0.19464 × LURAP1L—0.07795 × HMOX1 + 0.20553 × BNIP3.Fig. 5Prognostic risk score signature construction. A Among the 11 genes screened by Cox regression, the left side represents the P value of each gene obtained by Cox regression, the middle is the gene, and the right side represents the HR value obtained by Cox regression (> 1 indicates a risk factor, and < 1 indicates a protective factor). B LASSO regression results show the dotted line position as 5. *Five* genes were screened to build the signature. C Results obtained by LASSO regression show that the dotted line position points to 5. *Five* genes were finally screened to build the signature. The horizontal axis of the five genes screened by LASSO regression represents the LASSO regression coefficient of each gene, which is the coefficient of each gene in the risk score
## Efficiency evaluation of the prognostic risk score signature and clinical characteristics
The samples were classified into high- and low-risk groups according to the prognostic risk score signature, and prognostic survival analysis showed significant prognostic efficacy (****$P \leq 0.0001$). ROC curves and AUC were used to evaluate the efficacy. The results showed that the classification efficacy of the prognostic risk score signature was better than that of clinical characteristics such as age and gender, as shown in Fig. 6 (A-D).Fig. 6Effectiveness evaluation of the prognostic risk score signature (A-D). Survival curves for the high- and low-risk groups. The horizontal axis shows time and the longitudinal axis is survival probability, B ROC curve. The horizontal axis shows the false positive fraction and the longitudinal axis shows the true positive fraction. C AUC curve of clustering groups; the horizontal axis shows specificity and the longitudinal axis shows sensitivity. D AUC curve of metastatic grouping; the horizontal axis shows specificity and the longitudinal axis shows sensitivity. E Univariate Cox regression for all clinical indicators. The horizontal axis represents the HR value (greater than 1 is a risk factor, less than 1 is a protective factor), and the vertical axis represents the P value of single genes and univariate Cox regression. F Multivariate Cox regression results of clinical indicators. The horizontal axis represents the HR value (greater than 1 is a risk factor, less than 1 is a protective factor), and the vertical axis represents the P value of single genes and multivariate Cox regression. G Heatmap of differential expression of genes. The horizontal axis represents classification, the vertical axis represents genes, and the color represents expression. H Corresponding states of the sample survival time and the high- and low-risk groups. Differential display of risk score in clinical characteristics grouping (I-K). I. 0 refers to the state of alive or existence. 1 refers to the state of death. J 1 refers to the occurrence of metastasis and 2 refers to the absence of metastasis. K 0 refers to the first category of the consistent clustering, and 1 refers to the second category of the consistent clustering Univariate and multivariate Cox regression analyses confirmed that the prognostic risk score signature had better classification efficacy than clinical characteristics such as age and gender (Fig. 6E, F).
The prognostic risk score signature was evaluated by applying typical clinical characteristics, which showed that it had better predictive efficacy than clinical characteristics such as age and gender (Fig. 6G, H). The Wilcoxon test was used to calculate the P-value with better predictive efficacy in clinical characteristics such as survival status, occurrence of metastasis, and category labeling (Fig. 6I-K).
## Validation of the prognostic signature in the external dataset
The prognostic risk score signature was applied to the GEO dataset GSE21257 for external data validation (Fig. 7), including 53 samples (Supplementary table 4). Prognostic survival analysis, ROC, AUC, and univariate as well as multivariate Cox regression analyses showed that it had a good classification efficiency. Fig. 7External dataset validation. A *Survival analysis* curve. B AUC curves for different survival times. C AUC curves for different clinical characteristics. D Univariate Cox regression analysis results. E Multivariate Cox regression results of multiple factors
## Establishment of a columnar table of clinical characteristics
The columnar table of clinical characteristics shown in Fig. 8 was constructed by combining age, gender, clustering type, metastasis occurrence, and risk score. Fig. 8Nomograms for clinical features. A represents different clinical features in the classification effect. B and C show the calibration curves for the nomogram predicting 3-year and 5-year disease-free survival (DFS) rates
## Immunological infiltration differential analysis
Considering the immune differences between the high- and low-risk groups of ferroptosis factors, we performed differential analysis of immune cell infiltration based on high and low prognostic risk groups, and significant differences in infiltration were found in subtypes of B cells and some T cells by the Wilcox test (Fig. 9).Fig. 9Differential analysis of immune infiltrating cells between high- and low-risk groups. A Differential state of immune infiltrating cells in different types. B Correlation between expression of five genes and immune cells infiltration state. C The diagrams for distribution of the risk score in three types of immune cells
## Evaluation of immunotherapy predictors using the prognostic risk score signature
The prognostic risk score signature was applied to the GSE35640 (melanoma) dataset to assess whether it could be used as a predictor of immunotherapy response. As shown in Fig. 10, the PR and PD rates differed between the high- and low-risk groups, and the AUC value was greater than 0.6, indicating high efficacy (PR refers to immunotherapy predicting partial response, or effective; PD refers to immunotherapy predicting progressive disease, or non-effective).Fig. 10Evaluation of immunotherapy predictors for the prognostic risk signature. A Survival analysis. B Different proportions of PR and PD in high- and low-risk groups confirm the validity of the classification. C Risks core of PR and PD. D AUC curve of prognosis signature; the horizontal axis shows specificity and the longitudinal axis shows sensitivity
## Sankey diagram analysis for clinical characteristics
We combined the prognostic high- and low-risk groups, clustering type, and clinical characteristics to construct a Sankey diagram (Fig. 11). The Sankey diagram shows significantly differences in the proportions of patients with and without metastases in the high- and low-risk groups. Most of the high-risk patients had metastases, and only a small proportion of patients with metastases were in the low-risk group. Fig. 11Construction of the Sankey diagram combined with clinical features
## Biological experimental validation on quantification of key genes in signature and malignant biological behavior of tumor cells based on these genes
As Fig. 12A illustrated, the mRNA expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. MUC1 was upregulated in MG-63 and SAOS-2 cells (*$P \leq 0.05$, **$P \leq 0.01$). To further investigate the five genes in bone-derived cells, western blot (Fig. 12B) was performed to examine five ferroptosis-associated genes (MUC1, MAP3K5, LURAP1L, HMOX1, and BNIP3). MAP3K5, LURAP1L, HMOX1 and BNIP3 expression levels were markedly decreased in the osteosarcoma cells compared with the normal human osteoblast hFOB1.19 cells, they are negatively related with the riskscore. These findings revealed that each gene might characterize protective feature. However, MUC1 gene showed an obviously opposite trend and might characterize tumorigenic feature (*$P \leq 0.05$, **$P \leq 0.01$). Figure 12C showed that knockdown of gene expression by siRNA significantly inhibited the expression of five target genes. Wound healing test in SAOS-2 cell lines illustrated that knockdown of MUC1 impaired the ability of migration. On the contrary, knockdown of MAP3K5, LURAP1L, BNIP3 and HMOX1 enhanced cancer cell migration. The above results are shown quantitatively in Fig. 12D. Moreover, cell viability assays were also performed, presenting consistent tendency with the migration ability (Fig. 12E) (*$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$).Fig. 12A The relative mRNA expression levels were measured using the fold-change in each protein relative to GAPDH. The data are expressed as the mean ± standard deviation. B Expression of ferroptosis-related protein was measured by western blot, The relative protein expression levels were quantified and measured using the fold-change in each protein relative to β-actin, *$P \leq 0.05$ and **$P \leq 0.01$ vs. hFOB1.19. C The knockdown efficiency of siRNA on five key genes was measured by westernblot, and wound healing assay results based on siRNA shows the migration ability differences. D Quantification of wound healing assay. E Cell viability assay by CCK-8. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$ vs. si-NC
## Discussion
Current treatments for osteosarcoma include surgery and chemotherapy. Although some chemotherapeutic drugs show efficacy for the treatment of osteosarcoma, the disease control rate and survival time remain unsatisfactory. Patients with osteosarcoma have a poor prognosis, underscoring the need to explore new safe and effective treatment options in the clinic [10]. The role of ferroptosis in tumors was recently revealed and it is expected to be a new therapeutic target to guide clinical practice. Ferroptosis is a novel form of cell death caused by the massive accumulation of intracellular reactive oxygen species (ROS) and oxygen radicals. Tumor cells normally have persistently high ROS levels and are more vulnerable to ROS because of excessive oxidative stress [20]. Therefore, due to the crucial role of ROS in the initiation and progression of tumors, inducing abnormal ROS generation and accumulation might be a useful antitumor strategy [21]. Lv et al. demonstrated that PEITC induces ferroptosis, autophagy, and apoptosis in K7M2 osteosarcoma cells by activating the ROS-related MAPK signaling pathway. PEITC has promising anti-osteosarcoma activity [22, 23]. Ferroptosis is involved tumor drug resistance by impairing STAT3/Nrf2/GPX4 signaling, and it increases the sensitivity of osteosarcoma cells to cisplatin [11]. Certain pharmaceutical ingredients such as EF24, Artemisia Annua L. and Pure Artemisinin might serve as potential agents for the treatment of osteosarcoma [13, 24]. Based on theories above, ferroptosis-related factor classification and risk score signature construction is worthy of investigating.
This study analyzed downloadable osteosarcoma transcriptome data from the public database TARGET as well as 268 published ferroptosis-related genes. The samples were divided into two clusters based on the set of ferroptosis-related genes, and significant differences in survival status were detected. Functional annotation demonstrated that the differential genes were associated with inflammatory signaling pathways such as HIF-1, T cells, and IL-17. Based on inter-cluster differences in ferroptosis-related genes, prognosis-related factors were identified using univariate Cox regression analysis, and redundant factors were removed by applying LASSO regression to reduce dimensionality. Finally, five factors were selected to construct a prognostic risk score signature. The efficacy of the prognostic risk score signature was evaluated, and it was found to have better predictive efficacy than clinical characteristics such as age and gender.
In this study, five key factors in the prognostic risk-score signature were correlated with osteosarcoma. BNIP3 is a unique pro-apoptotic protein with ACAA2 as a functional binding partner in human osteosarcoma U-2OS cells [25]. Inhibition of BNIP3 expression suppresses cell apoptosis. Ye et al. showed evidence of baicalein’s anti-osteosarcoma mechanism links ROS-induced BNIP3 expression in MG-63 cells [26]. Herbert et al. showed that peptides present in secreted MUC1 may have immunoenhancing properties for osteosarcoma. As a survival-related upregulated gene in osteosarcoma, MUC1 was selected as a potential independent prognostic candidate gene, and has been associated with metastatic progression both in vivo and in vitro in several cancer types [27]. High expression of MUC1 is correlated with low survival of osteosarcoma patients [28], which is consistent with our results that MUC1 acts as a risk factor in our signature. Heme oxygenase-1 (HO-1) is a major antioxidant enzyme that plays a central role in the removal of intracellular ROS [29, 30]. Induced expression of HO-1 is responsible for the resistance of human osteosarcoma MG63 cells to the chemotherapeutic agent arsenic trioxide [31]. A study that identified four genes predicting the survival of osteosarcoma patients showed that MAP3K5 is negatively correlated with survival risk and risk score, which is consistent with our results [32]. MAP3K5 is a macrophage-associated gene signature member to predict the prognosis of osteosarcoma and might direct immunotherapy [33]. LURAP1L was identified in our risk score signature as a negative risk factor, although its role in cancer has not been reported to date. Our results indicated that knockdown of LURAP1L was in line with the increase of SAOS-2 proliferation and migration ability.
Risk score signature with five key factors illustrated in Result 5 showed that the classification efficacy and predictive efficacy of the prognostic risk score signature were better than some typical clinical characteristics, such as age, gender, survival status, occurrence of metastasis, as well as category labeling. Furthermore, we validated the prognostic risk score signature with external data, and found that it had better classification efficacy than conventional clinical features. The results of previous enrichment analysis led us to analyze differences in immune cell infiltration between high-and low- prognostic risk clusters, which showed significant infiltration differences of B cells and T cells in these subtypes. Immunotherapy has revolutionized cancer treatment and the clinical applications of immunotherapy have been adapted to range from the management of many tumors. Melanoma is characterized by rapidly spreading and life-threatening progression, which has currently the most available and comprehensive data in the public database. Therefore, melanoma datasets are used for effective verification to illustrate the advantage of the signature. Due to lack of immunotherapy dataset for osteosarcoma, we applied the prognostic risk score signature to the GSE35640 (melanoma) dataset [34] to assess whether it could be used as a predictor of immunotherapy and found that it had high efficacy as well. In future studies, this prognostic risk score signature might be the breakthrough in immunotherapy based on immunology features for cancer.
In addition, we designed in vitro experiments to validate the five key genes in ferroptosis-related prognosis signature. Human normal osteoblasts cell line hFOB1.19, and two kinds of human osteosarcoma cell line SAOS-2 and MG-63 are enrolled in our validation process. hFOB1.19 is the normal human osteoblast and regarded as negative control, while MG-63 and SAOS-2 are human osteosarcoma cell lines for dual validation experiments. By verifying the changes in gene expression in the three cell lines and the way in which they altered malignant biological behavior, the significance of their impact on OSA prognosis can be clarified to some extent. Based on the theory above, we examined the influence of the five key gene expression on malignant biological behavior of osteosarcoma cells, such as proliferation and migration. We used real-time PCR to initially validate the expression of five genes in three cell lines. Then it was confirmed by Western blot that the mRNA expression level of MAP3K5, LURAP1L, HMOX1 and BNIP3 decreased significantly in MG-63 and SAOS-2 cells compared with hFOB1.19 cells. MUC1 was upregulated in MG-63 and SAOS-2 cells. MAP3K5, LURAP1L, HMOX1 might serve as negative risk factor for prognosis of osteosarcoma or melanoma. Different from MUC1, knockdown of these genes could promote the proliferation and migration ability of tumor cells. While, MUC1 participated as a risk factor which is detrimental to prognosis. Aside from the four genes above, BNIP3 expression was not in concert with the role as a positive risk factor in signature. However, such results do not affect the overall guidance value of the risk score in ferroptosis-related prognosis signature. Five factors constitute the signature structure to assist in risk scoring process. However, the present study had some limitations. Additional prospective studies involving a large-cohort clinical studies are needed to confirm the role of the five ferroptosis-related signature in osteosarcoma prognosis. And further immunological features are supposed to be evaluated in pathological tissue samples in the future.
## Conclusion
Five ferroptosis-associated genes (MUC1, MAP3K5, LURAP1L, HMOX1, and BNIP3) correlated with the prognosis of osteosarcoma were screened for the construction of the risk score model. A novel ferroptosis-associated risk score signature to differentiate low- and high-risk groups of osteosarcoma was constructed based on multiple bioinformatics analyses. The signature was validated using an external independent dataset. It showed good classification efficacy for immunotherapeutic prognostic indicators for osteosarcoma and melanoma as well.
## Supplementary Information
Additional file 1. Supplementary materials. Additional file 2. Supplementary table 1.Additional file 3. Supplementary table 2.Additional file 4. Supplementary table 3.Additional file 5. Supplementary table 4.
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---
title: Association between calf circumference and incontinence in Chinese elderly
authors:
- Lin Li
- Feilong Chen
- Xiaoyan Li
- Yiyuan Gao
- Silin Zhu
- Xiyezi Diao
- Ning Wang
- Tao Xu
journal: BMC Public Health
year: 2023
pmcid: PMC10007784
doi: 10.1186/s12889-023-15324-4
license: CC BY 4.0
---
# Association between calf circumference and incontinence in Chinese elderly
## Abstract
### Background
The objective of this study was to analyze the association between calf circumference and incontinence in Chinese elderly, and to find out the maximal cut-off point by gender for the use of calf circumference in screening for incontinence.
### Methods
In this study, participants were from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). The maximal calf circumference cut-off point and other incontinence-related risk factors were explored using receiver operating characteristic (ROC) curves and logistic regression analysis.
### Results
The study included 14,989 elderly people (6,516 males and 8,473 females) over 60. The prevalence of incontinence in elderly males was $5.23\%$ ($\frac{341}{6}$,516), significantly lower than females, which was $8.31\%$ ($\frac{704}{8}$,473) ($p \leq 0.001$). There was no correlation between calf circumference < 34 cm in males and < 33 cm in females and incontinence after adjusting the confounders. We further stratified by gender to predict incontinence in elderly based on the Youden index of ROC curves. We found the association between calf circumference and incontinence was the strongest when the cut-off points were < 28.5 cm for males and < 26.5 cm for females, with an odds rate (OR) value of 1.620 (male, $95\%$CI: 1.197–2.288) and 1.292 (female, $95\%$CI: 1.044–1.600) after adjusting the covariates, respectively.
### Conclusions
Our study suggests that calf circumference < 28.5 cm in males and < 26.5 cm in females is a risk factor for incontinence in the Chinese elderly population. Calf circumference should be measured in routine physical examination, and timely interventions should be made to reduce the risk of incontinence in subjects with calf circumference less than the threshold.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15324-4.
## Background
Population aging has become an important issue in China. According to the Seventh National Population Census of the National Bureau of Statistics, by the end of 2020, the elderly population aged 60 and above in China has reached 264 million, accounting for $18.7\%$ of the total population [1], thus China has the largest elderly population in the world. With the increased aging population in China, age-related diseases have attracted more and more attention. Is there a link between sarcopenia and incontinence, given that they are both associated with aging? So far, only a few international studies have attempted to reveal the relationship between sarcopenia and incontinence, but no firm conclusions have been reached. Studies have shown that sarcopenia involved the weakness of abdominal muscles and pelvic floor muscles, leading to urinary incontinence(UI) as the urethra could not generate sufficient pressure to resist increases in intravesical pressure. Therefore, sarcopenia was considered to increase the susceptibility to UI in elderly people [2].
Sarcopenia is defined as age-related loss of muscle mass, decreased muscle strength, and/or lower physical performance [3]. The Global Leadership Initiative on Malnutrition (GLIM) approaches for diagnosing malnutrition included technical approaches(such as bioelectrical impedance analysis, dual-energy x-ray absorptiometry, computerized tomography and Ultrasound) and clinical approaches(such as calf circumference, mid-arm circumference and physical examination), and recommended that "cut-off values were calf circumference in male < 33 cm and in female < 32 cm" [4]. Asian Working Group for Sarcopenia (AWGS): 2019 Consensus update on sarcopenia diagnosis and treatment pointed out that "calf circumference, as a simple method to assess skeletal muscle mass in limbs, can be used for effective screening of sarcopenia," and the diagnostic parameters were "calf circumference in male < 34 cm and in female < 33 cm" [5]. Calf circumference is an anthropometric variable correlated with skeletal muscle mass (ASM), and estimates cross-sectional muscle area, subcutaneous fat and skin fold. When elderly people have very little subcutaneous fat, calf circumference is more representative of muscle mass [6].
The problem of incontinence among the elderly population has simultaneously become increasingly prominent. The negative impact of incontinence on the quality of life and social dignity of elders has become a serious medical and social problem due to the significant improvement in living standards. Incontinence mainly includes urinary incontinence and fecal incontinence. The International Continence Society defined that urinary incontinence (UI) is the complaint of any involuntary leakage of urine, which brings inconvenience to patients in social activities and personal hygiene [7]. Fecal incontinence (FI) is defined as the involuntary loss or passage of solid or liquid stools [8]. Studies on the epidemiology of incontinence have found that the incidence of UI in males is $5.4\%$ [9], and $25\%$ among Norwegian females [10]. The incidence of FI among older Brazilian males was $4.7\%$ and $7.3\%$ among females [11]. A survey from Peking Union Medical College Hospital in China showed that the prevalence of UI and FI in adult females was $30.9\%$ and $0.43\%$, and increased with age [12, 13].
In this study, calf circumference was used as a screening index to analyze the association between calf circumference and incontinence, aiming to provide a simpler, more convenient and effective indicator for clinical prevention and diagnosis of incontinence in the elderly population.
## Participants
The information was obtained from the Chinese Longitudinal Health and Longevity Survey (CLHLS), a nationwide, prospective cohort study of middle-aged and elderly people who were community-dwelling and institutionalized in China, initiated by the Center for Healthy Aging and Family Studies at Peking University. It covered most of China's provinces, and aimed to understand the health status of China's elderly, and related social, behavioral, and biological factors. The study conducted the first baseline survey in 1998, followed by a follow-up every two to three years, and covered the eastern, central, and western regions of China. Details such as sampling design, and data quality assessment can be found in previous studies [14, 15].
Our study used data obtained from the Seventh CLHLS cross-sectional survey conducted in 2018. The survey collected health data of 15,874 elderly people from 23 provincial administrative regions in China. We excluded individuals with missing data on calf circumference and incontinence status, as well as individuals under the age of 60, and filtered out the abnormal values of calf circumference. We further excluded individuals with chronic wasting diseases including tuberculosis, hepatitis and cancer. In the end, we included 14,989 subjects, of whom $43.47\%$ were male and $56.53\%$ were female (Fig. 1).Fig. 1Flowchart of Participants selection
## Data collection
This study collected the information on main research indicators (calf circumference length, incontinence status), demographic characteristics (gender, socioeconomic status, waist circumference, body mass index [BMI], education level, history of pregnant [only in females]), lifestyle characteristics (smoking status, drinking status, physical exercise, limited ability of daily activities, intake of fruits and vegetables) and history of diseases (including history of falling, history of respiratory diseases, urinary diseases, stroke or cardiovascular diseases, and nerves system disease). Calf circumference, waist circumference, height, and weight were measured by highly trained doctors and nurses. Data on demographic information, lifestyle characteristics, incontinence status, and history of diseases were obtained through face-to-face interviews using internationally compatible questionnaires, which had demonstrated good reliability and validity. All investigators received strict training in advance.
The study was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-24,713,074). All participants signed a written informed consent form.
## Definition of incontinence status
In this study, the status of incontinence was assessed through three options in the questionnaire. These included "able to control defecation," "occasional or sometimes incontinence," and "using catheters to assist in controlling incontinence or unable to control." " Able to control defecation" was defined as non-incontinence, and the latter two options were defined as the existence of incontinence.
## Measurement of calf circumference
The calf circumferences were measured by trained investigators. The subjects were in sitting position, bending their knees and hips 90 degrees, with feet placed naturally on the ground. Facing the subjects, the investigator placed an inelastic belt ruler around the calf, did not compress the subcutaneous tissue, and moved along the length of the calf to obtain the maximum perimeter. Each leg was measured twice, and the average value was taken as the circumference value of each leg. The measured value of the calf circumference of the two legs was then averaged and recorded as the final measured value of the calf circumference [16]. The measurement of calf circumference was in cm, accurate to one decimal place [17]. All the staffs received strict train before they were allowed to performed the measurement, and they were required to strictly follow the standard operating procedures (SOPs). Besides, participants also received pre-education to ensure that they would actively cooperate with the researchers. Other detailed quality control measures could be seen on CLHLS website [18].
Based on the 2019 Consensus released by the AWGS, the cut-off points of sarcopenia were indicated by the use of calf circumference < 34 cm for males and < 33 cm for females in different places such as communities and nursing homes [19]. Therefore, our study used these cut-off values to divide the calf circumference. Small calf circumference was defined as < 34 cm for males and < 33 cm for females, and the opposite was defined as thick calf circumference.
## Covariates
The covariates included demographic characteristics (gender, socioeconomic status, waist circumference, BMI, education, history of childbirth) and lifestyle characteristics (smoking history, drinking history, participation in exercise, limitation of daily activities, intake of fruits and vegetables, and history of respiratory diseases).
According to the Dietary Guidelines for Chinese Residents 2022 [20], the appropriate BMI level for Chinese people over 65 was 20.0 kg/m2–26.9 kg/m2, and BMI > 27 kg/m2 was associated with overweight or obesity [21]. The definition of socioeconomic status was based on subjects' subjective life feelings: those who thought that their living conditions were "very rich" or "relatively rich" were defined as "better," "average" was defined as "average," and "more difficult" and "very difficult" were defined as "poor." Waist circumference was measured using an inelastic tape at the end of quiet breathing [22]. Education levels were judged by the length of school time reported by the subjects. Those who had never attended school were defined as "illiterate," those who attended school for 1–10 years were defined as "primary or junior high school," and those who attended school for more than 10 years were defined as "university or above." Females who had never pregnant defined as "No history of pregnant," those who had pregnant once were defined as "*Having a* history of pregnant." Lifestyle characteristics covariates were classified according to the subjects' answers to the questionnaires. History of respiratory disease was defined as the subject having suffered from chronic respiratory disease such as bronchitis, emphysema, asthma, or pneumonia formerly or presently; Urological disease history was defined as the subject having chronic nephritis, prostatitis, etc., and history of neurological disease was defined as participants who had Parkinson's disease, dementia or epilepsy.
## Statistical analysis
SAS version 9.4 and R 4.2.2 software were used for data analysis. Two-tailed tests were conducted and $p \leq 0.05$ was defined as statistically significant. For baseline information, different groups were divided according to gender and incontinence status. Continuous normal distribution data were described by mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm$$\end{document}± standard deviations, and Student’s t-tests were used for comparison between groups. The classified data were described by numbers and percentages, and were compared using the Chi-squared test.
To clearly illustrated the association between calf circumference and incontinence, we modeled calf circumference against incontinence and used a restricted cubic spline with three knots located at the 25th, 50th, and 75th percentiles to flexibly model the underlying relationship. Two different methods were used to divide the calf circumferences. Firstly, according to AWGS’s 2019 Consensus, male calf circumference < 34 cm and female < 33 cm were defined as small calf circumference, while male ≥ 34 cm and female ≥ 33 cm were defined as thick leg circumference [19]. Secondly, we divided the calf circumference based on the maximal calf circumference cut-off points by calculating the greatest Youden index through the ROC curves. During analysis, we stratified the data by gender due to the gender differences in normal range of calf circumference and prevalence of incontinence among elderly people. The results showed that the Youden index was the greatest when 28.5 cm (male, Supplementary Table 1) and 26.5 cm (female, Supplementary Table 2) were used as cut-off points. Therefore, we defined small calf circumference as < 28.5 cm for males, and < 26.5 cm for females, and thick calf circumference was defined as calf circumference ≥ 28.5 cm for males and ≥ 26.5 cm for females.
We used logistic regression models to assess the associations between calf circumference and incontinence. We included the calf circumference as a continuous variable, a binary variable (male < 34 cm, female < 33 cm), and a binary variable (male < 28.5 cm, female < 26.5 cm) in the model to compare the relationship between different divisions of calf circumference and incontinence. Furthermore, we fit two models stratified by gender. Model 1 did the univariate analysis. Model 2 further adjusted age, history of falling, smoking status, drinking status, physical exercise, limited ability of daily activities, intake of vegetables and fruits, socioeconomic status, waistline circumference, BMI, education, history of respiratory diseases, history of urinary system diseases, history of stroke or cardiovascular diseases, history of nerves system disease and history of pregnant (only in females). We calculated the odds rates (ORs) and $95\%$ confidence intervals ($95\%$ CI) to estimate the association between calf circumference and incontinence among males and females.
## Characteristics of participants
Our study included 14,989 participants aged above 60. Among them, 6,516 were males ($43.47\%$) and 8,473 were females ($56.53\%$). According to Table 1, for both genders, all demographic characteristics were statistically different between different incontinence status groups, except for history of pregnant. In male populations, there were statistical differences in drinking and smoking status between groups, while there were no such differences among females. For both males and females, taking nutritional supplements was not associated with incontinence. Table 1Baseline information of participantsVariablesMale ($$n = 6516$$)pFemale ($$n = 8473$$)pNot IncontinenceIncontinenceNot IncontinenceIncontinenceTotaln = 6175n = 341 < 0.001n = 7769n = 704 < 0.001Age in years, mean (sd)82.69 (10.70)93.43 (8.85) < 0.00186.20 (11.90)97.06 (7.49) < 0.001Calf circumference1, cm32.72 (5.84)30.14 (6.81) < 0.00129.88 (6.11)26.64 (6.82) < 0.001Calf circumference2, % < 0.001 < 0.001 ≥ 34 (Male) /33 (Female) < 34 (Male) /33 (Female)2601 (42.11)90 (26.39)2196 (28.27)89 (12.64)3574 (57.89251 (73.61)5573 (71.73)615 (87.36)Calf circumference3, % < 0.001 < 0.001 ≥ 28.5 (Male) /26.5 (Female)5080 (82.27)206 (60.41)5754 (74.06)320 (45.45) < 28.5 (Male) /26.5 (Female)1095 (17.73)135 (39.59)2015 (25.94)384 (54.55)Age group, % < 0.001 < 0.00160–70770 (12.47)4 (1.17)772 (9.94)2 (0.28)71–801787 (28.94)28 (8.21)1765 (22.72)24 (3.41)81–901737 (28.13)62 (18.18)1910 (24.58)79 (11.22)91–1001351 (21.88)129 (37.83)1668 (21.47)199 (28.27) > 100530 (8.58)118 (34.60)1654 (21.29)400 (56.82)Waist-to-Hip Ratio (WHR), %0.033 < 0.001 < 0.9 (Male) /0.8 (Female)2729 (44.19)171 (50.15)2792 (35.94)356 (50.57) ≥ 0.9 (Male) /0.8 (Female)3446 (55.81)170 (49.85)4977 (64.06)348 (49.43)Socioeconomic status, % < 0.001 < 0.001Poor613 (10.02)55 (16.42)788 (10.25)139 (20.26) General4144 (67.71)224 (66.87)5555 (72.26)471 (68.66)Good1363 (22.27)56 (16.72)1345 (17.49)76 (11.08)Education level, % < 0.001 < 0.001Illiterate1363 (26.45)132 (41.90)4489 (66.23)505 (80.03)Primary & Middle school3030 (58.79)137 (43.49)1950 (28.77)95 (15.06)University and above761 (14.77)46 (14.60)339 (5.00)31 (4.91)BMI, % < 0.001 < 0.001Emaciation1616 (26.17)184 (53.96)2805 (36.11)472 (67.05)Normal3800 (61.54)123 (36.07)3961 (50.98)173 (24.57)Overweight or obesity759 (12.29)34 (9.97)1003 (12.91)59 (8.38)Pregnant history, %/0.509Never//115 (1.70)13 (2.06)Equal or more than once//6654 (98.30)619 (97.94)Frequently eat fruits & vegetables, % < 0.001 < 0.001No120 (1.95)29 (8.53)192 (2.48)68 (9.69)Yes6033 (98.05)311 (91.47)7556 (97.52)634 (90.31)Smoking status, % < 0.0010.291No2591 (42.56)184 (54.44)7000 (91.94)631 (90.79)Yes3497 (57.44)154 (45.56)614 (8.06)64 (9.21)Drinking status, % < 0.0010.579No3313 (54.59)221 (65.77)6765 (89.24)615 (89.39)Yes2756 (45.41)115 (34.23)816 (10.76)73 (10.61)Exercise, % < 0.001 < 0.001No3356 (55.21)234 (70.27)5022 (65.97)548 (79.65)Yes2723 (44.79)99 (29.73)2590 (34.03)140 (20.35)Limited daily activities, % < 0.001 < 0.001No4479 (72.72)52 (15.29)4866 (62.84)72 (10.26)Yes1680 (27.28)288 (84.71)2878 (37.16)630 (89.74)Take dietary supplements, %0.3410.849No5482 (90.03)298 (88.43)6738 (87.96)606 (88.21)Yes607 (9.97)39 (11.57)922 (12.04)81 (11.79)History of Fall, % < 0.001No4981 (81.94)217 (64.97)5784 (75.77)452 (66.47)Yes1098 (18.06)117 (35.03)1850 (24.23)228 (33.53)Respiratory diseases history, % < 0.001 < 0.001No5447 (88.21)276 (80.94)7196 (92.62)629 (89.35)Yes728 (11.79)65 (19.06)573 (7.38)75 (10.65)Urinary disease history, % < 0.0010.258No5612 (90.88)281 (82.40)7691 (99.00)700 (99.43)Yes563 (9.12)60 (17.60)78 (1.00)4 (0.57)Stroke or cardiovascular disease history, % < 0.001 < 0.001No5520 (89.39)236 (69.21)7112 (91.54)573 (81.39)Yes655 (10.61)105 (30.79657 (8.46)131 (18.61)Nervous system disease history, % < 0.001 < 0.001No6075 (98.38)285 (83.58)7600 (97.82)573 (81.39)Yes100 (1.62)56 (16.42)169 (2.18)131 (18.61)Continuous normal distribution data were described by mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$($$\end{document}(standard deviations), and Student’s t-tests were used for comparison between groups. The categorized data were described by numbers (%), and were compared using the Chi-squared testCalf circumference1: Take calf circumference as a continuous variable. Calf circumference2: The calf circumference is divided into two categories. The cut-off value is 34 cm for males, and 33 cm for females. Calf circumference3: The calf circumference is divided into two categories. The cut-off value is 28.5 cm for males, and 26.5 cm for females Additionally, for the two different methods of division of calf circumference, there were both statistical differences in the distribution of different incontinence groups in both genders. When calf circumference was classified using a cut-off value of 34 cm for males and 33 cm for females, the prevalence of incontinence was $6.56\%$ for male with thick calf circumference (≥ 34 cm) and $3.35\%$ for those with small calf circumference; for female subjects with thick calf circumference (≥ 33 cm), the prevalence was $9.94\%$, and the prevalence was $3.89\%$ for those with thin. The prevalence of incontinence significantly increased when using $\frac{28.5}{26.5}$ cm as cut-off points for male and female population. $10.98\%$ of males with small calf circumference (< 28.5 cm) suffered from incontinence, while this proportion was $16.01\%$ among females with small calf circumference (< 26.5 cm).
## Association of calf circumference groups with incontinence status among elderly people
The prevalence of incontinence was $8.31\%$ in elderly females, significantly higher than elderly males ($5.23\%$) ($p \leq 0.001$) (Table 1). We found a significant non-linear association between calf circumference and risk of incontinence both in males and females (Fig. 2). To the left of the inflection point, smaller calf circumference was a risk factor for incontinence and the risk of incontinence decreased significantly with increasing calf circumference, whereas to the right of the inflection point, increasing calf circumference was a protective factor for incontinence, but increasing calf circumference had little effect on reducing the risk of incontinence. Fig. 2Association of calf circumference and incontinence by sex. A Calf circumference restricted cubic spline regression with 3 knots in male participants; red solid line represents association between calf circumference and incontinence, shaded areas are $95\%$ CIs. p for non-linear < 0.001. B Calf circumference restricted cubic spline regression with 3 knots in female participants; red solid line represents association between calf circumference and incontinence, shaded areas are $95\%$ CIs. p for non-linear < 0.001 Table 2 shows the results of the logistic regression. The results show that the association strength between calf circumference and incontinence was the highest when the cut-off points of calf circumference were 28.5 cm for males and 26.5 cm for females. The ORs were 3.040 for males ($95\%$ CI: 2.420, 3.809) and 3.427 for females ($95\%$ CI: 2.930, 4.010), indicating that the risk of incontinence in males and females with smaller calf circumference was 3.040 and 3.427 times higher than those with thicker calf circumference, respectively. After adjusting for the covariates, the strength of the association became smaller, but still remained statistically significant. Compared with thick calf circumference, subjects with thin calf circumference had a $62.0\%$ (male, OR: 1.620, $95\%$ CI: 1.197, 2.188) and $29.2\%$ (female, OR: 1.292, $95\%$ CI: 1.044,1.600) increased risk of developing incontinence, respectively. A significant association between calf circumference and incontinence was found in Model 1 when the calf circumference entered the models in the forms of binary variables (cut-off points 34 cm for males and 33 cm for females), but disappeared in Model 2 after adjusting the confounders. Table 2Association of calf circumference and incontinence by genderMaleFemaleNot IncontinenceIncontinenceNot IncontinenceIncontinenceContinuous calf circumference 1 Model 11(reference)0.927(0.910,0.944)*1(reference)0.909(0.897,0.922)* Model 21(reference)0.972(0.948,0.996)*1(reference)0.985(0.967,1.004)Binomial calf circumference (Male < 34 cm and Female < 33 cm as cut-off points) Model 11(reference)2.029(1.593,2.607)*1(reference)2.722(2.181,3.440)* Model 21(reference)1.218(0.882,1.691)1(reference)1.118(0.820,1.540)Binomial calf circumference (Male < 28.5 cm and Female < 26.5 cm as cut-off points) Model 11(reference)3.040(2.420,3.809)*1(reference)3.427(2.930,4.010)* Model 21(reference)1.620(1.197,2.188)*1(reference)1.292(1.044,1.600)*Model 1 was univariate model; Model 2 further adjusted age, history of falling, smoking status, drinking status, physical exercise, limited ability of daily activities, intake of vegetables and fruits, socioeconomic status, waistline circumference, BMI, education, history of respiratory diseases, history of urinary system diseases, history of stroke or cardiovascular diseases, history of nerves system disease and history of pregnant (only in females)1The calf circumference was included in the model as a continuous variable2The calf circumference was classified with male < 34 cm and female < 33 cm as cut-off points3The calf circumference was classified with male < 28.5 cm and female < 26.5 cm as cut-off points* $p \leq 0.05$ Supplementary tables 3, 4, and 5 show the detailed parameters of every variable in the models. With the increase of age, the association strength also increased, and these trends appeared in both men and women, indicating that age is an important risk factor for incontinence. However, the effect of BMI was opposite. The risk of incontinence in people with normal and overweight or obese BMI was significantly lower than thinner people.
## Discussion
This study is the first to examine the association between calf circumference and incontinence in elderly individuals living in Chinese communities. The results indicated that calf circumference was statistically associated with incontinence in the elderly. Calf circumferences of less than 28.5 cm in males and less than 26.5 cm in females were identified as risk factors for incontinence, which were 1.620 ($95\%$CI: 1.197, 2.188) and 1.292 ($95\%$CI: 1.044, 1.600) times greater risk of incontinence than those with larger calf circumference.
A total of 14,989 elderly people over 60 years old were included in this study, with males accounting for $43.47\%$ and females $56.53\%$. The research showed that the prevalence of incontinence in elderly males was $5.23\%$, significantly lower than among elderly females ($8.31\%$). Existing studies mostly classify incontinence into UI and FI. For example, the prevalence of FI in female over 65 years old in Taiwan, China was $9.3\%$ [23]. The prevalence of liquid FI in older US females was $7.9\%$ and that of solid FI was $6.5\%$ [24]. The results of The EPIC Study, conducted jointly by Canada and five other countries, showed the prevalence of UI in elderly males over 60 years old was $4.14\%$ and that in elderly females was $13.23\%$ [25]. The results of the above study are slightly different from those of this study, which may stem from the different types of incontinence studied.
The GLIM pointed out that the recommended cut-off values in the diagnosis of malnutrition were calf circumference in male < 33 cm and in female < 32 cm [4]. They were lower than the diagnostic parameters from the Chinese expert consensus on diagnosis and treatment for elderly with sarcopenia [2021], which for sarcopenia were "calf circumference < 34 cm for men and < 33 cm for women"[3]. The GLIM was aimed at the entire adult population including the young, middle-aged and the elderly population, while the Chinese expert consensus was aimed at the elderly population only. Therefore, calf circumference of 34 cm in males and 33 cm in females were used as the grouping index for this study. After adjusting for confounders, we found that calf circumference < 34 cm in males and < 33 cm in females were not statistically associated with incontinence in elderly. So, are there any other cutoff values for calf circumference associated with incontinence in elderly people? No studies have been reported. Based on the GLIM's advice, receiver operating characteristic(ROC) curve could be used to identify a new maximal cutoff when there were no data from a reference population[4].Further stratification by gender to predict incontinence in elderly based on the sensitivity, specificity, and the Youden index of ROC curve, we found that the association strength between calf circumference and incontinence was the highest when the cut-off points of calf circumference were < 28.5 cm for males and < 26.5 cm for females, and the risk of incontinence in males with calf circumference < 28.5 cm and females with calf circumference < 26.5 cm was 3.040 times (males) and 3.427 times (females) higher than people with thicker calf circumference. The findings of NHANES suggested that, for elder people, a moderately low calf circumference (1 SD below the mean) can be adequate for sarcopenia diagnosis / screening and all calf circumference mean values differed among the ethnic and race groups [26]. Meanwhile, studies had shown that UI was closely related to musculoskeletal conditions and impaired function among elderly people, and it was suggested that markers of sarcopenia could be used as important clinical predictors of UI in elderly females [27]. Therefore, we speculate that calf circumference can be used as a predictor of incontinence in the elderly people. Recent studies in Japan also found that the average calf circumference of patients with FI was significantly lower than that of patients without FI, which was statistically significant and consistent with our findings [28].
Previous studies had found that pelvic muscle mass decreased along with general muscle mass in elderly patients with UI, and atrophy and weakness of pelvic muscle led to UI [29]. Another study found a significant increase in the prevalence of pelvic floor dysfunction in females with sarcopenia, and suggested that the severity of pelvic floor dysfunction may be related to sarcopenia [27]. This study also found a correlation between incontinence and sarcopenia in elderly people, but the diagnostic parameters were different. Calf circumference gradually reduces with aging and muscle loss, when it reduces to the first critical value (< 34 cm for males, < 33 cm for females), the population may suffer from sarcopenia. At this time the pelvic floor muscles may not be seriously weakened and no incontinence symptoms will occur. However, if sarcopenia was not treated effectively and the calf circumference continued to shrink to the second critical value (< 28.5 cm for males, < 26.5 cm for females), the pelvic floor muscles would become sufficiently weakened to incur incontinence symptoms. In this study, the proportion of males with calf circumference < 28.5 cm was $18.5\%$, and females with calf circumference < 26.5 cm was $38.0\%$, indicating that muscular dystrophy in the elderly population was relatively serious, and should be met with intervention and timely treatment. At the same time, it also indicated that the diagnostic parameters of calf circumference < 28.5 cm for males and < 26.5 cm for females proposed in this study were reliable and could be used as a screening index of incontinence in the elderly population. We suggest carrying out large-scale screening of calf circumference during physical examination of the elderly population, detecting risk of incontinence, and providing prevention guidance early, to improve the quality of life for elderly.
The study found that the risk of incontinence was significantly reduced in normal BMI and overweight and obese individuals compared with those who were thinner. Current research had been inconsistent on the relationship between BMI and incontinence. The survey results of adult Chinese females by Peking Union Medical College Hospital showed that overweight and obese females were more likely to develop stress UI than those with normal BMI, and BMI \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥ 24 kg/m2 was a risk factor for FI [13, 30]. However, a recent study on female UI and pelvic floor muscle strength found that pelvic floor muscle strength would increase with the increase of BMI [31]. This contradiction may be caused by the different study populations. The elderly population, who often suffer multiple diseases, is different from the adult population, so the study of a single disease could not be conducted in isolation. Sarcopenia has been demonstrated to be prevalent in older females with UI and may be a clinical predictor of incontinence, influencing the treatment and management of the disease [32]. In this study, the relationship between BMI and incontinence cannot be generalized, and overweight/obesity may be a risk factor for incontinence in normal adult populations. For the elderly population with sarcopenia, overweight/obesity may be a protective factor for incontinence, which needs to be further discussed after the study population and indicators are refined. Therefore, we suggest that screening and intervention for sarcopenia should be carried out at the same time as the diagnosis of incontinence in elderly people [27]. Only the treatment of both diseases can achieve better results; otherwise, the presence of sarcopenia may affect the treatment effect of incontinence. The elderly people should improve their bad living habits and give up smoking and drinking. Varieties of exercises can be engaged to improve physical function, including resistance exercise, aerobic exercise, Kegel exercise, passive exercise, etc. Balanced diet and increasing the intake of high-quality protein, amino acids, vitamin D and other nutrients are suggested. Exercise combined with nutritional intervention can prevent and treat the sarcopenia, so as to reduce the incidence of incontinence caused by sarcopenia [33].
The limitation of this study is that the data come from the national general survey of the health status of the elderly population, and not a specially designed study for incontinence and sarcopenia. The incontinence-related data are incomplete and data from the International Consultation on Incontinence Questionnaire (ICIQ) is lacking. Moreover, it is a cross-sectional study. To clarify the relationship between sarcopenia and incontinence in elderly people, large-scale prospective cohort studies with different populations should be designed to obtain conclusions with a higher level of evidence.
## Conclusions
Incontinence in the elderly population is closely related to sarcopenia, and calf circumference is a convenient screening index of incontinence among elderly people. Screening and treatment of sarcopenia are routinely performed in elderly patients with incontinence. This study for the first time proposes that calf circumference < 28.5 cm in males and < 26.5 cm in females is a risk factor for incontinence in the elderly population, and it is suggested to measure calf circumference in routine physical examination of the elderly population and timely interventions should be made to reduce the risk of incontinence in subjects with calf circumference less than the threshold.
## Supplementary Information
Additional file 1: Supplementary Table 1. Coordinate points of ROC curve and Youden index in male population using logistic regression model. Supplementary Table 2. Coordinate points of ROC curve and Youden index in female population using logistic regression model. Supplementary Table 3. Association of calf circumference (continuous variable) and incontinence by sex. Supplementary Table 4. Association of calf circumference (cut-off points: 34 cm for male and 33 cm for female) and incontinence by sex. Supplementary Table 5. Association of calf circumference (cut-off points: 28.5 cm for male and 26.5 cm for female) and incontinence by sex.
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|
---
title: Prediction of the risk of developing end-stage renal diseases in newly diagnosed
type 2 diabetes mellitus using artificial intelligence algorithms
authors:
- Shuo-Ming Ou
- Ming-Tsun Tsai
- Kuo-Hua Lee
- Wei-Cheng Tseng
- Chih-Yu Yang
- Tz-Heng Chen
- Pin-Jie Bin
- Tzeng-Ji Chen
- Yao-Ping Lin
- Wayne Huey-Herng Sheu
- Yuan-Chia Chu
- Der-Cherng Tarng
journal: BioData Mining
year: 2023
pmcid: PMC10007785
doi: 10.1186/s13040-023-00324-2
license: CC BY 4.0
---
# Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms
## Abstract
### Objectives
Type 2 diabetes mellitus (T2DM) imposes a great burden on healthcare systems, and these patients experience higher long-term risks for developing end-stage renal disease (ESRD). Managing diabetic nephropathy becomes more challenging when kidney function starts declining. Therefore, developing predictive models for the risk of developing ESRD in newly diagnosed T2DM patients may be helpful in clinical settings.
### Methods
We established machine learning models constructed from a subset of clinical features collected from 53,477 newly diagnosed T2DM patients from January 2008 to December 2018 and then selected the best model. The cohort was divided, with $70\%$ and $30\%$ of patients randomly assigned to the training and testing sets, respectively.
### Results
The discriminative ability of our machine learning models, including logistic regression, extra tree classifier, random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine were evaluated across the cohort. XGBoost yielded the highest area under the receiver operating characteristic curve (AUC) of 0.953, followed by extra tree and GBDT, with AUC values of 0.952 and 0.938 on the testing dataset. The SHapley Additive explanation summary plot in the XGBoost model illustrated that the top five important features included baseline serum creatinine, mean serum creatine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, spot urine protein-to-creatinine ratio and female gender.
### Conclusions
Because our machine learning prediction models were based on routinely collected clinical features, they can be used as risk assessment tools for developing ESRD. By identifying high-risk patients, intervention strategies may be provided at an early stage.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13040-023-00324-2.
## Introduction
Type 2 diabetes mellitus (T2DM) is a major challenge to public health worldwide, and the assessment and management of this chronic disease impose a heavy economic burden [1, 2]. T2DM is associated with many complications and problematic symptoms, including micro- and macrovascular complications [3, 4]. Among these complications, diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) and is associated with a future risk of progression to end-stage renal disease (ESRD) [5, 6]. However, a diagnosis of DKD is often delayed, particularly in the early stages of the disease, because most patients remain asymptomatic with respect to kidney dysfunction [7]. Therefore, identifying DKD patients with a rapid decline in the estimated glomerular filtration rate (eGFR) might be helpful for allowing early nephroprotective treatment to be administered to delay or prevent the progression of kidney failure.
Previous large-scale population-based cohort studies have identified multiple factors potentially contributing to rapid eGFR decline, such as hypertension [8, 9], proteinuria [10], demographic factors, and underlying comorbidities [7]. A meta-analysis of demographic and clinical laboratory data from twenty cohorts representing 41,271 T2DM patients was conducted to develop a categorization point system for DKD prediction [11]. The prediction model achieved an average area under the receiver operating characteristic curve (AUC) of 0.765. Because electronic health record usage provides hundreds of clinical features and a large volume of data, prediction models using a categorization point system may be insufficient to effectively make use of unaligned and correlated data structures. Recently, artificial intelligence (AI) has changed modern procedures, and the progress of machine learning with big data analysis has improved the capacity of predictive model development [12].
In a cohort study consisting of diabetic patients, an AI model using logistic regression was developed to predict the progression of DKD according to 3073 features [13], and it achieved an AUC of 0.743 and an average accuracy of $71\%$. However, only logistic regression was applied in this study, and the predictive ability of other machine learning models with respect to renal function progression in diabetic patients remains unknown. In addition, in the abovementioned study, the AI model predicted DKD progression for 6 months after the enrollment period; therefore, its predictive ability for a longer follow-up period is unknown.
In our study, we used a large-scale newly diagnosed DM cohort to perform machine learning models by using clinical features, including demographic characteristics, comorbidities, laboratory data and concomitant medications from outpatient department and emergency room visits as well as hospital admissions, to predict the risks of developing ESRD with a long follow-up period. We also used SHapley Additive exPlanation (SHAP) values to evaluate the accurate attribution values for each important feature within machine learning models.
## Data sources and study population
During the period of January 2008 to December 2018, we constructed a T2DM 10-year retrospective longitudinal cohort based on the information of patients with newly diagnosed T2DM from the Big Data Center, which includes the detailed patient demographic, underlying comorbidities, medication prescriptions, and laboratory data from all inpatient, outpatient and emergency services [14]. Patients without at least two eGFR values were excluded from our analyses. In addition, we excluded T2DM patients who had undergone renal replacement therapy, such as hemodialysis, peritoneal dialysis, and kidney transplant, before the enrollment points. This study was approved by the Institutional Review Board (Taipei Veterans General Hospitals, Approval no. 2022–03-006 AC), and the need for informed consent was waived because the data were deidentified.
## Feature selection
We extracted 78 features used for machine learning, including demographic characteristics, underlying comorbidities, laboratory data and concomitant drugs. The demographic characteristics included age, gender, smoking and alcohol consumption. Underlying comorbidities included histories of hypertension, transient ischemic attack, ischemic stroke, hemorrhagic stroke, myocardial infarction, coronary artery disease, congestive heart failure, chronic liver disease, cirrhosis, peptic ulcer disease, autoimmune disease, chronic obstructive pulmonary disease, asthma, peripheral arterial occlusive disease, cancer, gout, atrial fibrillation, valvular heart disease and diabetic retinopathy. The laboratory data included baseline serum creatinine, mean serum creatinine assessed within 1 year before the diagnosis of T2DM, cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, calcium, phosphate, white blood cells, hemoglobin, albumin, alanine aminotransferase, aspartate aminotransferase, total bilirubin, direct bilirubin, alkaline phosphatase, gamma-glutamyl transferase, glycated hemoglobin, glucose, the international normalized ratio, activated partial thromboplastin time, high-sensitivity C-reactive protein, iron, thyroid-stimulating hormone, free thyroxine, and spot urine protein-to-creatinine ratio (UPCR). Concomitant medications included renin-angiotensin-aldosterone system (RAAS) inhibitors, alpha blockers, beta blockers, calcium channel blockers, warfarins, direct oral anticoagulants, aspirins, clopidogrels, nitrates, statins, diuretics, spironolactones, metformins, sulfonylureas, meglitinides, sodium–glucose cotransporter 2 inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, thiazolidinediones, alpha-glucosidase inhibitors, insulins, nonsteroidal anti-inflammatory drugs, cyclooxygenase-2 inhibitors, proton pump inhibitors, steroids, allopurinols, febuxostats and benzbromarones.
## Class definition
In our study, the class was annotated as 1 if there was ESRD occurrence during the follow-up periods (defined as eGFR < 15 ml/min/1.73 m2 or the receipt of maintenance dialysis or kidney transplant), and the class was annotated as 0 if there was no ESRD occurrence. We calculated eGFR using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations [15].
## Data cleaning and machine learning model development
Categorical variables are presented as numbers (proportions) and continuous parametric variables are shown as the median (interquartile ranges [IQRs]). To impute the missing values of the clinical features, the K-nearest neighbor (KNN) algorithm was used before the machine learning methods [16, 17]. For model development, the study cohort was randomly divided to create a $70\%$:$30\%$ training set to test set ratio. Because the number of ESRD cases was much smaller than the number of non-ESRD cases, we performed the synthetic minority over-sampling technique (SMOTE)-Tomek algorithms to balance the number of samples taken for imbalanced data [18, 19]. Six machine learning models, including logistic regression, extra trees [20], random forest [21], gradient boosting decision tree (GBDT) [22], extreme gradient boosting models (XGBoost) [23], and light gradient boosting machine (LGBM) [24], are performed. We used forward-feature selection for the reduction in dimensions, which selects the most useful subset of features from all available features [25, 26]. Five-fold cross-validation is performed on the training set to estimate the performance and validate the stability of the applied machine learning models [27, 28].
## Hyperparameter optimization
A grid search in combination with the five-fold cross-validation was conducted to optimize the hyperparameters of logistic regression, extra trees, random forest, GBDT, XGBoost, and LGBM to achieve the best F1 score [29–31]. The details of hyperparameter optimization for each ensemble model are listed in Table 1. Grid searches determine the best hyperparameter value based on a set of given values. Table 1Hyperparameters of machine learning modelsModelHyperparametersRangeOptimal valuesLogistic regressionpenalty[l1, l2]l2Cs[0.001, 0.1, 1, 100, 1000]1Extra treesmin_samples_leaf[5, 8, 10]5criterion[gini, entropy, log_loss]entropymax_features[sqrt, log2, none]sqrtRandom forestmax_depth[3, 5, 10]10min_samples_split[2, 5, 10]5GBDTlearning_rate[0.01, 0.1, 0.2]0.2max_depth[3, 5, 8]8n_estimators[10, 20]20XGBoostgamma[0.5, 1, 5]0.5colsample_bytree[0.6, 0.8, 1.0]1.0max_depth[3, 4, 5]5LGBMn_estimators[8, 16, 24]24num_leaves[6, 12, 16]16max_bin[255, 510]510Abbreviations: GBDT gradient boosting decision tree, XGBoost extreme gradient boosting, LGBM light gradient boosting machine
## Model evaluation
The discriminative abilities of the different machine learning models were compared based on their AUCs. In addition, the F1 score, accuracy, precision, recall, average precision and log loss values of each model by using testing dataset were also presented. SHapley Additive exPlanations (SHAP) was used to evaluate the risk of developing ESRD in T2DM and to provide explanations for the attribution values of clinical features in a unified framework to interpret model predictions.
## Software and package applicating for modeling
We used Python (Python Software Foundation version 3.7.6, available at http://www.python.org) and open-source Scikit-learn library for the establishment of machine learning models and SAS version 9.4 (SAS Institute, Cary, NC) for statistical analysis [32]. We used Python and Scikit-learn library packages, including sklearn.impute. KNNImputer for missing value imputation, sklearn.model_selection.train_test_split for randomly dividing data into train and test sets, sklearn.model_selection. GridSearchCV for hyperparameter optimization, sklearn.linear_model. LogisticRegression for development of the logistic regression model, sklearn.ensemble. ExtraTreesClassifier for development of the extra tree model, sklearn.ensemble. RandomForestClassifier for development of the random forest model, sklearn.ensemble. GradientBoostingClassifier for development of the GBDT model, XGBoost Python package for development of the XGBoost model, lightgbm. LGBMClassifier Python package for development of the LGBM model, and sklearn.model_selection. StratifiedKFold for cross-validation. A P value of 0.05 was considered statistically significant.
## Characteristics and distribution of patients
A total of 105,234 T2DM patients aged > 20 years old were identified during the 10-year study period, of whom 34,059 had no eGFR measurements, 16,351 did not have at least two eGFR values, and 1347 patients receiving renal replacement therapy were excluded, which resulted in a final cohort of 53,477 T2DM patients. The detailed patient demographic data are provided in Table 2. The median patient age was 67.05 years (IQR 57.37 to 77.74 years), and $41.4\%$ of the patients were female. In addition, $58.2\%$ of patients had hypertension, $19.8\%$ had coronary artery disease, and $23.4\%$ had cancer. Regarding renal function, T2DM patients had baseline serum creatinine levels of 0.94 mg/dL (IQR 0.75 to 1.27 mg/dL), mean serum creatinine of 0.95 mg/dL (IQR 0.76 to 1.26 mg/dL) within 1 year before the diagnosis of T2DM. The dataset was randomly divided into a training set ($70\%$) and a testing set ($30\%$). Of all the T2DM patients, 4769 ($8.9\%$) patients developed ESRD. A total of 3334 ($8.9\%$) patients developed ESRD on the training set, and 1435 ($8.9\%$) patients developed ESRD on the testing set. Table 2Demographics and clinical features between T2DM patientsFull cohortTraining setTesting set($$n = 53$$,477)($$n = 37$$,433)($$n = 16$$,044)*Demographic data* Age, years67.05 [57.37, 77.74]67.09 [57.46, 77.78]66.97 [57.20, 77.66] Female gender,n(%)22,162 (41.4)15,508 (41.4)6654 (41.5) Smoking,n(%)12,424 (23.2)8690 (23.2)3734 (23.3) Alcohol consumption,n(%)9117 (17.0)6438 (17.2)2679 (16.7)Underlying comorbidities Hypertension,n(%)31,142 (58.2)21,816 (58.3)9326 (58.1) Transient ischemic attack,n(%)541 (1.0)371 (1.0)170 (1.1) Ischemic stroke,n(%)3359 (6.3)2339 (6.2)1020 (6.4) Hemorrhagic stroke,n(%)758 (1.4)533 (1.4)225 (1.4) Myocardial infarction,n(%)1806 (3.4)1251 (3.3)555 (3.5) Coronary artery disease,n(%)10,585 (19.8)7449 (19.9)3136 (19.5) CHF,n(%)3032 (5.7)2139 (5.7)893 (5.6) Chronic liver disease,n(%)4377 (8.2)3085 (8.2)1292 (8.1) Cirrhosis,n(%)1193 (2.2)859 (2.3)334 (2.1) Peptic ulcer disease,n(%)4613 (8.6)3238 (8.7)1375 (8.6) Autoimmune disease,n(%)508 (0.9)360 (1.0)148 (0.9) COPD,n(%)2881 (5.4)2026 (5.4)855 (5.3) Asthma,n(%)1132 (2.1)784 (2.1)348 (2.2) PAOD,n(%)99 (0.2)74 (0.2)25 (0.2) Cancer,n(%)12,513 (23.4)8788 (23.5)3725 (23.2) Gout,n(%)2445 (4.6)1751 (4.7)694 (4.3) Atrial fibrillation,n(%)1589 (3.0)1133 (3.0)456 (2.8) Valvular heart disease,n(%)1311 (2.5)917 (2.4)394 (2.5) Diabetic retinopathy,n(%)2744 (5.1)1911 (5.1)833 (5.2)*Laboratory data* at the diagnosis of T2DM Creatinine, mg/dL *Baseline serum* creatinine, mg/dL0.94 [0.75, 1.27]0.94 [0.76, 1.27]0.94 [0.75, 1.27] *Mean serum* creatinine, mg/dLa0.95 [0.76, 1.26]0.95 [0.76, 1.26]0.94 [0.76, 1.26]Cholesterol, mg/dL175.00 [155.00, 195.20]175.00 [155.00, 195.00]175.00 [155.95, 196.00]Triglyceride, mg/dL129.40 [94.00, 177.00]130.00 [94.60, 177.00]129.00 [94.00, 176.00]LDL-C, mg/dL104.00 [89.20, 120.00]104.00 [89.00, 120.00]104.40 [89.80, 120.40]HDL-C, mg/dL44.60 [39.00, 50.60]44.60 [39.00, 50.60]44.80 [39.00, 50.80]Uric acid, mg/dL5.98 [5.00, 7.00]5.98 [5.00, 7.00]5.96 [5.00, 7.00]Calcium, mg/dL9.18 [8.92, 9.40]9.18 [8.92, 9.40]9.16 [8.92, 9.40]Phosphate, mg/dL3.30 [3.04, 3.54]3.30 [3.04, 3.54]3.30 [3.04, 3.56]White blood cells, /mm37300 [6100, 8860]7300 [6100, 8860]7300 [6100, 8820]Hemoglobin, g/dL12.60 [11.40, 13.70]12.60 [11.40, 13.70]12.60 [11.40, 13.70]Albumin, g/dL3.90 [3.58, 4.16]3.90 [3.58, 4.16]3.90 [3.56, 4.16]Alanine transaminase, U/L23.00 [17.00, 35.00]23.00 [17.00, 35.00]23.00 [17.00, 35.00]Aspartate transaminase, U/L23.20 [19.00, 31.00]23.20 [19.00, 31.00]23.20 [18.80, 31.00]Total bilirubin, mg/dL0.69 [0.51, 1.00]0.69 [0.51, 1.00]0.69 [0.51, 0.99]Direct bilirubin, mg/dL0.26 [0.17, 0.38]0.26 [0.17, 0.38]0.26 [0.17, 0.38]Alkaline phosphatase, U/L84.00 [71.20, 104.20]84.00 [71.20, 104.00]84.00 [71.00, 104.20]Gamma-glutamyl transferase, U/L43.00 [28.80, 73.60]43.00 [28.80, 73.80]43.40 [28.80, 73.40]HbA1c, %7.70 [6.90, 9.42]7.70 [6.90, 9.43]7.70 [6.88, 9.40]Glucose, mg/dL142.00 [117.00, 186.00]142.00 [117.00, 186.00]142.00 [117.00, 186.00]INR1.02 [0.99, 1.10]1.02 [0.99, 1.10]1.02 [0.99, 1.10]aPTT, seconds57.56 [48.96, 85.10]57.56 [48.96, 85.10]57.37 [48.96, 85.10]Hs-CRP, mg/dL1.08 [0.32, 1.52]1.08 [0.32, 1.53]1.08 [0.32, 1.43]Iron, μg/dL64.60 [54.00, 77.20]64.60 [54.00, 77.00]64.40 [53.80, 77.20]TSH, uIU/mL1.77 [1.22, 2.31]1.76 [1.22, 2.31]1.78 [1.22, 2.31]Free T4, ng/dL1.08 [1.01, 1.17]1.08 [1.01, 1.17]1.08 [1.01, 1.17]Spot urine protein-creatinine ratio, g/g2.28 [0.50, 3.43]2.32 [0.51, 3.43]2.19 [0.49, 3.41]Concomitant medications RAAS inhibitors,n(%)30,111 (56.3)21,093 (56.3)9018 (56.2) Alpha, blocker,n(%)14,189 (26.5)9927 (26.5)4262 (26.6) Beta blocker,n(%)21,922 (41.0)15,333 (41.0)6589 (41.1) CCB,n(%)27,749 (51.9)19,465 (52.0)8284 (51.6) Warfarin,n(%)1761 (3.3)1234 (3.3)527 (3.3) DOAC,n(%)138 (0.3)94 (0.3)44 (0.3) Aspirin,n(%)16,713 (31.3)11,766 (31.4)4947 (30.8) Clopidogrel, n(%)6709 (12.5)4770 (12.7)1939 (12.1) Nitrate,n(%)12,810 (24.0)8960 (23.9)3850 (24.0) Statin,n(%)22,656 (42.4)15,843 (42.3)6813 (42.5) Diuretic,n(%)18,660 (34.9)13,073 (34.9)5587 (34.8) Spironolactone,n(%)5066 (9.5)3568 (9.5)1498 (9.3) Metformin,n(%)37,396 (69.9)26,158 (69.9)11,238 (70.0) Sulfonylurea,n(%)25,202 (47.1)17,631 (47.1)7571 (47.2) Meglitinide,n(%)8625 (16.1)6024 (16.1)2601 (16.2) SGLT2 inhibitor,n(%)505 (0.9)358 (1.0)147 (0.9) GLP1 receptor agonist,n(%)78 (0.1)60 (0.2)18 (0.1) Dipeptidyl peptidase-4 inhibitor,n(%)16,164 (30.2)11,299 (30.2)4865 (30.3) Thiazolidinedione,n(%)4847 (9.1)3408 (9.1)1439 (9.0) Alpha-glucosidase inhibitor,n(%)8530 (16.0)5956 (15.9)2574 (16.0) Insulin,n(%)26,752 (50.0)18,790 (50.2)7962 (49.6) NSAID,n(%)26,349 (49.3)18,450 (49.3)7899 (49.2) COX-2 inhibitor,n(%)7230 (13.5)5037 (13.5)2193 (13.7) Proton pump inhibitor,n(%)14,700 (27.5)10,267 (27.4)4433 (27.6) Steroid,n(%)8747 (16.4)6100 (16.3)2647 (16.5) Allopurinol,n(%)2466 (4.6)1711 (4.6)755 (4.7) Febuxostat,n(%)1065 (2.0)734 (2.0)331 (2.1) Benzbromarone,n(%)4388 (8.2)3078 (8.2)1310 (8.2)Abbreviations: T2DM type 2 diabetes mellitus, CHF congestive heart failure, COPD chronic obstructive pulmonary disease, PAOD peripheral arterial occlusive disease, LDL-C low-density lipoprotein cholesterol, HDL-C high density lipoprotein-cholesterol, HbA1c glycated hemoglobin, INR international normalized ratio, aPTT activated partial thromboplastin time, Hs-CRP high-sensitivity C-reactive protein, TSH thyroid stimulating hormone, T4 thyroxine, RAAS renin-angiotensin system, CCB calcium channel blocker, DOAC direct oral anticoagulant, SGLT2 sodium-glucose cotransporter 2, GLP1 glucagon-like peptide-1, NSAID nonsteroidal anti-inflammatory drug, COX-2 cyclooxygenase-2a The mean serum creatinine value assessed within 1 year before the diagnosis of T2DM
## Model prediction ability
Six machine learning models, i.e., logistic regression, extra tree classifier, random forest, GBDT, XGBoost, and LGBM, were performed, and the AUCs and other performance indices, such as accuracy, F1 score, precision, recall and average precision achieved by the machine learning models after data augmentation are presented in Supplementary Table 1. The AUCs resulting from 5-fold cross-validation of XGBoost models with a mean of 0.984 (Supplementary Fig. 1). On the testing dataset, AUCs showed that the XGBoost model had the highest predictive ability, with an AUC of 0.953, followed by the extra tree model with an AUC of 0.952 (Fig. 1).Fig. 1A Receiver operating characteristic curves and B precision–recall curves of machine learning models on the testing dataset. C XGBoost yielded the highest area under the ROC curve for prediction of end-stage renal disease followed by extra trees classifier and GBDT on the testing dataset. Abbreviations: ROC, receiver operating characteristic; PR, precision–recall; AUC, area under curve of receiver operating characteristic curve; A.precision, average precision; AUC PRC, area under curve of precision-recall curve; GBDT, gradient boosting decision tree; XGBoost, extreme gradient boosting; LGBM, light gradient boosting machine
## Ranks of feature importance and SHAP values in the XGBoost model
We performed feature importance plots of the XGBoost model based on the SHAP values and listed the top important features sorted by the impacts in descending order (Fig. 2A). The top five important features were baseline serum creatinine, mean serum creatinine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, UPCR and female gender. The impacts of feature importance on model output were also illustrated in the SHAP summary plot (Fig. 2B). Higher SHAP values of important features indicate a higher probability of impacts of the prediction in the XGBoost model. SHAP values in red dots indicate an increase in prediction, while those in blue dots indicate a decrease in prediction. Baseline serum creatinine, mean serum creatinine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, and UPCR showed positive impacts on the prediction of developing ESRD risk, while the female gender showed a negative impact. Fig. 2A The feature importance plot and B SHAP summary plot showed the top clinical important features for predicting risks of developing end-stage renal disease in the XGBoost model. Abbreviations: XGBoost, extreme gradient boosting; HSCRP, high-sensitivity C-reactive protein; UPCR, spot urine protein-to-creatinine ratio; ALT, alanine transaminase; DPP4i, dipeptidyl peptidase 4 inhibitors; HGB, hemoglobin; HbA1c, glycated hemoglobin; ALB, albumin; NSAID, nonsteroidal anti-inflammatory drug; HTN, hypertension; INR, international normalized ratio; PI, phosphate
## The dependent plots of interactions between serum creatinine, high-sensitivity C-reactive protein, UPCR and female gender
As shown in Fig. 3, the dependent plots illustrated the SHAP values and the interactions between serum creatinine, high-sensitivity C-reactive protein, UPCR and female gender in the XGBoost model. The risks of developing ESRD increased as baseline or mean serum creatinine increased and then reached a plateau when creatinine > 5 mg/dL (Fig. 3A–B). Figure 3C–F illustrates the interaction between the SHAP values of baseline serum creatinine, mean serum creatinine, high-sensitivity C-reactive protein, UPCR and female gender. The values on the y-axis indicate the interaction SHAP values between baseline serum creatinine and other important features, and values on the x-axis are the levels of baseline serum creatinine. Mean serum creatinine, high-sensitivity C-reactive protein, UPCR and female gender were positively correlated with the predictive value of baseline serum creatinine. Fig. 3The plots of SHAP value of (A) baseline serum creatinine and (B) mean serum creatinine within 1 year before diagnosis showed increased creatinine levels were associated with increased SHAP values. SHAP interaction plots showed the interaction impacts between baseline serum creatinine and (C) mean serum creatinine (D) HSCRP, (E) UPCR, and (F) female gender on the prediction model’s output. Abbreviations: SHAP, SHapley Additive exPlanations; HSCRP, high-sensitivity C-reactive protein; UPCR, spot urine protein-creatinine ratio
## Discussion
In the current study, we developed machine learning models to predict the development of ESRD among T2DM patients based on electronic medical records. We used the machine learning system to conduct feature selection and compare the AUCs among the different machine learning models. We found that the XGBoost model had the highest predictive performance with the highest AUC of 0.953 on the testing dataset compared to other machine learning algorithms. The top five important features were baseline serum creatinine, mean serum creatinine within 1 year before the diagnosis of T2DM, high-sensitivity C-reactive protein, UPCR and female gender.
Previous studies in nondiabetic populations have attempted to find useful markers to predict ESRD. A Norwegian large-scale general health study including 65,589 adults aged > 20 years from 1995 through 1997 established a clinical predictive model (incorporating age, gender, physical activity, diabetes, systolic blood pressure, antihypertensive medication, and high-density lipoprotein) for the future risk of ESRD, and the AUC reached 0.864 [33]. After adding albuminuria and eGFR, the AUC of the model was increased to 0.936. Ishani et al [34]. studied 12,866 men who were at high risk for heart disease and found that dipstick proteinuria, eGFR < 60 ml/min/1.73 m2, and hematocrit were related to the development of ESRD. Because the study populations were limited to nondiabetic populations, the findings of these studies may not be generalizable to T2DM groups. For diabetic patients, proteinuria [35, 36], diabetic retinopathy [37, 38], increased glycated hemoglobin levels [39], hypertension [40], and cardiovascular diseases [41, 42] may precede kidney function decline and have been demonstrated to be associated with renal function progression.
A customized software program for CKD risk identification in Australia (the Electronic Diagnosis and Management Assistance to Primary Care in Chronic Kidney Disease (eMAP:CKD) program) was developed to integrate primary care electronic health records from more than 150,000 patients [43]. After the initiation of the program, there was a significant improvement in CKD documentation from 0.48 to $1.55\%$. In addition, the proportions of at-risk patients diagnosed with CKD at 15 months were found to be significantly increased from 7.8 to $24.40\%$. Furthermore, recent studies have applied AI to predict the risks of CKD. Kanda et al. [ 44] conducted a study including 7465 subjects and found that AI models with support vector machine (SVM) models can help predict CKD progression in both high-risk and low-risk subjects. After the 3-year follow-up, the accuracy of the SVM models was increased. Chen et al. [ 45] used three different models, i.e., K-nearest neighbor (KNN), SVM, and soft independent modeling of class analogy (SIMCA), to analyze data from 386 patients with or without CKD for clinical risk assessment and achieved accuracies over $93\%$. In their study, KNN and SVM achieved better performance than SIMCA. Almansour et al. [ 46] studied data from 400 patients with the goal of diagnosing CKD at an early stage and found that artificial neural networks (accuracy: $99.75\%$) performed better than SVMs (accuracy: $97.75\%$).
Although several studies have developed machine-learning models to detect diabetes and diabetic complications, to date, only one machine learning model has been developed to detect renal function progression in diabetic patients. Makino et al. [ 13] conducted a longitudinal data analysis with big data representing diabetes patients with stage 1 to 2 diabetic nephropathy and found that logistic regression models can predict DKD aggravation with $71\%$ accuracy. A higher risk of hemodialysis was associated with DKD aggravation than with nonaggravation. However, the study was limited to the early stage of DKD and a single machine learning model with logistic regression. In our study, we found that the machine learning XGBoost model predicted the risk of developing ESRD, achieving an AUC value of 0.953 on the testing dataset.
With a positive SHAP value, the machine learning models revealed that baseline serum creatinine showed the greatest impact on predicting the risk of developing ESRD. A previous study found that better baseline renal function was protective against renal function decline [47]. Our models also found that mean serum creatinine within 1 year before diagnosis of T2DM was an important predictor of developing ESRD. The possible explanation may be that mean serum creatinine is reflective of the usual renal status. According to the SHAP dependence plots, the interaction with high-sensitivity C-reactive protein increases the prediction of risks of developing ESRD. Elevated high-sensitivity C-reactive protein was found to be independently associated with an increased risk of renal function decline in patients with diabetes and the general non-diabetic population [48, 49]. Higher UPCR levels at the time of diagnosis of T2DM were also associated with higher risks of developing ESRD, which was similar to previous research that found a positive correlation between UPCR and ESRD [50]. In contrast, female gender was associated with lower SHAP values and decreased risks of developing ESRD. A previous study also found that renal function decline in women was slower compared to men among middle-aged and elderly individuals [51].
Our study has several strengths. We established a predictive model by inputting big EMR data into the machine learning algorithm. The novelty of this study is the use of a 10-year longitudinal cohort to predict the risk of developing ESRD in newly diagnosed T2DM patients with baseline median creatinine of 0.94 mg/dL. The machine learning algorithm compared discriminative ability among different machine learning models and selected the best models. This approach offers not only improvement in AUCs but also selection of the best predicting model in cases where it is unclear what machine learning models are most suitable. In addition, the SHAP algorithm was used to interpret the model predictions, and the impacts of important features on developing ESRD were explored. Using SHAP summary plots, we demonstrated the strength and direction of each feature (positive or negative effects).
Our study also has real and perceived limitations. First, as patient information, including demographic data, underlying comorbidities and concomitant medications, was obtained from electronic health record systems and coding procedures, we could not identify mild diseases without coding in T2DM patients. Second, the inclusion of data on the duration and frequency of laboratory visits was not uniform but varied among patients. Finally, the training data and testing data were from the same dataset. Further validation in other cohorts is necessary.
## Conclusion
Our machine learning models employing longitudinal data from electronic health records were effective in predicting the risks of developing ESRD in T2DM patients in real-world clinical scenarios over a 10-year study period of observation. In addition, we used the SHAP method to provide explanations for the selected features to interpret model predictions. The developed model has the potential to predict the T2DM patients at increased risks for developing ESRD and thus, consequently initiating prevention or treatment plans for patients. In the future, external validation studies are necessary to convenient machine learning models to be developed for widespread use in clinical practice.
## Supplementary Information
Additional file 1: Supplementary Table 1. The performance of machine learning models after data augmentation for predicting the risk of end-stage renal disease in newly diagnosed type 2 diabetes mellitus. Supplementary Figure 1. Area under the receiver operating characteristic curve for the 5-fold cross-validation of the XGBoost machine learning models.
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|
---
title: Prevalence of sarcopenia in older women and level of agreement between the
diagnostic instruments proposed by the European Working Group on Sarcopenia in Older
People 2 (EWGSOP2)
authors:
- Daiana Vieira Sutil
- Adriana Netto Parentoni
- Leonardo Augusto Da Costa Teixeira
- Bruno de Souza Moreira
- Amanda Aparecida Oliveira Leopoldino
- Vanessa Amaral Mendonça
- Ana Cristina Rodrigues Lacerda
- Ana Lúcia Danielewicz
- Núbia Carelli Pereira de Avelar
journal: BMC Musculoskeletal Disorders
year: 2023
pmcid: PMC10007796
doi: 10.1186/s12891-023-06287-z
license: CC BY 4.0
---
# Prevalence of sarcopenia in older women and level of agreement between the diagnostic instruments proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2)
## Abstract
### Background
The European Working Group on Sarcopenia in Older People 2 (EWGSOP2) proposed the use of different diagnostic tools to assess sarcopenia. This study aimed to determine prevalence rates of sarcopenia according to the diagnostic instruments proposed by EWGSOP2 and to assess their level of agreement in older Brazilian women.
### Methods
A cross-sectional study with 161 community-dwelling older Brazilian women. Probable sarcopenia was assessed through Handgrip Strength (HGS) and the 5-times sit-to-stand test (5XSST). In addition to reduced strength, Appendicular Skeletal Muscle Mass (ASM) (obtained by Dual-energy X-ray absorptiometry) and ASM/height² were considered for diagnosis confirmation. Sarcopenia severity was determined by reduced muscle strength and mass and poor functional performance assessed by Gait Speed (GS), Short Physical Performance Battery (SPPB), and Timed Up and Go test (TUG). McNemar’s test and Cochran’s Q-test were used to compare sarcopenia prevalence. Cohen’s Kappa and Fleiss’s Kappa tests were used to assess the level of agreement.
### Results
The prevalence of probable sarcopenia was significantly different ($p \leq 0.05$) when using HGS ($12.8\%$) and 5XSST ($40.6\%$). Regarding confirmed sarcopenia, the prevalence was lower when using ASM/height² than with ASM. Regarding severity, the use of SPPB resulted in a higher prevalence in relation to GS and TUG.
### Conclusion
There were differences in the prevalence rates of sarcopenia and low agreement between the diagnostic instruments proposed by the EWGSOP2. The findings suggest that these issues must be considered in the discussion on the concept and assessment of sarcopenia, which could ultimately help to better identify patients with this disease in different populations.
## Introduction
Sarcopenia is a disease (ICD-10-MC) diagnosed by a reduction in the quality and/or quantity of muscle mass, which occurs due to gradual and generalized muscle changes [1]. Furthermore, the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) defined that muscle strength reduction should be considered the first stage in screening for this condition [1]. For diagnostic confirmation, in addition to the reduction in muscle strength, reduced muscle quantity and/or quality should be observed. The severity of sarcopenia would then be defined by changes in strength, muscle quantity/quality, and poor functional performance assessed by Gait Speed (GS), Short Physical Performance Battery (SPPB), or Timed Up and Go test (TUG) [1].
Recent estimates suggest that the overall prevalence of sarcopenia in older adults can range from $10.0\%$ [2] to $82.1\%$ [3, 4]. In addition, previous studies show that the prevalence of sarcopenia is higher in older women than in older men [5–7]. A possible explanation for the higher prevalence in women may be related to hormonal aspects and less muscle mass [8, 9]. Additionally, women present cumulative disadvantages throughout life, including poor access to education, income, and food, which consequently leads to a greater likelihood of poverty and, therefore, greater health problems and disabilities in old age [10].
Evidence suggests that the prevalence of sarcopenia can vary depending on the diagnostic algorithm, such as EWGSOP1, EWGSOP2, Asia Working Group for Sarcopenia (AWGS), International Working Group on Sarcopenia (IWGS), and Foundation for the National Institutes of Health (FNIH) [11–18]. Recently, Anand et al. [ 2022] demonstrated weak agreement between diagnostic criteria for sarcopenia when using different diagnostic algorithms [3].
Although the literature reports a lack of agreement between different algorithms [3, 11–18], to our knowledge, no previous study has investigated the differences in the prevalence of sarcopenia and the level of agreement between the diagnostic instruments proposed within the same consensus. The most current recommendation on sarcopenia is from EWGSOP2, which suggests strategies for screening (probable sarcopenia), diagnostic confirmation (confirmed sarcopenia), and severity of the disease. It is expected that the findings of this study provide guidance to health professionals and public managers on the choice of instruments used for sarcopenia diagnosis, which is essential to the planning of health service actions, such as the establishment of preventive approaches and therapeutic strategies for this condition. Thus, the aims of this study were to determine the prevalence rates of sarcopenia in older women according to the diagnostic instruments proposed by EWGSOP2 and to assess their level of agreement.
## Study design
This was a cross-sectional study conducted with community-dwelling older women, which was approved by the Research Ethics Committee of the Universidade Federal dos Vales do Jequitinhonha e Mucuri (Federal University of the Jequitinhonha and Mucuri Valleys) (protocol no. 1.461.306), following the principles described in the Declaration of Helsinki.
## Eligibility criteria
Older women aged 65 years and over residing in the community and able to walk independently were included. The exclusion criteria were: (a) younger than 65 years old; (b) cognitive decline detectable by the Mini-Mental State Examination, considering the Brazilian cutoff points related to schooling, proposed by Bertolucci et al. [ 19]: 13 points for illiterates; 18 points for people with 1 to 7 years of schooling; 26 points for those with 8 years or more of schooling; (c) neurological sequelae that could interfere in the results of the tests proposed by EWSGOP2 [Handgrip Strength (HGS), 5-times sit-to-stand test (5XSST), GS, SPPB, and TUG]; (d) hospitalization in the last three months; (e) fractures in the lower limbs for less than six months and with orthopedic problems; (f) musculoskeletal, respiratory, cardiovascular, and thyroid diseases or other inflammatory diseases in the acute phase; (g) practicing physical activity on a regular basis (at least three times a week); (h) presence of metal in their bodies; (i) visual or hearing impairment; or (j) bedridden.
## Procedures
Participants were selected for convenience and recruited through calls, invitations, and announcements in Basic Health Units, public places, and a geriatric office in Diamantina, Minas Gerais, Brazil. The older women were asked about the eligibility criteria of the study, as well as use of medication, history of falls in the last 6 months, and level of physical activity. All participants signed an informed consent form.
Data were collected between June 2016 and June 2017 by trained healthcare professionals at the Exercise Physiology Laboratory of the Universidade Federal dos Vales do Jequitinhonha e Mucuri. The evaluators who performed the Appendicular Skeletal Muscle Mass (ASM) measurements using Dual-energy X-ray absorptiometry (DXA) were different from those who applied the functional tests. Initially, the women were submitted to an anthropometric evaluation (body mass and height) and then the ASM. Both assessments were performed under fasting conditions. Subsequently, the functional tests were performed: HGS, 5XSST, SPPB, GS, and TUG. The sequence of execution of the functional tests was randomly determined.
## Instruments for diagnosing probable sarcopenia
To screen for probable sarcopenia, the HGS and 5XSST were used.
## HGS
The participants performed an isometric contraction applied on the Jamar® hand dynamometer, in a sitting position, with shoulder and wrist in a neutral position and elbow at 90 degrees of flexion [20]. Three measurements were performed with the dominant hand and the highest value among the three measurements was used in the analyses. A value lower than 16kgf is indicative of probable sarcopenia [1].
## 5XSST
To perform the test, the time taken by the participants to rise from and sit on a chair five times, as fast as possible, with the upper limbs crossed over the chest, was recorded [21]. Taking more than 15 s to perform the test is indicative of probable sarcopenia [1].
## Instruments for confirming the sarcopenia diagnosis
In addition to the reduction in muscle strength assessed by HGS or 5XSST, it is necessary to assess the ASM using DXA (Lunar Radiation Corporation, Madison, Wisconsin, USA, DPX model) to confirm the sarcopenia diagnosis.
For ASM measurement, the participants had to wear light clothes and not have metallic objects in or on their bodies. They were positioned in the scanning area of the equipment so that the sagittal line passed through the center of anatomical points such as the skull, spine, pelvis, and legs. For optimal positioning, Velcro bands joined the legs, knees, and feet. Data on lean and fat muscle mass were collected. Data adjusted for height (ASM/height²) were also obtained. The presence of sarcopenia was confirmed when ASM and ASM/height2 were lower than 15 kg and 5.5 kg/m2, respectively [1].
## Instruments for assessing sarcopenia severity
To assess sarcopenia severity, participants with confirmed sarcopenia performed three functional tests: Gait Speed (GS), Short Physical Performance Battery (SPPB), and Timed Up and Go test (TUG).
To assess GS, the participants walked a four-meter distance at a comfortable/habitual pace. Timing started when one of the feet crossed the starting line and ended when one of the feet completely crossed the finish line [22]. GS (m/s) was obtained by dividing the distance traveled (m) by the time (s). A GS lower than or equal to 0.8 m/s is indicative of severe sarcopenia [1].
The SPPB is a battery of tests used to objectively assess lower limb function in older adults through three tests: static body balance, lower limb muscle strength, and gait. For each of the tests, scores range from 0 to 4 points, with a maximum score on the instrument of 12 points. The higher the score, the better the performance [23]. A score less than or equal to 8 is indicative of severe sarcopenia [1].
The TUG is a test that consists of recording the time required by the individual to get up from a chair, walk three meters, pivot around an obstacle, return, and sit down again. The longer the time to perform the test, the worse the functional performance [24]. A time greater than or equal to 20 s is indicative of severe sarcopenia [1].
## Sample calculation
The sample calculation was performed considering the sarcopenia prevalence of $4.6\%$ in older Brazilian women using the EWGSOP2 criteria [25]. Assuming an absolute precision of $5\%$ and a confidence interval (CI) of $95\%$, a minimum sample size of 68 participants would be necessary to carry out the present study.
## Statistical analysis
Data were entered into SPSS software (IBM®, Chicago, IL, USA), version 23.0. The significance level adopted for the analyses was 0.05. Prevalence was described using relative frequency (%). To compare the prevalence of sarcopenia between the different diagnostic instruments, McNemar’s test (probable and confirmed sarcopenia) and Cochran’s Q-test (severe sarcopenia) were used. To assess the level of agreement between the diagnostic tools for sarcopenia, Cohen’s Kappa test (probable and confirmed sarcopenia) and Fleiss’s Kappa test (severe sarcopenia) were used. To interpret the agreement analysis, the classification categories proposed by McHugh [2012] were considered [26]: 0 to 0.20 represents no agreement; 0.21 to 0.39 represents minimal agreement; 0.40 to 0.59 represents weak agreement; 0.60 to 0.79 represents moderate agreement; 0.80 to 0.90 represents strong agreement; and above 0.90 represents almost perfect agreement.
## Results
Of the 337 older women initially contacted, 33 were younger than 65 years old and 76 refused to participate. Of the older women who signed an informed consent form, 13 reported having thyroid deficiency, 8 presented decompensated lung disease, 2 had hearing impairment, 2 had visual impairment, 7 had orthopedic problems, 10 presented cognitive decline identified by the Mini-Mental State Examination, 7 were bedridden, 13 practiced physical activity on a regular basis, 2 had been recently hospitalized, and 3 had metal in their bodies. This left a total of 161 eligible participants.
The 161 participants were community-dwelling older women (age: 74.4 ± 7.3 years; body mass: 61.0 ± 10.9 kg; height: 1.5 ± 0.1 m; BMI: 27.1 ± 4.6 kg/m2). They used, on average, 3.4 (± 2.1) medications, and $21.7\%$ had a history of falls in the last 6 months.
Fig. 1EWGSOP2 algorithm for case-finding, making a diagnosis, and quantifying severity in practice, and prevalence rates. Note: HGS: Handgrip Strength; 5XSST: 5-times sit-to-stand test; ASM: Appendicular Skeletal Muscle Mass; GS: Gait Speed; SPPB: Short Physical Performance Battery; TUG: Timed Up and Go test
## Probable sarcopenia
The prevalence rates of probable sarcopenia were $12.8\%$ and $40.6\%$, assessed through HGS and 5XSST, respectively (Fig. 1). There was a statistically significant difference in the prevalence of probable sarcopenia between the diagnostic instruments (X2 = 23.56; $p \leq 0.01$). Cohen’s Kappa test showed a lack of agreement between these diagnostic instruments [$K = 0.06$; $$p \leq 0.34$$] (Fig. 2).
Fig. 2Prevalence of probable sarcopenia (%) and agreement between diagnostic instruments. * Statistically significant difference in the prevalence of probable sarcopenia in older women between the diagnostic instruments. Note: HGS: Handgrip Strength; 5XSST: 5-times sit-to-stand test. Agreement analysis: $K = 0$–0.20: no agreement; $K = 0.21$–0.39: minimal agreement; $K = 0.40$–0.59: weak agreement; $K = 0.60$–0.79: moderate agreement; $K = 0.80$–0.90: strong agreement; K > 0.90: almost perfect agreement
## Confirmed sarcopenia
The prevalence rates of confirmed sarcopenia were $11.5\%$ and $2.7\%$, using HGS + ASM and HGS + ASM/height2, respectively (Fig. 1). There was a statistically significant difference in the prevalence of confirmed sarcopenia between the diagnostic instruments (X2 = 11.08; $p \leq 0.01$). Cohen’s Kappa test showed minimal agreement between these diagnostic instruments [$K = 0.35$; $p \leq 0.01$] (Fig. 3).
The prevalence rates of confirmed sarcopenia were $27.5\%$ and $6.5\%$, using 5XSST + ASM and 5XSST + ASM/height2, respectively (Fig. 1). A statistically significant difference was also observed in the prevalence of confirmed sarcopenia between these diagnostic instruments (X2 = 27.03; $p \leq 0.01$). Cohen’s Kappa test showed minimal agreement between these diagnostic instruments [$K = 0.31$; $p \leq 0.01$] (Fig. 3).
Fig. 3Prevalence of confirmed sarcopenia (%) and agreement between diagnostic instruments. * Statistically significant difference in the prevalence of confirmed sarcopenia in older women between the diagnostic instruments. Note: HGS: Handgrip Strength; ASM: Appendicular Skeletal Muscle Mass; 5XSST: 5-times sit-to-stand test. Agreement analysis: $K = 0$–0.20: no agreement; $K = 0.21$–0.39: minimal agreement; $K = 0.40$–0.59: weak agreement; $K = 0.60$–0.79: moderate agreement; $K = 0.80$–0.90: strong agreement; K > 0.90: almost perfect agreement
## Severe sarcopenia
The prevalence rates of severe sarcopenia were $3.4\%$, $5.4\%$, and $1.4\%$, using HGS + ASM + GS, SPPB, or TUG, respectively (Fig. 1). Cochran’s Q-test showed a statistically significant difference in the prevalence of severe sarcopenia between the three diagnostic instruments (X2[2] = 7.71; $$p \leq 0.02$$). Pairwise comparisons showed that the prevalence of severe sarcopenia was significantly higher when using SPPB than with TUG ($$p \leq 0.02$$). Fleiss’s Kappa test showed weak agreement between the three diagnostic instruments [$K = 0.52$, $p \leq 0.01$] (Fig. 4A).
When using HGS + ASM/height2 + GS, SPPB, or TUG, the prevalence rates of severe sarcopenia were $0.7\%$, $2.0\%$, and $0.7\%$, respectively (Fig. 1). There was no statistically significant difference in the prevalence of severe sarcopenia between the three diagnostic instruments (X2[2] = 4.00; $$p \leq 0.135$$). Fleiss’s Kappa test showed moderate agreement between the three diagnostic instruments [$K = 0.60$, $p \leq 0.001$] (Fig. 4A).
The prevalence rates of severe sarcopenia were $13.0\%$, $23.9\%$, and $5.1\%$, using the 5XSST + ASM + GS, SPPB, or TUG, respectively (Fig. 1). Cochran’s Q-test showed a statistically significant difference in the prevalence of severe sarcopenia between the three diagnostic instruments (X2[2] = 39.31; $p \leq 0.01$). Pairwise comparisons showed that the prevalence of severe sarcopenia was significantly higher when using SPPB than with GS ($p \leq 0.01$) and TUG ($p \leq 0.01$). Fleiss’s Kappa test showed weak agreement between the three diagnostic instruments [$K = 0.48$, $p \leq 0.01$] (Fig. 4B).
When using 5XSST + ASM/height2 + GS, SPPB, or TUG, the prevalence rates of severe sarcopenia were $2.9\%$, $5.8\%$, and $0.7\%$, respectively (Fig. 1). There was a statistically significant difference in the prevalence of severe sarcopenia between the three diagnostic instruments (X2[2] = 10.57; $p \leq 0.01$). Pairwise comparisons showed that the prevalence of severe sarcopenia was significantly higher when using SPPB than with TUG ($$p \leq 0.004$$). Fleiss’s Kappa test showed weak agreement between the three diagnostic instruments [$K = 0.44$, $p \leq 0.01$] (Fig. 4B).
Fig. 4Prevalence of severe sarcopenia (%) and agreement between diagnostic instruments. * Statistically significant difference in the prevalence of severe sarcopenia in older women between the diagnostic instruments. Note: HGS: Handgrip Strength; ASM: Appendicular Skeletal Muscle Mass; GS: Gait Speed; SPPB: Short Physical Performance Battery; TUG: Timed Up and Go test; 5XSST: 5-times sit-to-stand test. Agreement analysis: $K = 0$–0.20: no agreement; $K = 0.21$–0.39: minimal agreement; $K = 0.40$–0.59: weak agreement; $K = 0.60$–0.79: moderate agreement; $K = 0.80$–0.90: strong agreement; K > 0.90: almost perfect agreement
## Discussion
This study showed differences in the sarcopenia prevalence and severity tested using instruments proposed by the EWGSOP2 (rates: 0.7–$40.6\%$). Moreover, the level of agreement between all the instruments evaluated by the Kappa test was, in general, minimal or weak.
Similar to the findings of the present study, recent previous evidence found differences in the prevalence of sarcopenia between several diagnostic criteria in older Asian adults, with rates ranging from 5.9 to $82.1\%$ [3]. According to the authors, the great variability in prevalence rates is related to the several cutoff points existing in different diagnostic criteria for the definition of adequate muscle mass, which may vary between geographic regions and, therefore, must be adapted to the ethnic group to which it is being applied [3].
In this study, the prevalence of probable sarcopenia assessed by 5XSST was higher than when assessed by HGS and there was no agreement between the two screening tests. A possible explanation for the difference in prevalence between these two instruments may be related to the specificity of the assessment. While the HGS assesses upper limb muscle strength, the 5XSST assesses muscle strength in the lower limbs [27]. Moreover, tests such as the 5XSST may represent general physical performance and not only muscle strength [28].
Unlike the assessment using the 5XSST, when using the manual dynamometer, aspects of physical performance such as balance, endurance, and mobility are neglected. Corroborating this argument, a previous study by Felicio et al. [ 2014] found a low correlation between HGS and lower limb muscle performance in community-dwelling older women [29]. However, while the study by Felicio et al. [ 2014] evaluated the lower limbs using specific isokinetic tests [29], this study used physical-functional performance tests to provide a global evaluation. Furthermore, similar to the present findings, a previous study found a higher prevalence of probable sarcopenia when assessed using 5XSST ($91.0\%$) compared with HGS ($29.0\%$), suggesting that the assessment of lower limb muscles may be more sensitive to detecting loss of muscle strength in older adults [30]. Thus, muscle assessment of the lower limbs seems to be more adequate for the screening of sarcopenia. It may be the case that changes in lower limb muscles appear in earlier stages of the disease. However, this cannot be inferred from the findings of this cross-sectional study, although it may be an interesting topic for further longitudinal research.
The present study found a high prevalence of probable sarcopenia using 5XSST ($40.6\%$). Other authors also found high prevalence rates in their sample when using this functional test. For example, de Souza et al. [ 2022] found a prevalence of probable sarcopenia of $64.1\%$ for older women from the city of Balneário Arroio do Silva in the state of Santa Catarina, Brazil [31]. Another study conducted by de Souza et al. [ 2022] observed a prevalence of probable sarcopenia of $42.0\%$ in older Brazilian women using data from a study with probabilistic sampling carried out in Florianópolis in the state of Santa Catarina, Brazil [32]. Swan, Warters and O’Sullivan [2022] observed that $26.1\%$ of participants aged 60 years and over from the English Longitudinal Study of Ageing (ELSA) met the criteria for probable sarcopenia based on poor performance in 5XSST [33]. In addition, when examined for socioeconomic status, these authors found that the prevalence of probable sarcopenia was over 2-times higher in the most disadvantaged socioeconomic status group compared with the least disadvantaged ($47.0\%$ vs. $20.6\%$, respectively) [33]. Thus, divergences in the prevalence of probable sarcopenia across studies may also be related to the socioeconomic conditions of the populations studied. Notably, previous research by our group identified cutoff points for sociodemographic and anthropometric variables in screening for probable and confirmed sarcopenia in community-dwelling older adults [34]. In addition, Kim and Won [2019] found a high prevalence of confirmed sarcopenia in older Korean women ($14.4\%$) when using 5XSST + ASM, which is in line with the present results ($27.5\%$) [35]. Recently, Sayer and Cruz-Jentoft [2022] pointed out that studies on this topic need to be encouraged in low and middle-income countries to address local needs as well as for developing a global perspective on sarcopenia [36].
However, some restrictions regarding the use of HGS and 5XSST need to be highlighted. For patients with upper extremity impairment and/or affected by rheumatoid arthritis, hand osteoarthritis, or carpal tunnel syndrome, HGS may not be an accurate reflection of muscle strength and may lead to underestimations. Similarly, the 5XSST also has a restricted capacity to assess a wide variation in ability, which is relevant in older adults, since some cannot complete the five attempts and are therefore not assigned a score (floor effect). The utility of this test is therefore limited in individuals suffering from moderate to severe mobility limitations [37]. Despite the limitations of using these instruments to screen for probable sarcopenia, evidence suggests that HGS is accurate in detecting sarcopenia in community-dwelling older women [38].
In the present study, a higher prevalence of confirmed sarcopenia was observed when using ASM than with ASM/height². Bijlsma et al. [ 2013] also found that ASM is better for predicting physical performance in older adults than ASM/height² [39]. The authors argue that, when adjusted for height, ASM can underestimate sarcopenia in obese individuals and overestimate sarcopenia in underweight older adults [39]. Since this index is positively correlated with BMI, individuals with a greater BMI due to a larger amount of fat are less likely to be classified as having sarcopenia [39]. Furthermore, when comparing ASM adjusted for weight, body mass index, and height, Kim, Jang and Lim [2016] and Figueiredo et al. [ 2014] found a lower prevalence of sarcopenia using ASM/height² [40, 41]. It should be mentioned that in the present study, $47.8\%$ of older women were classified as obese, which might partially explain the difference in the prevalence of sarcopenia between the two criteria. There is an ongoing debate about the best adjustment and whether the same method can be used for all populations [1, 40].
In this study, it was observed that the prevalence of severe sarcopenia detected by GS and TUG was $0.7\%$ and by SPPB was $2.0\%$, considering HGS + ASM/height², showing no significant difference and exhibiting a moderate agreement between the diagnostic instruments. This similar prevalence may have occurred because only one participant had severe sarcopenia detectable by GS and TUG, while three participants were identified using SPPB. In consonance with our results, Paula et al. [ 2016] found a moderate-to-high agreement when using GS and TUG in their sample of older Brazilian women [42]. According to these authors, small changes in physiological capacity can be noted in a similar way when using these two physical-functional performance tests.
In the current study, the lowest prevalence of severe sarcopenia using 5XSST + ASM/height2 was found when the TUG was used ($0.7\%$). This prevalence was much lower when compared with previous studies (2.5–$21.6\%$) [42–44]. A possible explanation for the divergence in the prevalence rates across the studies refers to the cutoff point used for TUG performance. In this study, we used a cutoff of ≥ 20 s as recommended by the EWGSOP in 2019 [1], which is relatively high when compared with the cutoffs used by Paula et al. [ 2016] (> 11.3 s), Sui et al. [ 2021] (> 9.3 s), and Alexandre et al. [ 2012] (> 12.47 s) [42–44].
Despite the relevance of the findings of the present study, they should be considered with caution due to certain limitations. Firstly, the sample was obtained by convenience. Secondly, only women were included in this study, which meant that sex-related differences could not be evaluated. It would therefore be interesting to expand the study to include older men. Thirdly, our sample was exclusively composed of older women residing in a municipality of Brazil’s southeast region, which prevents extrapolating the results to populations of places with different sociodemographic and environmental characteristics.
Recent evidence highlights the need for a globally accepted definition of sarcopenia, as well as the need for operational parameters to better diagnose the disease [36, 45]. In this sense, our results add to existing knowledge by revealing the necessity for additional studies aimed at comparing diagnostic instruments within other existing consensuses and verifying the agreement between the available methods. Although the best instrument or criterion for diagnosing the presence of probable, confirmed, and severe sarcopenia is not yet known, researchers and health professionals should be aware of the differences in these definitions and their prevalence rates when applying the different instruments in older populations.
## Conclusion
There were differences in the prevalence rates of sarcopenia and low agreement between the diagnostic instruments proposed by the EWGSOP2. The findings of this study suggest that these issues must be considered in the discussion on the concept and assessment of sarcopenia, which could ultimately help to better identify patients with this disease in different populations.
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|
---
title: 'Association between triglyceride glucose index and sleep disorders: results
from the NHANES 2005–2008'
authors:
- Heng Pei
- Shuyu Li
- Xin Su
- Yangyang Lu
- Zhijun Wang
- Shouling Wu
journal: BMC Psychiatry
year: 2023
pmcid: PMC10007799
doi: 10.1186/s12888-022-04434-9
license: CC BY 4.0
---
# Association between triglyceride glucose index and sleep disorders: results from the NHANES 2005–2008
## Abstract
### Background
To determine the association between sleep disorders and Triglyceride glucose index.
### Methods
A cross-sectional analysis of the 2005 to 2008 National Health and Nutrition Examination Survey (NHANES) was performed. The 2005 to 2008 NHANES national household survey for adults ≥ 20 years was examined for the sleep disorders. TyG index: ln [triglyceride (mg/ dL) × fasting blood glucose (mg/dL)/2].Multivariable logistic and linear regression models were used to explore the association between the TyG index and sleep disorders.
### Results
A total of 4,029 patients were included. Higher TyG index is significantly associated with elevated sleep disorders in U.S. adults. TyG was moderately correlated with HOMA-IR (Spearman $r = 0.51$). TyG was associated with higher odds of sleep disorders(adjusted OR [aOR],1.896; $95\%$ CI, 1.260 2.854), *Sleep apnea* (aOR, 1.559; $95\%$ CI, 0.660 3.683), Insomnia(aOR, 1.914;$95\%$ CI, 0.531 6.896), and Restless legs (aOR, 7.759; $95\%$ CI,1.446 41.634).
### Conclusions
In this study, our result shown that population with higher TyG index are significantly more likely to have sleep disorders in U.S. adults.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12888-022-04434-9.
## Introduction
Humans spend about one-third of their time sleeping, either to recover or to rest [1]. Sleep disorders are currently considered a public health disease by the Centers for Disease Control (CDC) [2].It cost approximately $3400 to $5200/person/year for health care [3]. Previous studies have shown that sleep disturbances are significantly associated with decreased quality of life and increased metabolic disease, arterial stiffness [4],cardiovascular disease, and mortality [5].
Several observational studies in large populations suggest that insulin resistance (IR) may be a major cause of sleep disturbance [6]. The triglyceride glucose index, which has been reported to be significantly associated with IR, is a simple and reliable surrogate indicator for IR [7–9].The association of Triglyceride glucose index (TyG) with cardiovascular outcomes and the onset of diabetes has been demonstrated [10–13].TyG may also be a risk factor for sleep disorders, and this study aimed to explore the relationship between TyG and sleep disorders. NHANES includes a nationally representative sample of U.S. adults. Therefore, to assess the association of TyG with sleep disorders, this study collected datasets from the National Health and Nutrition Examination Survey (NHANES).
## Study population
This is a cross-sectional study with data from the National Health and Nutrition Examination Survey (2005–2008). The survey component included diet, questionnaires, and physiological measurements, as well as laboratory tests supervised by trained medical staff. In addition, NHANES employs various modern equipment to make data collection more reliable and efficient. In addition, each participant receives compensation and medical outcome reporting, which increases participant compliance. A total sample size of 20,497 adults was assessed from 2005–2008, Fig. 1 shows the study design and inclusion criteria, and participants who were excluded due to missing information on either covariate(missing data on sleep disorders,TyG index,HOMA-IR,fall asleep time,age, gender, race, smoke, drink,BMI,MVPA, Hypertension,Diabetes, CVD and cancer). The participants' medication information in the past month based on these modules, RXDUSE (Taken prescription medicine, past month),RXDDRUG(Generic drug name).Find the drug code via the RXQ_DRUG module. Only publicly available data were used in the analysis, and the NHANES protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board approval. Fig. 1Flow chart of subject selection
## Data collection and definitions
TyG index was calculated as ln [fasting triglycerides (mg/dL) x fasting glucose (mg/dL)/2] [14]. HOMA-IR index was calculated as [fasting glucose (mmol/L) x fasting insulin (μU/mL)/22.5] [15]. Sleep disorders was self-reported by participants. For each adult, standard demographic data were collected. The sleep disorders module was queried for the NHANES question SLQ060, SLQ050: “Have you ever been told by a doctor or other health professional that you have a sleep disorder?”, “ Have you ever told a doctor or other health professional that you have trouble sleeping?”,with those responding “yes” subsequently considered to have a sleep disorder in further analysis and SLQ070:People who self-report having Sleep Apnea,Insomnia,Restless Legs or other sleep disorders. ,with those responding “yes” subsequently considered to have a sleep disorder in further analysis.
## Assessment of covariates
Age, gender, race (Mexican American, Other Hispanic, non-Hispanic white,non-Hispanic black and other race) were obtained by interviews and physical examinations. Existing smokers were defined as those who smoked 100 or more cigarettes and smoked at the time of the survey. Heavy alcohol consumption was defined as those who had consumed 12 or more glasses of alcohol/for life and who had consumed alcohol at the time of the survey. Leisure levels were calculated as the number of minutes per week during which participants reported participating in moderate to vigorous physical activity (MVPA).Participants with a BMI of 25 kg/m2 were considered overweight in accordance with the limit values. Pre-existing co-morbidities initially included a history of CVD, including coronary artery disease, angina pectoris, myocardial infarction and stroke (yes/no);diabetes (categorised as physician-diagnosed and undiagnosed diabetes); hypertension (categorised as diagnosed hypertension and no hypertension);cancer (categorised as diagnosed cancer and no cancer).
## Statistical analysis
Sample weights were used for analysis in order to account for complex survey design and non-response to NHANES.Continuous variables were summarized as mean $95\%$CI or median (interquartile range) depending on variable distribution, and categorical variables as count (proportion). TyG was compared with HOMA-IR by using Spearman correlation and with sleep disorders. Calculated the area under a receiver-operating characteristic curve generated without covariate adjustment. Multivariate logistic regression and linear analysis was then performed to assess the contribution of TyG index to sleep disorders,Model 1 was unadjusted. Model 2 was adjusted for age, gender, and race. Model 3 was adjusted for age,gender,race,BMI,smoke,drink,MVPA,Hypertension,Diabetes,CVD and cancer. The dose–response association was assessed on a continuous scale with restricted cubic spline curves. The subgroup variable stratified analysis was presented with a fully adjusted Model 3. The log-likelihood test was used to evaluate the interaction effects of the TyG index with subgroup variables. Sensitivity analysis was performed to evaluate which was not affected by taking hypotensive drugs, lipid-lowering drugs or hypoglycemic drugs. All statistical analyses were performed by using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 4.1.3 (R Core Team 2020) with a 2-sided $P \leq 0.05$ considered statistically significant.
## Baseline data for study participants
Figure 1 presented the study design, sampling and exclusion;and 16,468 participants were excluded due to missing information on either covariate(missing data on sleep disorders,TyG index,HOMA-IR,fall asleep time,age, gender, race, smoke, drink,BMI,MVPA, Hypertension,Diabetes, CVD and cancer).Our final sample included 4,029 NHANES participants, of which $48.61\%$ were female and $71.76\%$ were non-Hispanic whites (table.1). Population-weighted mean age was 46.9 years. Only $25.48\%$ of people do physical activity every week$.81.18\%$ consumped at least 12 alcohol drinks/lifetime and drunk at the time of survey. About $50\%$ who smoked at least 100 cigarettes and smoked at the time of survey. One-thirdof the population was hypertensive and obesity. Table 1Baseline and Study Measurements ($$n = 4$$,029)VariablevalueMale%1988(48.61)Race%Mexican American744(8.05)Other Hispanic315(4.12)Non-Hispanic White2018(71.76)Non-Hispanic Black798(10.70)Other Race154(5.38)Smoke%1923(48.54)Drink%3090(81.18)MVPA%882(25.48)BMI ≥ $25\%$1535(33.42)Hypertension%1434(31.57)Diabetes%552(9.70)CVD%342(6.45)Cancer%367(8.32)Sleep Disorders%959(24.98)Sleep Apnea%176(4.43)Insomnia%69(1.42)Restless Legs%14(0.28)Age(year) mean $95\%$CI46.69(45.61 47.77)TyG index mean $95\%$CI3.77(3.76 3.78)logHOMA-IR mean $95\%$CI0.78(0.73 0.83)fall asleep time(minutes) mean $95\%$CI21.24(20.28 22.19) There were 959 ($24.98\%$) persons with self-reported sleep disorders,$4.43\%$ of participants with sleep apnea,$1.42\%$ persons suffered from Insomnia. The prevalence of self-reported Restless Legs was 14($0.28\%$). Average time(mean $95\%$CI) tofall asleep costed was 21.24(20.28–22.19)minutes.
## Comparison of TyG to HOMA-IR and sleep disorders
In the analyzed samples, TyG is Moderately correlated with HOMA-IR, corresponding to The population-weighted Spearman r was 0.51. Figure 2 Display participant-level TyG measurements HOMA-IR for log transformation. Fig. 2Scatterplot of triglyceride-glucose index against log-transformed homeostatic model assessment of insulin resistance (HOMA-IR). The population-weighted Spearman’s rho was 0.51 Figure 3 exhibited the population-weighted receiver-operating characteristic curve (ROC) of sleep disorders,sleep apnea,Insomnia and Restless Legs. The AUC for TyG to sleep disorders is 0.56, TyG was doing well Differentiation of Restless Legs(AUC = 0.79).Fig. 3a ROC for TyG to Sleep disorders(AUC = 0.56). b ROC for TyG to Sleep apnea(AUC = 0.61). c ROC for TyG to Insomnia(AUC = 0.53). d ROC for TyG to Restless Legs(AUC = 0.79)
## Associations of TyG, HOMA-IR with study outcomes
In Table 2, after adjusting for covariates, higher TyG index was associated with higher relative risks of sleep disorders(OR = 1.87; $95\%$ confidence interval (CI) 1.26, 2.85) and Restless Legs(OR = 7.76; $95\%$ confidence interval (CI) 1.45, 41.63). After adjusting for the same covariates, the HOMA-IR index had an increased risk of sleep disorders (OR = 1.21; $95\%$ confidence interval (CI) 1.05, 1.39) and Sleep Apnea(OR = 2.01; $95\%$ confidence interval (CI) 1.56, 2.59). Higher TyG means longer time to fall asleep(P value for linear trend, < 0.01)(Fig. 4).Table 2Association of triglyceride-glucose index (TyG) with study outcomes($$n = 4$$,029)VariableTyG indexlogHOMA-IRSleep disorders1.896(1.260 2.854)1.210(1.050 1.394)Sleep apnea1.559(0.660 3.683)2.006(1.557 2.585)Insomnia1.914(0.531 6.896)1.055(0.676 1.649)Restless legs7.759(1.446 41.634)1.059(0.435 2.582)adjusted for age,gender,race,BMI,smoke,drink,MVPA,Hypertension,Diabetes,CVD and cancerFig. 4Association of triglyceride-glucose index (TyG) with time of fall asleep (minutes). The p-value indicates a test for linear trend with increasing number of symptoms derived by treating symptom count as a continuous variable As shown in Fig. 5, Restricted cubic spline curves suggested that the relationship between TyG index and sleep disorders,sleep apnea,insomnia and Restless Legs was linear. We analyzed the association of TyG with sleep disorders stratified by age(e_table.1), sex(e_table.2), and race(e_table.3), and we found no interaction effects, the confidence interval was too wide, which precluded meaningful inference. Fig. 5a Restricted cubic spline fitting for the association between TyG index levels with sleep disorders. b Restricted cubic spline fitting for the association between TyG index levels with sleep apnea. c Restricted cubic spline fitting for the association between TyG index levels with Insomnia. d Restricted cubic spline fitting for the association between TyG index levels with Restless Legs *Sensitivity analysis* (e_table.4) was performed to evaluate which was not affected by taking lipid-lowering drugs,hypotensive drugs or hypoglycemic drugs. Since TyG appeared to be more strongly correlated with findings than HOMA-IR, we next tested whether the correlation of TyG with findings was independent of HOMA-IR. With additional adjustment for HOMA-IR,TyG continued to be associated with sleep disorders and Restless Legs.
## Discussion
In this nationally representative study, the relationship between TyG and sleep disorders was evaluated. The main finding of this paper is that TyG was associated with sleep disorders in the American adult population. This finding was also validated in adjusting the influence of HOMA-IR and was not affected by the use of lipid-lowering, hypotensive or hypoglycemic medications. We also found that increased exposure to TyG could increase the risk for Restless Legs, and the effect of HOMA-IR on Restless Legs was not significant,associations with Restless Legs seemed largely independent of IR (insulin resistance).
A previous study showed that participants with a higher TyG index had a higher risk of obstructive sleep apnea than the low-level group in Korean adult [16]. It may be related to differences in race or adjusted covariates,we found no positive effect of TyG index on sleep apnea in our study. However,we found the association between HOMA-IR and sleep apnea,similar to those previously published [17].After adjustment for HOMA-IR, changes in sleep disturbances suggesting insulin resistance did not fully explain the potential impact of TyG on the findings. Compared with the HOMA-IR metric, TyG is a lower-cost measure for IR [18, 19].*In a* large population. TyG index can serve as a practical alternative of IR measurement. In addition, the correlation between IR and sleep disturbances has already been documented [20].
Another significant finding was drawn from our linear trend analysis. This study shown that higher TyG means longer time to fall asleep,Edward etl found that longer periods to fall asleep (> 30 min) were associated with higher fasting insulin only for women [21], it confirmed our results. At the Circadian clocks level,several researchers have explored the interaction of gene behavior and have shown that interactions between diet and clock gene mutations affect fasting blood glucose [22], insulin resistance [23, 24], and T2DM [25].A study shows a correlation between improvements Insulin sensitivity and indicators of increased sleep duration After 40 days of sleep (approximately 45 min extra per night) [26],which partly explained our findings. Bosco D et al. found that IGT (prediabetes) is frequently associated with idiopathic RLS(Restless Legs Syndrome) [27].We also found that association between TyG index and Restless Legs with adjusted HOMA-IR.It suggests that prediction of TyG to sleep disorders is not only related to metabolism, but also genetics. In patients with dysglycemia Metabolic RLS may be due to simultaneous reduction in Inhibitory dopaminergic control of the dorsal horn of the spinal cord Excitatory nociceptive input due to peripheral Neuropathy [28].Basic research is still needed to complete the mechanisms of genetics.
Results from our study shown that a linear dose–response relationship between TyG and various sleep disorders, suggesting that regardless of their causal relationship, the earlier interventions on these two modifiable indicators, the greater the benefit. The American Heart Association proposed to take sleep health as one of Life’s Essential 8 [29], blood lipids and blood glucose are also included. Therefore, the combined improvement of sleep quality and TyG may improve cardiovascular health more than a single improvement,evidence from cohort studies is required. There were numerous cross-sectional and prospective epidemiological studies have shown that insufficient and perhaps excessive sleep time predisposes to systemic and central obesity, the metabolic syndrome, cardiovascular disease and all-cause mortality [5, 30].However,a recent prospective epidemiologic study reported for the first time that fasting hyperinsulinaemia and insulin resistance (as assessed by the HOMA-IR index) preceded incident ‘observed apnoeas’ over a 6-year follow-up period [31].Importantly, this link is bidirectional: on the one hand, the circadian clock regulates energy intake and metabolic pathways throughout the organism, while on the other hand feeding behavior and the nutrient composition of the diet influence the circadian clock itself, especially peripheral metabolic organs and their outputs [4].Our study shows a positive relationship between TyG index and sleep disturbance, but the mechanism of the interaction between metabolism and sleep disturbance remains to be investigated.
Our study was the first to investigate the relationship between TyG and sleep disorders, and the positive relationship has been observed. In this study, the sleep disorders includes several kinds of sleep problems, which could largely reflect people's state of sleep. the findings would serve as a reminder to the public to pay more attention to sleep health. This study provides a foundation for future multi-centre cohort studies on sleep disorders and TyG index. Previous studies showed insulin resistance were associated with sleep disorders [6], which may partly explain the significant relationship of TyG index with sleep disorders. However, our investigation has limitation. it was a study by observation, and causality cannot be demonstrated.
## Conclusions
After adjusting for case complexity, a high TyG index was associated with higher odds of individuals with sleep disorders in the general population. Insulin resistance did not fully explain the findings. Our finding suggests that the TyG index may be an independent predictor of the development of sleep disorders.
## Supplementary Information
Additional file 1: e_table.1. Association of triglyceride-glucose index (TyG) with study outcomes, stratified by age. Additional file 2: e_table.2. Association of triglyceride-glucose index (TyG) with study outcomes, stratified by gender. Additional file 3: e_table.3. Association of triglyceride-glucose index (TyG) with study outcomes, stratified by raceAdditional file 4: e_table.4. Association of triglyceride-glucose index (TyG) with study outcomes
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|
---
title: The oncogenic circular RNA circ_63706 is a potential therapeutic target in
sonic hedgehog-subtype childhood medulloblastomas
authors:
- Keisuke Katsushima
- Rudramani Pokhrel
- Iqbal Mahmud
- Menglang Yuan
- Rabi Murad
- Prabin Baral
- Rui Zhou
- Prem Chapagain
- Timothy Garrett
- Stacie Stapleton
- George Jallo
- Chetan Bettegowda
- Eric Raabe
- Robert J. Wechsler-Reya
- Charles G. Eberhart
- Ranjan J. Perera
journal: Acta Neuropathologica Communications
year: 2023
pmcid: PMC10007801
doi: 10.1186/s40478-023-01521-0
license: CC BY 4.0
---
# The oncogenic circular RNA circ_63706 is a potential therapeutic target in sonic hedgehog-subtype childhood medulloblastomas
## Abstract
Medulloblastoma (MB) develops through various genetic, epigenetic, and non-coding (nc) RNA-related mechanisms, but the roles played by ncRNAs, particularly circular RNAs (circRNAs), remain poorly defined. CircRNAs are increasingly recognized as stable non-coding RNA therapeutic targets in many cancers, but little is known about their function in MBs. To determine medulloblastoma subgroup-specific circRNAs, publicly available RNA sequencing (RNA-seq) data from 175 MB patients were interrogated to identify circRNAs that differentiate between MB subgroups. circ_63706 was identified as sonic hedgehog (SHH) group-specific, with its expression confirmed by RNA-FISH analysis in clinical tissue samples. The oncogenic function of circ_63706 was characterized in vitro and in vivo. Further, circ_63706-depleted cells were subjected to RNA-seq and lipid profiling to identify its molecular function. Finally, we mapped the circ_63706 secondary structure using an advanced random forest classification model and modeled a 3D structure to identify its interacting miRNA partner molecules. Circ_63706 regulates independently of the host coding gene pericentrin (PCNT), and its expression is specific to the SHH subgroup. circ_63706-deleted cells implanted into mice produced smaller tumors, and mice lived longer than parental cell implants. At the molecular level, circ_63706-deleted cells elevated total ceramide and oxidized lipids and reduced total triglyceride. Our study implicates a novel oncogenic circular RNA in the SHH medulloblastoma subgroup and establishes its molecular function and potential as a future therapeutic target.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40478-023-01521-0.
## Introduction
Medulloblastoma (MB) is a highly malignant childhood brain tumor accounting for ~ $20\%$ of all pediatric brain tumors and $63\%$ of intracranial embryonic tumors [8]. Approximately 500 patients are diagnosed with medulloblastoma in the United States each year, of whom $60\%$ are children under fifteen [8]. Advances in next‐generation sequencing and genome‐wide association analyses have unraveled significant heterogeneity in medulloblastoma [12], such that the World Health Organization Classification of Tumors of the Nervous System has for some time classified MBs into molecular subgroups: wingless (WNT)-activated, sonic hedgehog (SHH)-activated and TP53 wildtype, SHH-activated and TP53 mutant, and non-WNT/non-SHH [24]. Many studies have discovered reliable molecular markers for these subgroups. However, their degree of overlap, underlying genetics and biology, and intrinsic diversity have yet to be fully identified [29, 45], despite a need to define individual tumors for targeted therapy. There is a compelling clinical need for novel molecular markers and therapeutic targets for specific molecular subgroups to improve outcomes.
Recent studies have identified several medulloblastoma subgroup-specific biomarkers and molecular targets including oncogenes and tumor suppressor genes such as MYCN, MYC, TP53, CDK6, ALK, GLI1, SNCAIP, OTX2, and SNCA [19]. Fully defining medulloblastoma heterogeneity requires an approach that goes beyond characterizing individual genes, since cancer development represents the product of complex interactions in and between signaling networks and their regulation. Noncoding (nc) RNAs—which represent most of the transcribed genome—may be useful for sub-stratifying MBs. We recently identified significant heterogeneity in long non-coding RNAs (lncRNAs) in MBs by molecular subgroup [11], with lnc-HLX-2-7 oncogenic in Group 3 (G3) MBs [13] and Sprightly in Group 4 (G4) MBs [16].
Circular RNAs (circRNAs) have recently emerged as a class of endogenous tissue- and developmental stage-specific ncRNAs [15]. CircRNAs are exceptionally stable and generally cytoplasmic [15, 33]. CircRNAs are now established as pathogenic in various cancers and have potential as diagnostic or therapeutic targets. In addition, circRNAs are abundant in the mammalian brain [33], so they may be perfect candidates for biomarkers in medulloblastoma. CircRNAs are generated from pre-messenger RNA via back-splicing, where the 3′ and 5′ ends are connected via a covalent bond to form a loop [7] structure devoid of a 5′ cap and 3′ poly(A) tail. Therefore, circRNAs are resistant to degradation by RNases and are abundant in mammalian cells and body fluids [28], providing opportunities for non-invasive sampling for biomarker analysis. CircRNAs can regulate gene expression and translation by sponging RNA-binding proteins and microRNAs (miRNA, miR) [31, 42] and, in some cases, generating a protein through translation [36]. CircRNAs are dysregulated in various cancers, where they mediate cellular proliferation, migration, and invasion. CircRNAs have also been documented in medulloblastoma: two circRNAs (circ‐SKA3 and circ‐DTL) promoted the proliferation, migration, and invasion of medulloblastoma cells in vitro by regulating gene expression [25]. A recent study investigated the oncogenic characteristics of circ-SKA3, which increased ID3 expression by decoying miR-326 to promote medulloblastomagenesis [43]. One computational analysis proposed medulloblastoma subgroup-specific circRNAs, but these have yet to be validated experimentally [32]. Further detailed analysis of the circRNA content in medulloblastoma and their subgroup-specific distribution is urgently required to pave the way for new clinical diagnostics and therapeutics. In addition to regulating cancer hallmarks such as proliferation and invasion, several circRNAs have also been shown to modulate lipid synthesis and other metabolism pathways in cancer by altering various miRNAs [41].
Elevated lipid synthesis is a cancer hallmark, since cancer cells require fatty acids, glycerolipids, glycerophospholipids, and cholesterol esters for cellular membrane maintenance and cellular proliferation. For instance, circ_0057558 expression has been shown to be positively associated with triglyceride (TG) levels in prostate cancer [38] and, using bioinformatics approaches, three circRNA interaction axes were predicted in prostate cancer with unclear roles in cancer metabolism [37]. CircRNAs have also been shown to interact with c-myc [40] and HIF1-α [21] in several cancer models, suggesting a potential role for circRNAs in cancer metabolism. However, the full and probably varied roles of circRNAs in medulloblastoma and metabolism have yet to be characterized.
Here we identified medulloblastoma subgroup-specific circRNAs in 126 MBs through RNA-seq data analysis using the CIRI2 [6] detection pipeline. We combined a biostatistical approach and random forest classification to identify subgroup-specific circRNAs with diagnostic potential. Candidate circRNAs were validated by RT-PCR in cell lines and patient samples. We further tested the SHH subgroup-specificity of one circRNA (circ_63706; hsa-PCNT_0003, CircAtlas 2.0) using RNA fluorescence in situ hybridization (FISH) in clinical samples, paving the way for using circ_63706 as an SHH-specific biomarker. Detailed molecular analysis suggested that circ_63706 may reprogram global lipid metabolism in MB cells to enhance tumorigenesis. Based on our results, we postulate that oncogenic circRNA circ_63706 is an important therapeutic target and biomarker for SHH MBs.
## RNA sequencing datasets
FASTQ files for RNA-seq data were collected from the European Genome-Phenome Archive (http://www.ebi.ac.uk/ega/, accession number: EGAD00001003279) after obtaining permission from the ICGC Data Access Compliance Office. The data represented 175 medulloblastoma samples [$$n = 18$$ WNT, $$n = 46$$ SHH, $$n = 45$$ Group 3 (G3), and $$n = 66$$ Group 4 (G4)].
Later, we used to confirm our initial analysis in a separate dataset ($$n = 22$$ WNT, $$n = 43$$ SHH, $$n = 9$$ G3 and $$n = 23$$ G4) obtained from the St. Jude Hospital. We also isolated RNA samples from patient-derived xenografts (PDXs). The Wechsler-Reya lab established the DMB006, DMB012, RCMB28, RCMB32, RCMB38, RCMB40, RCMB45, and RCMB51 PDXs. The Olson lab at the Fred Hutchinson Cancer Research Center established MED211FH, MED511FH, and MED1712FH PDXs. The Milde lab at the German Cancer Research Center (DKFZ) established the BT-084 PDX, and the Cho lab at Oregon Health and Sciences University established the MB002 PDX. The Wechsler-Reya lab maintained all PDXs.
## RNA fluorescence in situ hybridization (RNA-FISH)
RNA was visualized in formalin-fixed, paraffin-embedded tissue (FFPE) sections using the QuantiGene ViewRNA ISH Tissue Assay Kit (Thermo Fisher Scientific, Waltham, MA). Tissue sections were rehydrated and incubated with proteinase K. Subsequently, we incubated the sections with ViewRNA probesets designed to target human circ_63706 (Thermo Fisher Scientific). Hybridization was performed according to the manufacturer’s instructions.
## siRNA-mediated knockdown
siRNAs targeting circ_63706 were purchased from Integrated DNA Technologies (Coralville, IA). Cells were transfected with 20 nM siRNA targeting each gene or control non-targeting siRNA (negative control siRNA) (AM4611, Applied Biosystems, Foster City, CA) for 48 h using Lipofectamine RNAiMAX (Thermo Fisher Scientific). Knockdown efficiency was assessed using qRT-PCR. The following siRNAs sequences targeted circ_63706 (#1): CAGCTGGAGACCCTGAAGGAA and (#2): ACAGCTGGAGACCCTGAAGGA.
## Medulloblastoma xenografts
All mouse studies were performed following the policies and regulations of the Animal Care and Use Committee of Johns Hopkins University, which approved the studies. We established intracranial medulloblastoma xenografts by injecting DAOY, ONS76, and DAOY cells with circ_63706 knockdown into the cerebellums of NOD-SCID mice (Jackson Laboratory, Bar Harbor, ME). Cerebellar coordinates were − 2 mm from lambda, + 1 mm laterally, and 1.5 mm deep. We evaluated tumor growth with weekly bioluminescence imaging using an in vivo spectral imaging system (IVIS Lumina II, Xenogen, Alameda, CA).
## Differential expression of circRNAs in medulloblastoma subgroups
The circRNA detection pipeline is depicted in Fig. 1A. The pipeline detected 79,099 circRNAs in 175 medulloblastoma samples. After filtering out low count samples, the count matrix contained 8925 circRNAs across 126 samples ($$n = 14$$ WNT, $$n = 23$$ SHH, $$n = 37$$ G3, and $$n = 52$$ G4) (Fig. 1B). Two-dimensional principal component analysis (PCA; Fig. 1C) showed group-specific sample clustering, with all sample groups tending to overlap ($95\%$ CIs, marked by shaded areas). Clustering of differentially expressed circRNAs (Additional file 3: Figs. S1–S8) mirrored the PCA findings. Fig. 1Differential expression of circRNAs in medulloblastoma subgroups. A Circular RNA identification pipeline for the 126 medulloblastoma patients in four subgroups. B Raw total circular RNA counts across all 126 medulloblastoma patient samples. C Principal component clustering of 8925 highly expressed circRNAs. D–G The red and blue points and shades show significant circular RNAs for a group versus others with an adjusted p value < 0.05 and |log2 fold change|> 2. The box plot shows normalized expression for two significant upregulated circRNAs in WNT (D), SHH (E), G3 (F), and G4 (G) across all 126 medulloblastoma patients Nevertheless, several circRNAs were differentially expressed between subgroups (|log2-fold change|> 2 and FDR < 0.05; Fig. 1D–G). Since we sought to identify highly statistically significant group-specific circRNAs, we focused on differentially expressed (upregulated) circRNAs in a given group vs. the other three groups. 114 circRNAs in the WNT subgroup, 48 in the SHH group, 13 in G3, and 21 in G4 MBs were upregulated (Additional file 3: Fig. S2B). Figure 1D–G illustrate the top two differentially expressed circRNAs in each subgroup identified by the limma-voom method (Additional file 1).
Since only 13 circRNAs were significantly upregulated in G3 MBs (log2-fold change > 2 and FDR < 0.05), we took a similar number ($$n = 15$$) of upregulated circRNAs from other groups for experimental validation and functional studies (i.e., 58 significantly upregulated circRNAs, Additional file 4: Table S2). Data were ordered according to decreasing log2-fold change values to select the top 15 circRNAs. These 58 circRNAs separated the 126 medulloblastoma samples according to medulloblastoma subgroup, especially SHH and WNT from G3 and G4 tumors (Additional file 3: Fig. S3; expression in Additional file 3: Figs. S4–S7).
## Subgroup-specific marker genes from random forest (RF) classification
The random forest (RF) machine learning algorithm provides efficient and high predictive accuracy for many data types, including clinical and molecular data. RF is particularly useful for genomic data analysis, which is typically of small sample size but high feature dimension. Differential expression (DE) analysis packages are not optimized for circular RNA analysis due to inherent complications with normalization. Therefore, to validate the top 58 circRNAs obtained from DE analysis, we built and applied an RF model to identify subgroup-specific circRNAs. The model used 460 circRNAs across 126 samples, selected using recursive feature elimination (RFE) with RF. The heatmap of loading coefficient of the top 28 circRNAs contributing to the classification model appears in Additional file 3: Fig. S8A. By evaluating the contrast of loading coefficients and expression in normalized data (Additional file 3: Fig. S9), 16 subgroup-specific circRNAs were finally identified ($$n = 5$$ in WNT, $$n = 5$$ in SHH, $$n = 3$$ in G3, and $$n = 3$$ in G4; Additional file 3: Fig. S8B), nine of which (in bold and italicized letter) were also present in the top 58 differentially expressed genes. The area under the receiver operating characteristics (AUC-ROC) curve was > $95\%$, suggesting a high predictive accuracy for the RF classification model (Additional file 3: Fig. S8C).
## Validation of circRNAs by quantitative RT-PCR
The two combined analytic methods identified 65 subgroup-specific circRNAs (Additional file 4: Table S2), which were subsequently filtered to a final 12 circRNAs with higher abundance in raw count data ($$n = 4$$ SHH, $$n = 5$$ G3, and $$n = 3$$ G4; Additional file 4: Table S3). We designed primer pairs to cover the circRNA junction sequence using NCBI Primer-BLAST and Primer3Plus tools (Additional file 4: Table S1), and ACTB was used as a control.
The Ct values for the expression of all circRNAs across all cell lines and PDX samples are provided in Additional file 4: Table S4. Of the 12 circRNAs, only three had subgroup-specific overexpression when validated in cell lines (Fig. 2A); two in G3 (circ_40859, circ_43076) and one (circ_21305) in G4. Six circRNAs were validated in PDX samples, including all four SHH-specific circRNAs (circ_30598, circ_63706, circ_64014, and circ_66962) and two G4-specific circRNAs (circ_21305 and circ_33068) (Fig. 2B).Fig. 2CircRNAs validated by qRT-PCR and RNA-FISH in cell lines, PDXs, and clinical MB patient samples. A and B Fold change for normal cerebellum (CB) in medulloblastoma cell line samples (A) and PDX samples (B). Values indicate fold change relative to cerebellum. * $p \leq 0.05$, **$p \leq 0.01$, Kruskal–Wallis analysis. C and D RNA-FISH confirms that circ_63706 expression is specific to SHH medulloblastoma patients. C Representative RNA-FISH analysis of circ_63706 in medulloblastoma tissues. RNA-FISH analysis of circ_63706 in SHH medulloblastoma patients (top panels) and G3 and G4 medulloblastoma patients (lower panels). D Spot numbers relate to circ_63706 per cell in SHH, G3, and G4 medulloblastoma patients. $$n = 20$$, *$p \leq 0.01$, Student’s t-test. E Kaplan–Meier survival curves of SHH medulloblastoma patients according to circ_63706 expression. SHH medulloblastoma patient samples were divided into circ_63706 high (average spot number per cell > 1.0) and circ_63706 low (average spot number per cell < 1.0) We predicted the protein-coding potential of these six circRNAs using the RNAsamba tool [3], which uses a neural network classification model. The output of this model is summarized in Additional file 4: Table S5. Out of all six circRNAs, five had protein coding potential (circRNAs circ_33068 and circ_63706 had > $90\%$ coding potential) and circ_30598 had no coding potential.
## Circ_63706 expression is specific to SHH MBs
Of the six SHH subgroup-specific circRNAs (circ_30598, circ_63706, circ_64014, circ_66962, circ_21305, and circ_33068), only circ_63706 showed statistically significantly higher expression in the SHH subgroup by qRT-PCR in PDX samples compared with the other three groups. The cell line results are shown in Additional file 3: Fig. S10. Therefore, we decided to focus on circ_63706 and further confirmed its expression by RNA-FISH in formalin-fixed paraffin-embedded tissue samples from patients with MBs. Out of 20 medulloblastoma samples, circ_63706 was highly expressed in six SHH samples but not in any G3 or G4 MBs (Fig. 2C). Quantitative analysis of the tissues further confirmed significantly higher circ_63706 expression in SHH MBs than in G3 and G4 MBs, with high specificity ($100\%$; $p \leq 0.0023$; Fig. 2D). Importantly, the significantly higher expression level of circ_63706 in SHH MBs was further confirmed in an independent sample set using the St. Jude Cloud [27] (Additional file 3: Fig. S11). Survival analysis using clinical data reported in our previous study showed that circ_63706 overexpression was associated with poor patient outcomes in SHH MB (Fig. 2E). Collectively, our analyses suggest that circ_63706 expression is specific to SHH MBs and can be detected using an assay readily applicable to the clinical setting (FISH).
## Functional characterization of circ_63706 in SHH cell lines and PDXs
To investigate the function of circ_63706, we used two individual siRNAs to inhibit circ_63706 expression in DMB012, icb1712, and RCMB32 SHH MB PDXs and DAOY, ONS76, and UW228 SHH MB cell lines. Transfection with siRNAs targeting circ_63706 significantly and almost completely abolished circ_63706 expression compared with controls (si-NC) in these SHH MB cell lines and PDXs ($p \leq 0.01$, Fig. 3A) without affecting host gene expression (PCNT), (Additional file 3: Fig. S12). circ_63706 knockdown significantly inhibited cell proliferation in all SHH MB cell lines and PDXs ($p \leq 0.01$, Fig. 3B). Furthermore, circ_63706 knockdown significantly inhibited SHH cell migration and invasion ($p \leq 0.01$, Fig. 3C, D). Conversely, restoring circRNA levels rescued the knockdown phenotype (Additional file 3: Fig. S13).Fig. 3Inhibition of circ_63706 suppresses SHH MB cell proliferation, migration, and invasion in vitro. A circ_63706 expression was detected in DAOY, ONS76, UW228, DMB012, icb1712, and RCMB32 cells with si-circ_63706 or NC. B MTS assays were performed to assess proliferation in DAOY, ONS76, UW228, DMB012, icb1712, and RCMB32 cells with si-circ_63706 or NC. C Cell migration was assessed by a wound healing assay in DAOY, ONS76, and UW228 cells with si-circ_63706 or NC. D Cell invasion was assessed by transwell assays and DAOY, ONS76, and UW228 cells with si-circ_63706 or NC. Scale bar, 200 μm. Data shown are mean ± SD. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ To gain further insights into the functional significance of circ_63706, gene expression was measured by RNA-seq in DAOY cells treated with either si-NC or si-circ-63706. Among 735 genes with a significant change in expression (FDR < 0.05), 340 genes were upregulated and 395 genes were downregulated in cultured DAOY cells treated with si-circ-63706 (Additional file 3: Fig. S14A). Ingenuity Pathway Analysis (IPA) revealed that circ_63706 knockdown preferentially affected genes associated with cell proliferation and apoptosis (Additional file 3: Fig. S14B). Of note, circ_63706 knockdown downregulated genes contributing to important cancer pathways including RBBP4, TGFA, E2F1, and HRAS (Additional file 3: Figs. S14 and 15).
## Circ_63706 regulates tumor formation in mouse intracranial xenografts
To evaluate the effect of circ_63706 on tumor growth in vivo, we established intracranial MB xenografts in NOD-SCID mice. We knocked down circ_63706 in DAOY and ONS76 SHH cells using a lentivirus with a luciferase reporter (Fig. 4A, B). Weekly evaluation of tumor growth by bioluminescence imaging revealed significantly smaller tumors in mice transplanted with circ_63706-knockdown DAOY and ONS76 cells than in mice transplanted with control cells ($$n = 5$$, $p \leq 0.05$, Fig. 4C, D).Fig. 4Circ_63706 promotes tumorigenesis and growth of SHH MB cell in vivo. A Expression of circ_63706 in DAOY and ONS76 control (sh-NC) and DAOY and ONS76 with circ_63706-knockdown (sh-circ_63706 #1 and #2) cells. Relative expression to sh-NC is indicated on the y-axis. B Cell viability assays performed with DAOY and ONS76 control (sh-NC) and DAOY and ONS76 with circ_63706-knockdown (sh-circ_63706 #1 and #2) cells. Points represent the mean ± SD of three biological replicates. C DAOY and ONS76 control (sh-NC) and DAOY and ONS76 with circ_63706-knockdown (sh-circ_63706 #1 and #2) cells expressing luciferase were implanted into the cerebellums of NOD-SCID mice, and tumor formation was assessed by bioluminescence imaging. Changes in bioluminescent signal were examined weekly after tumor implantation. D Quantification of total photon counts from mice implanted with DAOY and ONS76 control (sh-NC) and DAOY and ONS76 with circ_63706-knockdown (sh-circ_63706 #1 and #2) cells. $$n = 5$.$ E Ki67 staining of xenograft tumor sections. Nuclei are stained with DAPI. Scale bars, 50 μm. Quantification of Ki67-positive cells are shown in (F). * $p \leq 0.05$, Student’s t-test. G Overall survival was determined by Kaplan–Meier analysis, and the log-rank test was applied to assess the differences between groups. * $p \leq 0.05$, Mantel–Cox log-rank test On day 28, Ki67 immunofluorescence analysis in tissue sections of excised tumors showed reduced cell proliferation in circ_63706-knockdown ONS76 tumors ($p \leq 0.01$, Fig. 4E, F). Kaplan–Meier plots demonstrated that the group transplanted with circ_63706-knockdown cells had significantly prolonged survival compared with control (Fig. 4G). Together, these results demonstrate that circ_63706 regulates tumor growth in vivo and may function as an oncogene.
## Circ_63706 depletion enhances lipid oxidation and bioactivity and reduces total triglycerides
Lipid metabolism is a key factor in tumor cell proliferation and growth. As circ_63706 promoted tumor growth in vitro and in vivo, we further explored the underlying molecular basis in circ_63706-knockdown cells using an untargeted lipidomics approach. Interestingly, circ_63706 knockdown globally enhanced lipid oxidation and downregulated total triglycerides (TGs) (Fig. 5). Lipid oxidation induces toxicity in cancer cells and ultimately leads to cell death. Using ultra-high-pressure liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS)-based global lipidomics, we found that total oxidized lipid ($$n = 284$$) was significantly higher in circ_63706-knockdown cells compared with controls (Fig. 5A), suggesting that circ_63706 may suppress fatty acid oxidation in medulloblastoma. We also identified the top 50 upregulated oxidized lipids and found that both glycerophospholipids (PC and PE) and glycerolipids (DG and TG) were mainly oxidized (Fig. 5B). Lipid oxidation in circ_63706-knockdown cells may impact other key lipid molecules critical for cancer cell proliferation and growth, such as TGs. Importantly, DGs ($$n = 58$$), the breakdown product of TGs, were slightly elevated, whereas total TGs ($$n = 258$$) were significantly reduced in circ_63706 KO cells, suggesting impaired glycerolipid metabolism that may interfere with cellular proliferation (Fig. 5C, D). Furthermore, both saturated and unsaturated fatty acids containing TGs were highly affected by circ_63706 knockdown (Fig. 5E). Since circ_63706 appeared to promote a lipid landscape benefitting tumor cells, we further explored the impact of circ_63706 on bioactive lipids, which are usually toxic to tumor cells. Sphingolipids, including different ceramides, are known to regulate cancer cell signaling to control tumor growth and survival [30]. Using the global lipidomics approach, we found that circ_63706 markedly suppressed ceramide and sphingolipid production (Fig. 5F–I). Total sphingomyelin (SM; $$n = 105$$) was significantly upregulated in circ_63706 knockdown cells (Fig. 5F). When restricted to the top 40 SMs, their levels were consistently upregulated in circ_63706 knockdown cells (Fig. 5G). Ceramide accumulates as a bioeffector that mediates cancer cell death. We found that circ_63706 knockdown cells were significantly enriched for different forms of ceramide including Cer (ceramide) itself and its different subclasses such as CerNS (nitroso-ceramide), CerP (phosphorylated-ceramide), and CerG (glycosylated-ceramide) (Fig. 5H, I). Overall, circ_63706 may play a critical role in suppressing the accumulation of the bioactive lipid molecules to favor medulloblastoma proliferation and growth. Fig. 5Circ_63706 suppresses lipid oxidation and membrane components. The box plot presents total oxidized lipids in circ_63706 knockdown cells (A) and the heatmap represents the top 40 oxidized lipid molecules in circ_63706 knockdown cells (B). The box plot presents total TG (C) and DG (D) lipids in circ_63706 knockdown cells, and the heatmap represents the top 40 TG molecules in circ_63706 knockdown cells (E). The box plot presents total SMs (F) and the heatmap represents the top 40 SM molecules in circ_63706 knockdown cells (G). The box plot presents total ceramide and its subclasses in circ_63706 knockdown cells (H) and the heatmap represent the top 40 ceramides and its subclasses in circ_63706 knockdown cells compared with control (I) Fatty acids (FAs) and their biochemistry are increasingly recognized as important in cancer and therapeutic development. We found that circ_63706 knockdown significantly enriched for active lipid ontologies including sphingosine, sphingomyelin, and ceramide, while circ_63706 overexpression significantly reduced the active lipid component. Unsaturated fatty acids, specifically fatty acids with 2, 3, and 6 double bonds, were elevated in circ_63706 knockdown cells. Interestingly, saturated lipids were elevated in circ_63706-overexpressing cells (Additional file 3: Fig. S16). Strikingly, there were distinct modulations of FA chain length in circ_63706 knockdown SHH cells: FAs with 20 or more carbons were significantly reduced and FAs with 18 or fewer carbons were markedly elevated in circ_63706 knockdown SHH cells. Taken together, our data demonstrate that circ_63706 promotes FA metabolism in the SHH medulloblastoma subtype.
## Mapping the secondary structure and modeling the 3D structure of circ_63706
Characterizing the 3D structures of circRNAs is crucial for understanding their cellular functions such as microRNA sponging and interactions with RNA-binding proteins and other RNAs. The experimental determination of RNA structure is challenging, but circRNA structure determination is especially difficult due to significant overlap between circRNA and linear RNA sequences [22]. There is no experimentally determined circRNA structure currently available. Recent advances in computational modeling based on experimental RNA structure data have allowed RNA structures to be predicted with high accuracy. This information is important for future therapeutic targeting either with antisense oligonucleotides or small molecules that bind to highly conserved secondary structures in circRNAs. Therefore, we mapped the secondary structure of the circ_63706 sequence using MXfold2, which uses deep learning to integrate thermodynamic information to accurately predict secondary structures of newly discovered ncRNAs [34]. This information was used to predict the 3D structure of circ_63706 with FARFAR2, which uses an RNA fragment assembly method to model RNA structures.
A 500-ns molecular dynamics simulation of the modeled circ_63706 showed that the double-stranded pairings were stable, while the overall tertiary structure remained flexible. Figure 6 compares the initial modeled structure with that at the end of the 500 ns simulation. Analysis of the structure with x3DNA-DSSR showed that the circ_63706 structure has two major dsRNA helix stems with 29 and 19 base pairs, respectively, as well as several other shorter segments ranging from 2 to 9 base pairs. Similarly, the structural features also include three 3-way junctions, one 4-way junction, six hairpin loops, and 12 internal loops. To explore the binding regions of the miRNA, we used the database of 87 mature miRNA human sequences from miRBase paired with the circ_63706 sequence using IntaRNA. Six top-ranked circRNA-miRNA duplexes, along with their binding energies compared with those within the circRNA structure, are shown in Additional file 4: Table S6. The binding sites in the structure are highlighted in Fig. 6.Fig. 6Mapping the secondary structure and modeling the 3D structure of circ_63706. The structure of circ_63706 A after the minimization of the modeled structure and B after 500 ns of MD simulation. Potential miRNA binding sites are highlighted in red and numbered according to the list in Additional file 4: table S6
## Discussion
To identify medulloblastoma subgroup-specific circRNA biomarkers, we subjected publicly available RNA-seq data to the CIRI2 circular RNA detection pipeline. By applying machine learning and statistical methods, we identified group-specific circRNAs. Among these identified circular RNAs, circ_63706 (hsa-PCNT_0003, CircAtlas 2.0) was a potential SHH subgroup-enriched molecule, confirmed by qPCR and RNA-FISH of clinical tissue samples.
WNT and SHH MBs generally contain mutations activating those pathways and, aside from rare TP53-mutant SHH tumors, are less aggressive than G3 and G4 tumors. Biomarkers for these two groups include immunohistochemical stains for YAP1, nuclear β-catenin, monosomy 6 in WNT tumors, and identifying activating pathway mutations through sequencing [9]. SHH MBs are generally identified by co-expression of GAB1 and YAP1 and by demonstrating activating mutations. Transcriptional or methylation profiling approaches can also distinguish the four groups but are usually not accepted clinical assays.
A recent study identified somatic copy number aberrations (SCNAs) in 1,087 unique MBs [26]. The most common focal copy number gain was a tandem duplication of SNCAIP, a gene associated with Parkinson’s disease, exquisitely restricted to group IV alpha. Recurrent translocations of PVT1, including PVT1-MYC and PVT1-NDRG1 arising through chromothripsis, were limited to G3 tumors. Numerous targetable SCNAs, including recurrent events targeting TGF-beta signaling in G3 and NF-kappaB signaling G4, are attractive candidate molecular markers. Circular RNAs can act as tumor suppressors or oncogenes, but little is known about their role in MBs. In a recent study, Lv et al. [ 25] selected four paired normal cerebellum and medulloblastoma tissue samples for sequencing and identified 33 differentially-expressed circRNAs in medulloblastoma tissues. Two of these circRNAs, circular-spindle and kinetochore associated complex subunit 3 (circ-SKA3) and circ-DTL, promoted the malignant phenotype of medulloblastoma when upregulated. Significantly higher expression of circ-SKA3 has also been reported in medulloblastoma compared with normal tissues [25].
Ours is the first comprehensive in silico analysis of circular RNAs in medulloblastoma and the first detailed characterization of one circular RNA (circ_63706; hsa-PCNT_0003, CircAtlas 2.0) in medulloblastoma patient tissues. The availability of specific classes of lipid is critical for successful cancer cell proliferation. TGs are critical to cell membrane homeostasis, lipid droplet formation, and signaling through lipid rafts for oncogenesis [2]. We demonstrated that circ_63706 is important for modulating TG and DG levels in SHH cells (Fig. 5 and Additional file 3: Fig. S15). Since cell membranes and their major components such as lipid rafts harboring signaling receptors cannot function properly without the appropriate DG-TG distribution [1], circ_63706 may play an important role in regulating the lipid component of cell or organelle membranes and, consequently, facilitating the malignant MB phenotype. Indeed, we observed that circ_63706 overexpression positively modulates membrane components, plasmalogen, and glycerolipids and glycerophospholipids (Additional file 3: Fig. S15). An accumulation of oxidized lipid is toxic to cancer cells through induction of cell death through ferroptosis [35]. Here we revealed that circ_63706 significantly decreases oxidized lipids in SHH cells and could be responsible for the observed increased MB cell proliferation. circ_63706 globally affects bioactive lipids and may be a potential therapeutic target. Cancer cells harbor differential FA saturations and chain lengths, and this fatty acid biochemistry and its perturbation can form the basis for therapeutic targets [17]; indeed, circ_63706 significantly modulated both fatty acid saturation and chain length (Additional file 3: Fig. S16). Since fatty acids are the building blocks of major lipids and their dysregulation plays critical roles in reprogramming cancer cell metabolism [10], this is a potential mechanisms by which circ_63706 drives cancer cell proliferation, growth, and metastasis, as circ_63706 overexpression drive increase cell proliferation (Additional file 3: Fig. S13).
Our molecular dynamics simulation of the modeled circ_63706 suggest that several miRNAs can bind to circ_63706 (Additional file 4: table S6). hsa-miR-26b-3p is a known tumor suppressor that regulates cellular proliferation, growth, and metastasis in osteosarcoma [5] and breast cancer [20]. Similarly, hsa-miR-103a-2-5p is a tumor suppressor in prostate cancer [4] and in squamous cell carcinoma of the tongue [23]. Interestingly, circRNA circ_0007142 can function as an miRNA sponge and inhibit hsa-miR-103a-2-5p, promoting proliferation in colorectal cancer [44]. Also, sequestering of hsa-miR-103a-2-5p by another circRNA circ_0087293 (circRNA-SORE) acting as a sponge promotes drug resistance in hepatocellular carcinoma [39]. We detected hsa-miR-22-5p as another potential circ_63706-binding miRNA, which is reported as a suppressor of breast cancer metastasis [14] and can reverse drug resistance in colon cancer [18].
Overall, this study provides several important insights into novel molecular signatures of medulloblastoma subgroups for diagnostic and therapeutic purposes.
## Supplementary Information
Additional file 1: Supplementary File. Additional file 2: Supplementary Methods. Additional file 3: Supplementary Figures. Additional file 4: Supplementary Tables.
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|
---
title: 'Seroprevalence of hepatitis B virus surface antigen (HBsAg) in Egypt (2000–2022):
a systematic review with meta-analysis'
authors:
- Ahmed Azzam
- Heba Khaled
- Ola A. Elbohy
- Shueb Abdirahman Mohamed
- Sana Mostafa Hussein Mohamed
- Ahmed H. Abdelkader
- Ahmad Ashraf Ezzat
- Amora Omar Ibrahim Elmowafy
- Ola Ali El-Emam
- Mona Awadalla
- Neveen Refaey
- Shimaa Mohamed Abdou Rizk
journal: BMC Infectious Diseases
year: 2023
pmcid: PMC10007808
doi: 10.1186/s12879-023-08110-5
license: CC BY 4.0
---
# Seroprevalence of hepatitis B virus surface antigen (HBsAg) in Egypt (2000–2022): a systematic review with meta-analysis
## Abstract
### Background
Hepatitis B infection seriously threatens global public health, especially in developing nations. Despite several investigations on HBV incidence, the national pooled prevalence remains unknown, particularly in populations at-risk at whom interventions should be primarily aimed.
### Methods
A comprehensive literature search of the following databases: Medline [PubMed], Scopus, Google Scholar, and Web of Science was conducted following the PRISMA guidelines. I-squared and Cochran's Q were used to measure the heterogeneity between the studies. Publications that matched the following were included: Primary studies published in Egypt from 2000 to 2022 reported HBV prevalence based on HBsAg. We excluded any studies that were not performed on Egyptians or that were performed on patients suspected of acute viral hepatitis or studies focusing on occult hepatitis or vaccination evaluation studies, or national surveys.
### Results
The systematic review included 68 eligible studies reporting a total of 82 incidences of HBV infection based on hepatitis B surface antigen with a total sample size of 862,037. The pooled national prevalence among studies was estimated to be $3.67\%$ [$95\%$ CI; 3: 4.39]. Children under 20 with a history of HBV vaccination during infancy had the lowest prevalence of $0.69\%$. The pooled prevalence of HBV infection among pregnant women, blood donors, and healthcare workers was $2.95\%$, $1.8\%$, and $1.1\%$, respectively. While patients with hemolytic anemia and hemodialysis patients, patients with malignancies, HCC patients, and chronic liver disease patients had the highest prevalences at $6.34\%$, $25.5\%$, $18.6\%$, and $34\%$, respectively. Studies reporting HBV prevalence in urban settings compared to rural settings revealed a similar HBV prevalence of $2.43\%$ and $2.15\%$, respectively. Studies comparing HBV prevalence in males and females revealed a higher prevalence among males ($3.75\%$) than females ($2.2\%$).
### Conclusion
In Egypt, hepatitis B infection is a significant public health issue. The blocking of mother-to-infant hepatitis B transmission, the scaling up of the scope of the existing vaccination program, and implementing new strategies, including screen-and-treat, may reduce the prevalence of the disease.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12879-023-08110-5.
## Background
Hepatitis B virus (HBV) is a partially double-stranded DNA virus belonging to the genus Orthohepadnavirus and the virus family Hepadnaviridae [1]. HBV specifically attacks the liver and can cause both acute and chronic diseases. The HBV life cycle is unique in that the circular partially double-stranded DNA (rcDNA) is converted to covalently closed circular DNA (cccDNA). The latter is used as a transcriptional template for all viral gene products, including the pregenomic RNA (pgRNA) [2]. Because current antiviral therapy with nucleos(t)ide analogues interferes with pgRNA reverse transcription and has limited effect on cccDNA, which appears as a stable minichromosome inside the nucleus of infected hepatocytes, HBV-infected patients require lifelong antiviral therapy [3–5].
There are 2 modes of HBV transmission: first, perinatal transmission or vertical transmission (from mother to child at birth). Second, horizontal transmission (transmission among individuals of the same generation). The most common mode of transmission changes with the endemicity of HBV. In areas with high endemicity, HBV is primarily transferred vertically from infected mothers to neonates around the time of birth; in addition, "horizontal" transmission by close contact between children has also been documented [6]. In low-endemicity areas, however, HBV infection is primarily acquired during adolescence and early adulthood and is strongly linked to high-risk behaviors such as unprotected sex and injectable drug use [6]. Chronic hepatitis B Antiviral therapy has decreased the rates of liver decompensation and, as a result, lowered hospitalization and mortality rates. Moreover, the longer-term benefits of antiviral therapy may include reversing liver fibrosis, reducing the risk of developing hepatocellular carcinoma, and decreasing the number of patients requiring liver transplantation [7–9].
Globally, an estimated 257–400 million people have chronic HBV infection [10]–[12]. Correspondingly, an estimated $29\%$ of cirrhosis-related deaths worldwide were due to HBV [13]. Hepatitis B now ranks as the 15th leading cause of global mortality worldwide [14].
The prevalence of HBV varies worldwide, with the highest levels in sub-Saharan Africa and some countries in the Western Pacific region [10, 13]. An earlier meta-analysis conducted in Egypt that included 13 studies covering the period from 1983 to 2002 estimated the pooled prevalence of HBV to be $6.7\%$ among healthy populations and $25.9\%$ among hepatocellular carcinoma (HCC) [15]. The most recent Egyptian Health Issues Survey (EHIS), conducted in 2015 by El-Zanaty and colleagues, estimated a $1\%$ prevalence of HBV infection based on HBsAg seroprevalence among 26,047 healthy participants aged 1–59 years and a $1.56\%$ among 16,003 healthy participants aged 15–59 years [16].
Despite several investigations examining the incidence of HBV, the national pooled prevalence of HBV in Egypt remains unknown, especially in specific subpopulations at which intervention should be aimed. So we conducted a systematic review with meta-analysis to overcome the shortcomings of individual research and promote an improved understanding of HBV epidemiology and provide the evidence necessary to guide research, policy, and programmatic efforts in Egypt.
## Search strategy
A comprehensive literature search of the following databases: MEDLINE [PubMed], Scopus, Google Scholar, and Web of Science was conducted using the following keywords: hepatitis B, Hepatitis B virus (HBV), hepatitis B surface antigen (HBsAg), viral liver disease, viral hepatitis, and Egypt. The review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement [17] and was registered in the PROSPERO International prospective register of systematic reviews, registration number CRD42022338782.
Additional file 1: Tables S1 and S2 illustrate the preferred reporting items for systematic reviews and meta-analyses checklist and the search strategy used in PubMed, respectively.
## Inclusion and exclusion of studies
We included studies that fully satisfied all of the following: Only primary studies (cross-sectional, case–control, or cohort studies) of participants residing in Egypt, Studies reporting the prevalence of HBV infection based on the presence of hepatitis B surface antigen and published in English between January 1st, 2000, and August 1st, 2022.
Studies were excluded if any of the following conditions were met: studies focusing on occult infection studies that were not carried out in Egypt or on Egyptian immigrants, Patients suspected of having acute viral hepatitis, non-human studies, the HBsAg detection method was not clear, and the full text wasn't available. We also excluded national surveys, studies based on the data of a national survey, and vaccination evaluation studies.
Studies were selected based on the aforementioned inclusion and exclusion criteria by two independent authors (AAZ, HK). Any disagreement was settled by consensus among all authors.
## Data extraction
The data extraction was conducted by four investigators (AAZ, HK, NR, and MA) and cross-checked by the rest of the authors.
From each included study, the following was extracted: the last name of the first author; publication time; study period; sample size; total HBsAg positive patients; region, population; study; age range; male/female%; setting (urban/rural%); and participants with anti-HBs levels of less than 10 IU/L.
To decrease the heterogeneity between studies, we classify them based on the risk of exposure into:(A)Low-risk populations are subdivided into children under the age of 20 with a history of HBV vaccination during childhood, healthy adults, pregnant women, and blood donors.(B)Intermediate-risk populations, such as healthcare workers and other workers who may be at risk due to occupational exposure, such as barbers or waste and sewage workers.(C)High-risk populations, such as patients with hemolytic anemia who require blood transfusions or patients with end-stage renal failure who require hemodialysis, as well as people who have had direct contact with HBV and HIV-infected patients.(D)Patients with liver-related conditions.(E)Patients with special conditions such as malignancies and other special cases.
## Quality assessment
The quality of the included studies was checked using a 12-point scoring system based on the Downs and Black checklist [18], adopted in similar reviews [19, 20] by three reviewers(OAE, SAM, and SMHM) and crosschecked by two independent reviewers (AAE, AHA). The 12-points were: (objective of the study was clearly described, the study design was clearly stated, participants were representative of the population from which they were recruited, participants accrued during the same time, modest sample size, management of missing data, age, gender and other characteristics explored/reported, e.g. were confounders reported, was detection method of HBV reported, were potential biases reported, was outcome clearly described?
Studies were classified into 3 grades: Grade A (12–9), Grade B (8–5), and Grade C (4–1).
## Data synthesis
I-squared and Cochran's Q were used to measure the heterogeneity between the studies and based on the random effects model, results were reported as proportions with a $95\%$ confidence interval (CI). Analyses of the subgroups were conducted based on the aforementioned subclasses and demographics of the participants. All statistical analysis was performed using StatsDirect statistical software (Version 3.0.0, StatsDirect Ltd, Cheshire UK). Publication bias testing by funnel plot and associated tests were not conducted as they do not produce reliable results for meta-analysis of proportions [21].
## Study selection
Figure 1 outlines the schematic flow of the studies identification and inclusion processes. A total of 1809 records were identified through the literature search. 607 duplicates were removed. The remaining 1202 publications were then evaluated by title and abstract, and 1109 articles were found to be irrelevant and excluded. The remaining 93 articles were reviewed for eligibility using full text, and 28 were rejected. Three additional eligible studies were identified by searching the reference lists of the included studies, bringing the total number of studies included to 68 [22–89].Fig. 1Flow chart depicting the selection of publications
## The characteristics of the included studies
The characteristics of the studies included are shown in Additional file 1: Tables S3–S7. Fourteen [14] of the 68 included studies were published from 2000 to 2009, while 54 studies were published from 2010 to August 2022. Seventeen studies were graded as "B" and 51 studies as "A". $96.34\%$ of the total sample size was for the low-risk population, $0.58\%$ for intermediate, $0.2\%$ for high-risk, $2.77\%$ for patients with liver-related conditions, $0.067\%$ for patients with malignancies, and $0.04\%$ for patients with heterogeneous cases (Table 1).Table 1Meta-analysis of HBV prevalence among subgroups in EgyptGroupSubgroupNo. of estimatesSample size (n)Pooled proportion (%)$95\%$ CI (%)HeterogeneityI$2\%$ (inconsistency)Cochran QP valueLow-riskChildren below 20 years with a history of HBV vaccination during infancy859600.69[0.23:1.4]76.229.40.0001Healthy adults1268,4942.4[1.28: 3.9]74.342.8 < 0.0001Pregnant women955222.95[1.6:4.6]89.878.3 < 0.0001blood donors15750,5151.8[1.4:2.3]99.42235 < 0.0001total44830,4911.93[1.55:2.35]994452.6 < 0.0001Intermediate-riskHealth care workers725541.1[0.55:1.8]53.9130.043Other workers325022.73[1.26:4.73]83.912.40.002total105056High-riskPatients with hemolytic anemia and hemodialysis patients1014406.34HBV-infected95.8216.3 < 0.0001direct contact with HBV-infected patients1154–––––HIV-infected patient1141–––––total1217355.86[1.8:12]95.2230.4 < 0.0001Liver related conditionsHCC2236218.6[4.69:38.9]99.2125.6 < 0.0001HCV23081.57[0.49:3.25]00.2570.6117CLD221,21534[8.8:65.59]97.540 < 0.0001Total23,885Special casesPatients with malignancies652625.5[14.37:38.6]89.547.6 < 0.0001Heterogeneous cases43452.27[0.95:4.1]2.73.080.3788Overall total82862,0373.67[3:4.39]99.515,617 < 0.0001HCC Hepatocellular carcinoma, HCV hepatitis C virus, CLD chronic liver disease
## The pooled national prevalence
A total of 68 eligible reports were included in the systematic review, with an overall sample size of 862,037. Based on HBV surface antigen testing, 82 incidences of HBV infection were reported (Fig. 1). The pooled prevalence across studies, as shown in Fig. S1, was calculated to be $3.67\%$ ($95\%$ CI: 3: 4.39), with a high degree of heterogeneity, as shown by I2 = $99.5\%$. The pooled prevalence of HBV infection with $95\%$ CI for all categories is shown in Table 1, along with an evaluation of the heterogeneity.
## Low-risk population
Forty-four studies were classified as low-risk studies, totaling 830,491 people. The pooled prevalence of these studies was estimated to be $1.93\%$ ($95\%$ CI: 1.55–2.35) (Fig. 2), with a high heterogeneity of $99\%$ by I$2\%$. Eight studies reported the prevalence of HBsAg in children below 20 years with a history of HBV vaccination during infancy (sample size: 5960) and revealed the lowest prevalence of $0.69\%$ ($95\%$ CI; 0.23–1.4) (Fig. 3), with $76.2\%$ heterogeneity based on I$2\%$. Twelve studies reported the prevalence of HBsAg among healthy adults (sample size: 68,494) with a pooled prevalence of $2.4\%$ ($95\%$ CI; 1.28: 3.9) (Fig. 4). Nine studies discussed the prevalence of HBV in pregnant women (sample size: 5522) and showed the highest prevalence of $2.95\%$ ($95\%$ CI; 1.6–4.6) (Fig. 5), among the low-risk population but $95\%$ CI overlapped. There were fifteen studies investigating HBsAg prevalence in blood donors with a sample size of 750,515; these studies had a pooled proportion of $1.8\%$ ($95\%$ CI; 1.4: 2.3) (Fig. 6).Fig. 2Forest plot of HBV seroprevalence among the low-risk populationFig. 3Forest plot of Children below 20 years of age with a history of HBV vaccination during infancyFig. 4Forest plot of HBV among healthy adultsFig. 5Forest plot of HBV among pregnant womenFig. 6Forest plot of HBV among blood donors
## Intermediate-risk population
Ten studies were included in the intermediate-risk population group: seven reported HBsAg prevalence among healthcare workers, and three studies examined other occupationally exposed workers like barbers or waste and sewage workers. Unexpectedly, the pooled prevalence of HBV infection among healthcare workers was low at $1.1\%$ ($95\%$ CI: 0.55:1.8), with a moderate level of heterogeneity (I2 = $53.9\%$) (Fig. 7) and a total sample size of 2554. Three studies with a total sample size of 2502 reporting HBsAg prevalence among barbers or waste and sewage workers revealed a pooled proportion of $2.73\%$ ($95\%$ CI; 1.26:4.73) (Fig. 8), and a high level of heterogeneity (I2 = $83.9\%$).Fig. 7Forest plot of HBV seroprevalence among Health care workersFig. 8Forest plot of HBV seroprevalence among barbers, waste, and sewage workers
## High-risk population
The high-risk population group included twelve reports: ten studies on patients with hemolytic anemia requiring blood transfusion or with end-stage renal failure requiring hemodialysis (sample size: 1440), one study on individuals with direct contact with HBV-infected patients (sample size: 154), and one study on HIV-infected patients (sample size: 141), with a total sample size of 1735 (Table 1). As represented in Table 1, the pooled prevalence among the high-risk population was identified at $5.86\%$ ($95\%$ CI; 1.8:12), while it was $6.34\%$ ($95\%$ CI; 1.56:14) in patients with hemolytic anemia and the hemodialysis group (Figs. 9, 10, respectively).Fig. 9Forest plot of HBV seroprevalence among high-risk populationFig. 10Forest plot of HBV seroprevalence among Patients with hemolytic anemia requiring blood transfusion or with end-stage renal failure requiring hemodialysis
## Patients with liver-related conditions
Four studies reporting six incidences of HBV infection, two on HCC, two on HCV, and two on patients with chronic liver diseases were included. The total sample size for these studies was 23,885. Patients with chronic liver disease had the highest prevalence of $34\%$ ($95\%$ CI; 8.8:65.59), followed by HCC patients with $18.6\%$ ($95\%$ CI; 4.69:38.9), while the prevalence was the lowest in HCV-infected patients at $1.57\%$ ($95\%$ CI; 0.49:3.25) (Figs. 11, 12, 13, respectively).Fig. 11Forest plot of HBV prevalence among patients with chronic liver diseaseFig. 12Forest plot of HBV seroprevalence among Patients HCCFig. 13Forest plot of HBV seroprevalence among Patients with HCV
## Special clinical cases
Six studies reported HBsAg prevalence in patients with malignancies with a pooled proportion of $25.5\%$ ($95\%$ CI; 14.37:38.6) and a total sample size of 526 (Table 1; Fig. 14).Fig. 14Forest plot of HBV seroprevalence among Patients With malignancies Another four studies reported HBsAg prevalence in patients with rheumatoid arthritis and diabetes mellitus, with a total sample size of 345 and a pooled prevalence of $2.27\%$ ($95\%$ CI: 0.95: 4.1) (Fig. 15).Fig. 15Forest plot of HBV seroprevalence among patients with heterogeneous clinical cases
## Sub-group analyses based on gender and setting
Nine studies reporting HBV prevalence among male participants compared to female participants revealed a higher prevalence among males than females at $3.75\%$ and $2.2\%$, respectively (Table 2) (Additional file 1: Figs. S2, S3). While eight studies comparing HBV prevalence in urban and rural settings found nearly similar HBV prevalence rates of $2.43\%$ and $2.15\%$, respectively (Table 2) (Additional file 1: Figs. S4, S5, Tables S9, S10 present the characteristics of the studies according to gender and setting, respectively. Table 2Meta-analysis of HBV prevalence in Egypt according to gender and settingNo. of estimatesSample size (n)Pooled proportion (%)$95\%$ CI (%)HeterogeneityI$2\%$Cochran QP valueMale949,5233.75[2.08:5.9]98.5532.3 < 0.0001Female938,7962.2[1.25:3.4]8761.7 < 0.0001Rural813,9332.15[1.18:3.4]9287.5 < 0.0001Urban885272.43[1.45:3.65]87.556.2 < 0.0001
## Discussion
Infection with the Hepatitis B virus (HBV) is a major threat to global public health, particularly in developing countries [90, 91]. Egypt has one of the highest HCV prevalences in the world [92]. In spite of this, there is still no accurate estimation of the level of HBV prevalence at the national level in Egypt. In 2015, the World Health Organization (WHO) set a target to eliminate hepatitis B by the year 2030. In compliance with this target, we performed a systematic review with meta-analysis to overcome the shortcomings of individual research and promote an improved understanding of HBV epidemiology, provide the evidence necessary to guide research, policy, and programmatic efforts in Egypt, and highlight the need for additional follow-up research and preventive measures in subpopulations with high HBV prevalence.
Levels of HBV endemicity based on HBsAg prevalence have been classified into four categories: low ($2\%$), lower-intermediate (2–$4.99\%$), higher-intermediate (5–$7.99\%$), and high (≥ 8) [93]. Accordingly, the endemicity level of HBV infection in Egypt should be classified as lower-intermediate with a pooled prevalence of 3.67 ($95\%$ CI; 3: 4.39). A meta-analysis on the global prevalence of HBV infection done in the general population (blood donors, healthcare workers (HCWs), and pregnant women) estimated a $1.71\%$ ($95\%$ CI; 1.67–1.76) HBsAg prevalence in Egypt [10]. However, that meta-analysis included studies from 1965 to 2013 without information on the source of data for each country. In addition, the review provided a pooled estimate of prevalence with no data on populations at-risk, at whom interventions should be primarily aimed. Similar studies found that rural areas had a higher prevalence of HBV than urban areas [94–97]. According to the current analysis, however, HBV prevalence was greater in urban than rural regions, at $2.43\%$ and $2.15\%$, respectively, but their $95\%$ confidence intervals overlapped, which is likely explained by an equally uninformed population on health and hygiene measures. Males were more likely than females to be infected with HBV ($3.75\%$ vs. $2.2\%$), but their $95\%$ confidence intervals overlapped. This can be attributed to a tendency for Egyptian men to spend more time outdoors, making them more exposed to HBV infection.
The overall pooled HBV prevalence estimate among low-risk populations ($1.93\%$) was significantly lower than previous meta-analysis of HBsAg prevalence in Egypt among healthy individuals ($6.7\%$), which included studies from 1983 to 2002 [15]. Such a decrease in HBsAg prevalence may be attributed to the introduction of the HBV vaccination in 1992. The first step towards the eventual eradication of hepatitis B is the universal immunization of infants. In Egypt, the HBV vaccination program was applied in 1992 with a schedule of 2, 4, and 6 months of age [98]. Among all studied populations, children under 20 with a history of HBV vaccination in infancy had the lowest prevalence of $0.69\%$, indicating that HBV vaccination during infancy in Egypt provides adequate protection. The responsiveness to the HBV vaccine was also evaluated using the prevalence of unprotective levels of anti-HBs (< 10 IU/L) in a population with a history of infancy vaccination, which clearly demonstrated a high incidence of unprotective levels of anti-HBs over time, i.e., unprotective levels of anti-HBs were less common in children under 5 years and highest in those over 15 years (Additional file 1: Table S8). Long-term HBV vaccine protection should be investigated further in populations more than 20 years post-primary vaccination by assessing breakthrough infection and anti-HBs levels. The prevalence of HBV in pregnant females was the highest among the low-risk groups, with a pooled prevalence of $2.9\%$, but $95\%$ CI overlapped. Hepatitis B is most commonly transmitted from mother to child during birth (vertical transmission) as well as horizontally during early childhood. These routes are also responsible for the vast majority of chronic infections [99]. Efforts should be coordinated to eliminate these transmission routes in Egypt as being the most important strategy to control the HBV epidemic. Pregnant women should be screened during their antenatal care, and newborns of infected mothers should be given hepatitis B immunoglobulin (HBIG). Transfusion of blood or blood products can result in the spread of infectious diseases when proper procedures are not followed. Our results revealed a relatively low prevalence of HBV infection among blood donors ($1.8\%$), and this may be due to only healthy people aged 18–60 being allowed for blood donation.
A 2002 survey of Egyptian HCWs reported unsafe practices in the use and disposal of sharps and determined that HCWs had an average of 4.9 needlestick injuries per year [100]. Unexpectedly, the pooled prevalence of HBV infection among healthcare workers was low at $1.1\%$ despite occupational exposure. This low prevalence may be explained by healthcare workers' growing understanding and awareness of infection control. HBV is stable on environmental surfaces for at least 7 days [101] therefore, it can be transmitted through accidental injuries to at-risk individuals as a result of their occupational exposure, e.g., sewage and waste workers and barbers and their clients. According to the three studies discussing this issue, HBV prevalence in barbers and their clients ($4\%$) was higher than in waste workers and sewage workers ($1.49\%$ and $2.93\%$, respectively), suggesting a higher need for protection and follow-up for barbers.
Patients with hemolytic anemia and hemodialysis patients are especially vulnerable to infections, including HBV. According to the current analysis, the pooled HBV prevalence among those subpopulations is estimated at $6.34\%$. Strict adherence to standard infection prevention measures, regular screening of HBV markers, and, finally, HBV immunization for all patients may help minimize the incidence.
The high pooled estimates for HBsAg prevalence in patients with liver-related disorders, such as hepatocellular carcinoma and liver disease patients (Table 1) reflect the important role that HBV plays in the incidence of liver diseases in Egypt. Two studies found a prevalence of $1.57\%$ of HBV among chronically infected patients with HCV (Additional file 1: Table S6). This may be explained by the ability of HCV to inhibit HBV replication, leading to a greater incidence of occult HBV (HBV DNA-positive and HBsAg-negative) [102, 103], which was not evaluated in the two studies and may have contributed to the low prevalence.
In addition to the patients' immunosuppressed states, patients with cancer frequently need several transfusions and are more likely to contract blood-transmissible diseases like HBV. This may explain the high incidence rate of HBV among patients with cancer ($25.5\%$).
This current review provides the most updated figures regarding HBV prevalence and reflects the current situation in Egypt. However, there are several limitations to this review: First, studies used a variety of screening kits, and there may have been a difference in sensitivity and specificity between study periods, resulting in the different prevalence rates. Second, some studies had a small sample size. Third, not all studies reported the prevalence of HBV in rural compared to urban areas or males compared to females. Fourth, the overall prevalence may not be entirely representative of a true national prevalence, as there is no data about HBsAg prevalence in some regions.
## Conclusion
Our review highlights the prevalence of HBsAg in Egypt in the last two decades, particularly in those at high risk, for whom intervention should be targeted. More effort is needed to reduce infection rates by screening blood and blood products more thoroughly and emphasizing vaccination for those at high risk of infection. The universal immunization program, implemented in Egypt more than three decades ago, appeared to be effective. But universal antenatal hepatitis B virus screening programs also need to be implemented. Finally, community awareness will be required to properly address Egypt's HBV problem.
## Supplementary Information
Additional file 1. Table S1: Supplementary Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Table S2: PubMed search strategy for studies published between January 1st, 2000 and July 2022. Table S3: Studies reporting hepatitis B virus (HBV) seroprevalence among populations at low risk in Egypt. Table S4: Studies reporting hepatitis B virus (HBV) seroprevalence among populations at intermediate risk in Egypt. Table S5: Studies reporting hepatitis B virus (HBV) seroprevalence among populations at high risk in Egypt. Table S6: Studies reporting hepatitis B virus (HBV) seroprevalence patients with liver-related conditions. Table S7: Studies reporting hepatitis B virus (HBV) seroprevalence patients with specific clinical cases. Table S8: Prevalence of unprotective levels of anti-HBs (< 10 IU/L) in a population with a history of vaccination during infancy. Table S9: HBV prevalence according to gender. Table S10: HBV prevalence according to setting. Fig. S1: Forest plot of the prevalence of hepatitis B virus infection in all subpopulations in Egypt. Fig. S2: Forest plot of HBV prevalence among male participants. Fig. S3: Forest plot of HBV prevalence among female participants. Fig. S4: Forest plot of HBV prevalence in urban areas in Egypt. Fig. S5: Forest plot of HBV prevalence in rural areas in Egypt.
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|
---
title: 'Body perception in pregnant women: a qualitative study'
authors:
- Zahra Sohrabi
- Ashraf Kazemi
- Ziba Farajzadegan
- Mojgan Janighorban
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10007813
doi: 10.1186/s12884-023-05467-y
license: CC BY 4.0
---
# Body perception in pregnant women: a qualitative study
## Abstract
### Background
Dramatic body changes in pregnancy cause severe concerns among pregnant women about their appearance. Therefore, this study aimed to explore body perception in pregnant women.
### Materials and methods
The qualitative study, using the conventional content analysis approach, was conducted on Iranian pregnant women who were in their second or third trimester of pregnancy. Participants were selected through purposeful sampling method. In-depth and semi-structured interviews were held with 18 pregnant women aged 22 to 36 years, using open-ended questions. Sampling was performed until data saturation was reached.
### Results
Three main categories were extracted from 18 interviews: [1] “symbols,” with two subcategories, including ‘motherhood’ and ‘vulnerability,’ [2] “feelings toward body changes,” with five subcategories, including ‘negative feelings toward skin changes,’ ‘feeling unfit,’ ‘attention-drawing body shape,’ ‘the ridiculous body shape’ and ‘obesity,’ and [3] “attraction and beauty,” with two subcategories, including ‘sexual attraction’ and ‘facial beauty.’
### Conclusion
The results showed that pregnant women’s body perception could be described as maternal feelings and feminine attitudes toward changes during pregnancy compared to mental ideals of facial and body beauty. It is recommended that Iranian women’s body perception during pregnancy be evaluated using this study results and that counseling interventions be implemented for women with negative body perceptions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12884-023-05467-y.
## Background
Body Image (BI) is how individuals see their body and their perception of how others view them [1] and influenced by cultural and social factors [2]. BI impacts social and interpersonal relationships by affecting self-esteem [3]. Consequently, in some individuals, despite their normal appearance, persistent preoccupation with physical appearance would cause extreme and disturbing fear of being ugly or unattractive [4–6].
BI is a multidimensional concept that involves people’s positive and negative perceptions, thoughts, behaviors, and attitudes about their body and appearance [1, 7]. BI includes perceptual and attitudinal dimensions. Perceptual dimension refers to the extent to which individuals assume that their competence is measured by their appearance. The attitudinal dimension refers to two elements: orientation and evaluation [2].
One of the processes, which causes noticeable changes, is pregnancy. Pregnancy is a period when extensive physiologic changes occur in a relatively short period of about 40 weeks, during which body changes create new aspects of body perceptions. As the body encounters new changes, the mental preoccupation with its new shape takes on new dimensions. The difference between one’s mental and ideal images forms a positive or negative body image [2], According to a systematic review, women’s body perception depends on the social construction of beauty [8], which is a mental concept.
The previous study showed that these profound physiological changes would cause first-time mothers’ bodies to distance from their mental ideals [9]. Generally, when women’s body perception during pregnancy is not accepted and leads to negative attitudes toward their bodies, their mental health might be harmed [10]. Przybyła-Basista et al. ’s study showed that body acceptance during pregnancy was one of the predictors of prenatal depression [11].
Moreover, body perception distanced from mental ideals during pregnancy can lead to dissatisfaction with the body, which is associated with eating disorders [12]. The well-being of pregnant women could be harmed by eating disorders resulting from body dissatisfaction [13]. An eating disorder might be associated with insufficient weight gain during pregnancy [14, 15] and, thus, a preterm low birth weight infant [16]. In addition, there is evidence that disrupted body perception among pregnant women is associated with common pregnancy complications, such as pregnancy-related lumbopelvic pain [17].
Due to the importance of healthy behavior following changes in body perception, it is necessary to identify women’s perspectives toward better contextualizing the issue of the mental concept.
A previous study shows that while for light-skinned American women, slimness is a measure of beauty [18]. However, in Non-Western countries, particularly less developed countries such as Iran, large body sizes are accepted as a measure of beauty and are considered a symbol of better socioeconomic status [7].
Women’s distinct perspective toward pregnancy might affect their body perception in different socio-cultural conditions and there is insufficient scientific evidence for discovering different aspects of body perception during pregnancy in a traditional social context such as Iran. In this regards a qualitative approach is necessary to assess body perception during pregnancy among Iranian women. Therefore, this study was conducted to explore body perception during pregnancy.
## Study design and participants
The qualitative study was conducted on Iranian pregnant women who were in their second or third trimester of pregnancy from July 2019 to April 2020. Since truth and reality are not fixed and are formed by individuals in their specific socio-cultural context [19]; therefore, the content analysis approach was chosen. Participants were selected through purposeful sampling method. In-depth and semi-structured interviews were held with 18 pregnant women using open-ended questions. Sampling was performed until data saturation was reached.
Inclusion criteria included: ‘a singleton pregnancy without complications,’ ‘ability to speak Farsi,’ ‘no hearing problems,’ ‘no physical malformations,’ and ‘no mental or cognitive problems,’ based on the recorded information in participants’ medical files. The Ethics Committee of the Isfahan University of Medical Sciences approved this study.
## Sampling
In order to achieve maximum diversity, samples were selected using the purposive sampling method. In this type of sampling, the goal is to select individuals with a rich source of information to help the researcher gain a better understanding [20]. Moreover, to understand various aspects of the research, selecting the participants with the maximum diversity in terms of characteristics and position is essential. Therefore, to investigate the participants’ perception of their bodies, the samples were selected from a wide variety of individuals regarding characteristics (age, educational level, body mass index prior to pregnancy, employment status, and socio-economic context). Eighteen pregnant women referred to the health centers for prenatal care were selected as the study participants.
## Data collection
Data were collected through in-depth interviews, observations, and memo-writing and continued until data saturation. One researcher conducted sampling and interviews under the supervision of a reproductive health specialist. She identified the potential subjects and evaluated their inclusion criteria. The eligible pregnant women were invited to participate in the study, and the necessary explanations were provided. The time and place of the interview were determined considering the participants’ opinions.
Before performing in-depth interviews, participants’ demographic characteristics (age, educational level, employment status, gestational age, and monthly income) were recorded. Moreover, the body mass index before pregnancy was recorded based on the information from their medical file. Before the interviews, the study objective and method of implementing the interviews were explained to the participants. Besides, the researcher ensured the confidentiality of participants’ information and their volunteer participation, which had no impact on their care. At all stages of the study, participants’ values and decisions were respected. They were also allowed to withdraw from the study at any stage.
After obtaining informed consent from the participants, face-to-face, semi-structured, in-depth interviews were conducted in a private room at health centers or participants’ houses or workplaces based on their preferences. Interviews started with the open questions: “Describe your pregnant body.” and “Explain your feelings about your body.” The interview questions were developed using the opinions of two psychologists experienced in the body image field and consulting the research team.
Based on the provided information by the participants and in order to clarify the study objective, the following questions were asked: “Please explain more.” “ Give an example,” and “Where did this feeling originate from?” The duration of the interviews was determined based on participants’ interest, from 60 to 90 min.
In order to ensure the trustworthiness of the data, an effort was made to employ methods to maintain credibility, dependability, transferability, and conformability. Therefore, by spending a sufficient amount of time collecting the data and repeatedly reviewing them, using maximum diversity in sampling (selecting participants with diverse demographic characteristics), providing participants with feedback on interviews, and obtaining their confirmation on the categories extracted from the interviews, the credibility of the data was improved. In order to establish the stability of the data, the data dependence method was used. In other words, the interview transcriptions were provided to the qualitative research experts to confirm the relevance of the content and results.
## Data analysis
The interviewer and supervisor analyzed the data using the conventional qualitative content analysis approach and Granheim and Lundman’s method [21]. The researchers transcribed the interviews verbatim after repeatedly listening to them. Afterward, they read the transcripts several times to gain a general idea. All interviews and observations were considered analysis units. Related words, sentences, or paragraphs were considered meaning units. Afterward, codes were compared with each other regarding their similarities and differences and were categorized under more abstract categories. A second researcher analyzed the transcripts and interviews to confirm the reliability of the findings.
Based on the observations during meetings and communications with participants, their non-verbal reactions were noticed and recorded immediately after the interviews and analyzed. All of the interviews and observations were conducted by one researcher. Three individuals cross-coded the data, and data analysis was performed simultaneously with data collection.
## Results
In this study, 18 pregnant women were interviewed. Participants’ demographic characteristics are provided in Table 1. According to the results, pregnant women’s body perception was categorized into three main categories: “symbols,” “feelings toward body changes,” and “attraction and beauty.” The category; ‘symbols’ was derived from the following subcategories: ‘symbol of motherhood’ and ‘symbol of vulnerability.’ The category of ‘feelings toward body changes’ was derived from the subcategories: ‘negative feelings toward skin changes,’ ‘feeling unfit,’ ‘attention-drawing body shape,’ ‘the ridiculous shape of the body,’ and ‘obesity.’ The category of attraction and beauty was derived from the subcategories: ‘sexual attraction’ and ‘beauty’ (Table 2).
Table 1Demographic characteristics of the participantsParticipant’s No. AgeGABMIKg/m2Educational levelEmployment statusMonthly income (Rials) *P1262424.20Bachelor’s degreeHousewife10–20 millionP2323220.20Bachelor’s degreeEmployed> 30 millionP3342623.43Bachelor’s degreeEmployed10–20 millionP4222218.07Bachelor’s degreeHousewife10–20 millionP5332820.30Bachelor’s degreeHousewife> 30 millionP6333617.64DiplomaHousewife10–20 millionP7293324.50Bachelor’s degreeHousewife10–20 millionP8353323.07DiplomaHousewife< 10 millionP9353525.08DiplomaHousewife10–20 millionP10261120.01DiplomaEmployed10–20 millionP11303723.03Bachelor’s degreeEmployed10–20 millionP12283423.40DiplomaHousewife10–20 millionP13323326.40Bachelor’s degreeEmployed> 30 millionP14263721.42DiplomaHousewife< 10 millionP15363728.50DiplomaHousewife10–20 millionP16223822.50DiplomaEmployed10–20 millionP17262823.20DiplomaEmployed10–20 millionP18243429.40Master’s degreeEmployed10–20 million*One-dollar equivalent to 70,000 RialsGA gestational age, BMI Body mass index Table 2The main categories, sub categories and abstracted sub-categoriesMain categorySubcategorySymbolsSymbol of motherhoodSymbol of vulnerabilityFeelings toward body changesNegative feelings toward skin changesFeeling unfitAttention drawing body shapeRidiculous shape of the bodyObesityAttraction and beautySexual attractionBeauty
## Symbols
This category was derived from two subcategories: [1] a symbol of motherhood and [2] a symbol of vulnerability.
## A symbol of motherhood
Pregnant women had a maternal feeling toward the changes in their bodies, especially their growing abdomen. Most of them stated that those changes triggered their motherly feelings. A 26-year-old pregnant woman said: “I have a good feeling toward my body because I am going to be a mother. I am deeply fond of the baby growing inside me; that is why I love the growth of my abdomen.” ( P1).
## A symbol of vulnerability
Some pregnant women believed that the changes in their bodies during pregnancy were signs of vulnerability. A 28-year-old pregnant woman mentioned: “My abdomen is too big. I cannot even see my feet. Walking has become too hard for me. I am always afraid of falling. I am always stressed out. Somehow, I am taking care of myself and my baby. I would rather stay at home all the time.” ( P14)
## Feelings toward body changes
This category was achieved from five subcategories: [1] negative feelings toward skin changes, [2] feeling unfit, [3] an attention-drawing body shape, [4] the ridiculous shape of the body, and [5] obesity.
## Negative feelings toward skin changes
Pregnant women expressed irritation from changes on different parts of their skin and believed that color changes and stretch marks on their abdomen were unattractive. Moreover, color changes, stretch marks on the breasts, and darkened skin of the underarm and neck caused negative feelings. A 35-year-old pregnant woman stated: “My underarms and sides of my breasts have become darker, and my thighs look bluish. I am worried that I cannot get rid of them. I do not want anyone to see them because they look ugly.” ( P8)
## Feeling unfit
Pregnant women believed the changes were a factor in losing fitness and unpleasant body shape. Some women mentioned that they would compare their body shape with others and the standards for beauty in the media. The former clothes that no longer fit them would intensify their sense of being unfit. In this regard, a 29-year-old woman said: “My abdomen has become big. I have gained weight. I look awful, and I think my appearance has become ridiculous. My back has dented, and my abdomen has stuck out. I do not want to look at myself in the mirror. I am fat and out of shape. I feel like I have gained extra fat all over my body” (P7).
Moreover, pregnant women needed to hide the changes due to their breasts’ enlargement and sagging. A 26-year-old pregnant woman mentioned: “In the past, everything was good, and I was fit. Now, everything looks lumpy. My breasts have become big. It makes me sad that my body shape is ruined.” ( P17)
## An attention-drawing body shape
Women in the present study stated that their enlarged bellies and hips during pregnancy would draw others’ attention, making them uncomfortable. In this regard, a 26-year-old pregnant woman said: “My hips have become too wide and large. They look terrible! With these hips, I look ugly and unfit. It seems that my lower parts have become loose. I always try to put on a loose T-shirt to cover my hips. I think that everybody is looking at my hips.” ( P17)
## The ridiculous shape of the body
Some women considered their body shape ridiculous and believed those changes would look ridiculous from others’ point of view. A 36-year-old woman stated: “My flanks have enlarged. I do not have a good image in my mind. I do not know what to say, but I think that I look like a pear with a wide bottom.” ( P15)
## Obesity
Some women were satisfied with their weight gain and abdomen growth and considered it attractive. Others had negative feelings toward it. In this regard, a 26-year-old woman said: “I feel that I have gained weight only in my lower limbs. My upper body looks the same. My thighs have become very fat. When I look at myself in the mirror, I can understand exactly how big and fat my hips and legs are.” ( P 10)
## Attraction and beauty
This category was derived from two subcategories: [1] sexual attraction and [2] facial beauty.
## Sexual attraction
Some women mentioned that changes during pregnancy, especially in their breasts and genital area increased their sexual attraction. In this regard, a 33-year-old pregnant woman mentioned: “My breasts have become larger; I am so happy about it. Because my breasts were too small before, but now they look attractive. I think my husband finds me more gorgeous now. All men like this kind of change.” ( P 5) However, other women believed that those changes decreased their sexual attraction. In this regard, a 33-year-old pregnant woman said: “It makes me uncomfortable that my genital area has become dark. This darkness is obvious, and I feel ashamed when my husband sees it. I feel that my sexual attraction during intercourse has decreased.” ( P 10)
## Facial beauty
Some participants were pleased with the changes in their faces and considered themselves prettier. They believed that others likewise considered them more good-looking. In this regard, a 32-year-old woman said: “During the first few months, I thought I became more beautiful. I thought my skin was brighter. My eyes were more distinctive, and my lips were redder.” ( P 13) Some other women were dissatisfied with the changes, especially in their noses and lips. These women mentioned that using no cosmetics due to their possible harm to the infant made them unattractive. Moreover, attending friend gatherings and workplaces without wearing makeup increased their dissatisfaction. In this regard, a 28-year-old woman said: “My nose has become too big and ugly. My lips have an ugly bluish look. It is not pretty at all. I prefer to have only a big abdomen during pregnancy so that others understand I am pregnant. Besides all the weight I have gained, the nose and lips are the worse.” ( P 12)
## Discussion
The present study was conducted to explore body perception in pregnant women. The present study indicated that body perception would induce a sense of motherhood under the influence of women’s feelings toward pregnancy. Along with the body perception, changes in different body parts would create a new body perception leading to negative or positive feelings.
Results of this study revealed that some changes, such as the growth of the abdomen, symbolically led to the feeling of motherhood which helped mothers adapt to changes. In the study by Clark et al., most pregnant women positively adapted to the changes during pregnancy. Participants in that study stated that the fetus’s movements and experiencing a new sensation influenced their satisfaction with the changes during pregnancy [22].
This result might indicate mothers’ bond with their growing embryos. In line with the results of the present study, Bergbom et al. report that, since changes during pregnancy are a sign of fertility, they are associated with a pleasant feeling for pregnant women [23].
In the study by Whitaker et al., some pregnant women expressed satisfaction with their growing abdomen since they considered it a sign of pregnancy progress and the growth of the fetus [24]. Nash et al. believed that pregnant women considered the growth of their abdomen as a unique pregnancy change with which other changes are associated [25].
Women’s pleasant feeling toward their body changes during pregnancy might indicate their positive attitude toward pregnancy since some reports have shown that accepting new roles following pregnancy, especially the existence of the fetus, is one of the critical factors in accepting changes during pregnancy [9].
Along with maternal feelings created by pregnancy changes, especially the growth of the abdomen, a sense of vulnerability and the need to protect the fetus were also created. Most mothers felt that the changes, especially their enlarged abdomen, would expose the fetus to various damages. This issue has not been addressed in any other study.
In addition to the symbolic motherly perception women mentioned about their pregnancy, other dual emotions expressed were placed in the ‘perceptions of body changes’ category. Women in the present study expressed negative and positive perceptions of the changes during pregnancy. Most women had a negative body perception of the changes on their skin, especially stretch marks on their bellies and legs, which created an unpleasant perception of the skin.
The review by Meireles et al. showed contradictory and inconclusive findings related to body image in pregnant women. Some studies show an improvement in body dissatisfaction among pregnant women. On the other hand, dissatisfaction with body shape and weight among pregnant women was also underlined in other studies [26].
Pregnant women’s other body perception was ‘unfitness.’ They described the changes in their bodies as disproportionate. Some participants described their breasts as too big, which did not fit their body size. Earle et al., in their study, mentioned that while most pregnant women were pleased with their enlarged breasts, others were dissatisfied due to extensive breast enlargement and physical discomfort [27]. Hodgkinson et al. reported that pregnant women perceived their body changes as transgressing the socially constructed ideal and feminine beauty. They also considered the physical manifestation of the mothering roles incongruent with their roles as a partner [8]. Another result showed that enlarged body size in different parts during pregnancy created a ridiculous body shape in women’s minds. A study has stated that women feel ashamed of their enlarged bellies and hips during pregnancy. These women were concerned that the increased size would persist after delivery. Although the increased body size following pregnancy is an expected change, some women describe it as obesity.
The concept of obesity in the present study was in line with the study by Whitaker et al. They reported that women considered weight gain during pregnancy as a factor in losing self-confidence. It was also reported that the opinions of the husband, physician, parents, and friends impacted the formation of that perspective [24].
Haruna et al., in their study, revealed that, due to healthcare providers’ emphasis on controlling weight gain during pregnancy, pregnant women mostly felt disappointed. These women believed that healthcare providers’ extensive focus on weight gain would affect their satisfaction with the body in the long term [28]. These results indicated that women would require more information to distinguish body changes during pregnancy from obesity.
Another description of the body during pregnancy was women’s opinions about sexual attraction, especially following breast enlargement. This issue was mostly observed in women who were dissatisfied with the small size of their breasts before pregnancy. These women stated that the enlargement of their breasts increased their sexual attraction. In the study by Watson et al., participants stated that their husbands found them more attractive sexually [4].
Another finding of the present study indicated that facial changes created a perception of having a prettier face in some women. Consequently, they described their faces as more good-looking. In the study by Harper and Rail, women mentioned that extra weight distributed in their abdomen, hips, and thighs was acceptable; however, if it appeared on their faces and arms, they would feel dissatisfied [29]. Some of the changes in the face, such as fattening or enlargement of the lips, might be under women’s standards for beauty; however, if they were accompanied by nose swelling and melasma, it would lead to the unattractiveness of the face. Therefore, individuals’ standards for facial beauty could affect their perception of the face. It is necessary to interpret the results of the study considering certain limitations. An unwanted pregnancy can affect women’s perception of their pregnant body, which was not considered in this study. Other study limitations were including low sample size, lack of quantitative measures, and no fidelity checks within data analysis.
## Conclusion
The study results showed that pregnancy in Iranian women is experienced through a symbolic view of body changes, feelings toward changes, and attractiveness and beauty. Subcategories of symbols included understanding maternal symbols and vulnerability. Moreover, the subcategories of feelings toward changes and perception of attractiveness and beauty included positive and negative feelings toward the physiological changes during pregnancy, which might be associated with negative judgment about the body and affect the pregnant women’s mental health. The findings of this study could be used to help develop an educational and counseling program for pregnancy.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary material 1: Peer review report.
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|
---
title: HMGB1 mediates synaptic loss and cognitive impairment in an animal model of
sepsis-associated encephalopathy
authors:
- Xiao-Yu Yin
- Xiao-Hui Tang
- Shi-Xu Wang
- Yong-Chang Zhao
- Min Jia
- Jian-Jun Yang
- Mu-Huo Ji
- Jin-Chun Shen
journal: Journal of Neuroinflammation
year: 2023
pmcid: PMC10007818
doi: 10.1186/s12974-023-02756-3
license: CC BY 4.0
---
# HMGB1 mediates synaptic loss and cognitive impairment in an animal model of sepsis-associated encephalopathy
## Abstract
### Background
Microglial activation-mediated neuroinflammation is one of the essential pathogenic mechanisms of sepsis-associated encephalopathy (SAE). Mounting evidence suggests that high mobility group box-1 protein (HMGB1) plays a pivotal role in neuroinflammation and SAE, yet the mechanism by which HMGB1 induces cognitive impairment in SAE remains unclear. Therefore, this study aimed to investigate the mechanism of HMGB1 underlying cognitive impairment in SAE.
### Methods
An SAE model was established by cecal ligation and puncture (CLP); animals in the sham group underwent cecum exposure alone without ligation and perforation. Mice in the inflachromene (ICM) group were continuously injected with ICM intraperitoneally at a daily dose of 10 mg/kg for 9 days starting 1 h before the CLP operation. The open field, novel object recognition, and Y maze tests were performed on days 14–18 after surgery to assess locomotor activity and cognitive function. HMGB1 secretion, the state of microglia, and neuronal activity were measured by immunofluorescence. Golgi staining was performed to detect changes in neuronal morphology and dendritic spine density. In vitro electrophysiology was performed to detect changes in long-term potentiation (LTP) in the CA1 of the hippocampus. In vivo electrophysiology was performed to detect the changes in neural oscillation of the hippocampus.
### Results
CLP-induced cognitive impairment was accompanied by increased HMGB1 secretion and microglial activation. The phagocytic capacity of microglia was enhanced, resulting in aberrant pruning of excitatory synapses in the hippocampus. The loss of excitatory synapses reduced neuronal activity, impaired LTP, and decreased theta oscillation in the hippocampus. Inhibiting HMGB1 secretion by ICM treatment reversed these changes.
### Conclusions
HMGB1 induces microglial activation, aberrant synaptic pruning, and neuron dysfunction in an animal model of SAE, leading to cognitive impairment. These results suggest that HMGB1 might be a target for SAE treatment.
## Introduction
Sepsis-associated encephalopathy (SAE) is a diffuse cerebral dysfunction caused by sepsis without direct central nervous system (CNS) infection or other types of encephalopathy [1]. SAE manifests as varying degrees of impaired consciousness, from mild delirium to coma, along with electroencephalographic (EEG) changes [2, 3], which severely reduces patients’ quality of life, increases mortality rates, and places tremendous economic pressure on society, families, and individuals [4]. Previous studies have shown that SAE development may be related to neuroinflammation, blood‒brain barrier (BBB) disruption, abnormal synaptic function, and mitochondrial dysfunction [5–7]; however, the exact mechanism remains unclear.
High mobility group box-1 protein (HMGB1) is a nonhistone DNA binding protein present in the nucleus of eukaryotic cells. HMGB1 is released passively from damaged cells and is actively extracellularly secreted by activated immune cells [8]. When immune cells are exposed to microbe-associated molecular patterns, pathogen-associated molecular patterns, and endogenous inflammatory mediators, HMGB1 activates immune cells and mediates the inflammatory response [9]. One recent study demonstrated that pregabalin ameliorates microglial activation and neuronal damage by blocking the HMGB1 signaling pathway in radiation-induced brain injury [10]. HMGB1 is also involved in cognitive impairment-related diseases, such as Alzheimer’s disease (AD) and traumatic brain injury (TBI) [11, 12]. Notably, HMGB1 is a late mediator of inflammation in sepsis [13]. In an animal model of SAE, the serum levels of HMGB1 are increased in sepsis survivors, which remains elevated for at least 4 weeks after CLP [14, 15]. Inflachromene (ICM) is a small molecule that can block cytoplasmic localization and extracellular release of HMGBs by perturbing its post-translational modification [16]. ICM treatment inhibits the cytosolic translocation of HMGB1, which is confirmed by genetic experiments based on the silencing of HMGB1 [17]. A previous study also revealed that using ICM can inhibit HMGB1 release thus limiting fructose 1,6-bisphosphatase 1-dependent hepatic stellate cell activation, the development of the secretory phenotype and tumor progression [18]. However, the specific mechanism by which HMGB1 results in cognitive impairment in SAE remains unclear.
Microglia, the primary innate immune cell population in the brain, can actively release HMGB1. HMGB1 can bind to microglial surface receptors to activate downstream inflammatory pathways and promote microglial activation [9]. Microglial activation plays essential roles in immune surveillance, synaptic plasticity regulation, and maintaining dynamic homeostasis within the CNS [19]. Microglia are activated under pathological conditions, inducing synaptic loss, neuron dysfunction, and neural circuit disruption [20, 21]. These abnormalities might contribute to the pathogenesis of cognitive impairment in many diseases. Therefore, we hypothesized that HMGB1 secretion mediates microglial activation, aberrant synaptic pruning, and neuronal dysfunction, ultimately leading to cognitive impairment in SAE mice.
## Animals
C57BL/6JGpt male mice (10–12 weeks, 20–30 g) were provided by Jiangsu Jicui Pharmachem Biotechnology Co. All mice were housed under standard laboratory conditions with an automatically controlled temperature of 22 ± 2 °C, 55–$65\%$ humidity, and 12 h–12 h light–dark cycle. Four to 5 mice were housed per cage, with unlimited access to food and water.
## SAE model
SAE was induced by cecal ligation and puncture (CLP). The mice were anesthetized by intraperitoneal injection of $1\%$ pentobarbital sodium. A 1.5 cm midline incision was performed in the abdomen to expose the cecum. The exposed cecum was ligated with 4-0 sutures below the ileocecal valve (5 mm from the cecal tip). Then, a 22 G needle was used to puncture the cecum, and a small volume of feces was gently squeezed out. After the cecum was returned to the abdominal cavity, the abdomen was closed in sequence. The mice were resuscitated immediately by hypodermic injection of normal saline (20 ml/kg), placed on a heat blanket and returned to the original cage after awakening from anesthesia. The same incision was cut in the abdomen to expose the cecum in the sham group mice, and no ligation or perforation was performed.
## Experimental design and drug treatment
Mice were randomly assigned to the sham + vehicle group (sham + vehicle), CLP + vehicle group (CLP + vehicle), sham + inflachromene (ICM) group, or CLP + ICM group (CLP + ICM). ICM (Cayman, USA, 10 mg/kg) or vehicle (distilled water containing DMSO and PEG400) was administered intraperitoneally (i.p.) daily for 9 consecutive days beginning 1 h before CLP operation [22]. The experimental protocol is presented in Fig. 1A.Fig. 1ICM ameliorated sepsis-induced cognitive impairment. A Flow chart of the experiment. B Comparison of the mouse survival rates among the four groups. C Comparison of the mouse body weights among the four groups. D–F OFT performance among the four groups. G NOR performance among the four groups. H Y maze test performance among the four groups. Data are presented as the mean ± SEM ($$n = 8$$–12 mice per group). * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ vs. the indicated groups
## Open field test
The open field test was performed to evaluate the locomotor activity of mice. A white open box (40 × 40 × 40 cm) divided into 16 squares was used. Each mouse was placed in the center of the box and allowed to explore for 5 min freely. The total distance traveled in the arena was recorded. The chamber was cleaned with $75\%$ ethanol between each trial.
## Novel object recognition
New object recognition tests were carried out in a 40 × 40 × 40 cm white open box. The task procedure consists of the habituation, learning, and test phases. [ 1] In the habituation phase, mice were placed in the open box and allowed to explore freely for 10 min; [2] in the learning phase, after 1 day of habituation, mice were placed in the open box in the presence of two identical objects (A + A). [ 3] The test phase was conducted 24 h after the learning phase. One of the objects was replaced by a novel object, B, with a distinctively different color and shape. The length of time spent exploring familiar and novel objects was measured. The Discrimination Ratio was calculated as the new object exploration time/(new object exploration time + old object exploration time). The chamber and objects were cleaned with $75\%$ ethanol between each trial.
## Y maze
A Y maze test was conducted to measure spatial working memory. In this experiment, a Y-maze with three arms (A, B, and C) at a 120° angle was used. Each mouse was placed into the center of the maze at the beginning of the experiment and allowed to explore freely for 8 min. Spontaneous alternation was defined as three consecutive entries into different arms, e.g., ABC, BCA, and CAB. The percentage of spontaneous alternation was used to assess working memory ability. The percentage of spontaneous alternation was calculated as spontaneous alternation number/(total number of entries-2) × $100\%$. Ethanol ($75\%$) was used to clean the maze between each trial.
## Immunofluorescence
Mice were deeply anesthetized by i.p. injection of $1\%$ pentobarbital sodium and then transcardially perfused with ice-cold phosphate-buffered saline (PBS) followed by $4\%$ paraformaldehyde (PFA). The brains were removed and postfixed in $4\%$ PFA overnight and then dehydrated in $30\%$ sucrose for 72 h at 4 °C. Brains were then sliced into 30 μm coronal sections and mounted on slides. Slices were permeabilized and blocked with PBS containing $0.3\%$ Triton X-100 and $10\%$ NDS for 1 h at room temperature. Primary antibodies were incubated in PBS containing $0.3\%$ Triton X-100 and $5\%$ NDS overnight [rabbit anti-Iba1, 1:500 (WAKO, cat#019-19741); goat anti-Iba1, 1:1000 (Abcam, ab5076); rabbit anti-HMGB1, 1:100 (Abcam, ab18256); rat anti-CD68, 1:500 (BioLegend, cat#137001); mouse anti-vglut1, 1:500 (SYSY, 135011) and rabbit anti-c-Fos, (CST, cat#2250)]. The brain sections were then washed with PBS and treated with appropriate secondary antibodies conjugated with Alexa Fluor fluorophore in PBS containing $0.3\%$ Triton X-100 and $5\%$ NDS for 1 h at room temperature. The sections were rewashed with PBS and counterstained with DAPI (1:1000; Beyotime Biotechnology) for nuclear staining. Slides were coverslipped with $50\%$ glycerin. All images were acquired with Leica TCS SP5 and Zeiss LSM880 confocal microscopes.
## Golgi staining
Golgi staining was performed per the Golgi staining kit (PK401, FD NeuroTechnologies) manufacturer’s directions. Mice were anesthetized with $1\%$ pentobarbital sodium intraperitoneally. Whole brains were dissected, immersed in solution A + B, and stored in the dark at room temperature. After 2 weeks, the brains were transferred to solution C for 3 days. One hundred millimeter-thick coronal slices were cut at room temperature with a Vibratome (Leica VT1000S) and transferred onto $1\%$ gelatin-coated slides for staining. After staining with solution D + E + distillation-distillationH2O and dehydration with different concentrations of ethanol series, the slices were cleared in xylene and coverslipped with neutral balsam mounting medium. All images were acquired with an Olympus BX53 microscope and analyzed with Fiji.
## In vivo electrophysiology
The mice were anesthetized by intraperitoneal injection of $1\%$ pentobarbital sodium and fixed on the stereotaxic apparatus after complete anesthesia. The bregma points were exposed by cutting the scalp and adjusting the plane on the axis of the front and rear fontanel line to make it on the same level. Two points on the left side of the skull were selected for the placement of cranial nails. A 2.5 × 2.5 mm bone window was made on the right skull surface, and the dura was carefully picked out with a syringe needle. An 8-channel microfilament electrode array was fixed on a brain stereotaxic instrument and slowly placed in the CA1 region of the hippocampus (anteroposterior: − 2.2 mm, mediolateral: -1.5 mm, dorsoventral: − 1.6 mm). The electrodes were fixed with bone cement after determining successful embedding. Raw data were recorded in NOR on day 14 after surgery. In our study, the bands were classified as delta oscillations (1–4 Hz), theta oscillations (4–8 Hz), alpha oscillations (8–12 Hz), beta oscillations (12–30 Hz), and gamma oscillations (30–90 Hz). The signals were filtered with a passband of 0.3–300 Hz and further amplified and digitized at 2 kHz. All data analyses were performed by Neuroexplorer (Plexon Corporation, Dallas, TX) software.
## In vitro electrophysiology
After mice were anesthetized with isoflurane ($5\%$ air volume, 3 min), the whole brain was carefully removed and placed in 0 °C sucrose-rich artificial cerebrospinal fluid [ACSF; 126 NaCl, 2.5 KCl, 1 MgCl2, 1 CaCl2, 1.25 KH2PO4, 26 NaHCO3, 20 glucose (mM)]. The brain was coronally cut out according to the brain atlas in 0 °C sucrose-rich ACSF with $95\%$ O2 and $5\%$ CO2 oxygenation conditions (thickness 250 μm, blade speed 0.14 mm/s). The hippocampal slices were transferred into ACSF with sufficient oxygenation and incubated at room temperature for at least 45 min to ensure complete cell activity recovery. The hippocampal slices were placed on a Nikon orthomosaic microscope (FN-1 with a 10 × 40 water immersion objective stage). The stimulating electrode was placed in the Schaffer collateral (SC) to deliver the test and conditioned stimulation. The recording electrode was placed 200–300 µm from the stimulation electrode in the stratum radiatum of CA1. Forty to $50\%$ of the maximum EPSP value was used as the baseline. After 10 min of baseline stabilization, LTP was induced with three strings of high-frequency stimulation (HFS) (50 Hz, 100 pulse), and the slope of the potential was normalized to the mean base value. The data were recorded through a SutterPatch-IPA2 amplifier, and data acquisition was performed by selecting signals in the range of 0–10 kHz and filtering them through a 5 kHz low-pass filter. Igor Pro9.0 was used to analyze the data [23].
## Statistical analysis
GraphPad Prism 8.0 (GraphPad Software) was used for statistical analyses. Data are expressed as the mean ± standard error (mean ± SEM). The normality of data distribution was assessed by the Shapiro–Wilk test. When comparing multiple groups, data were analyzed using two-factor ANOVA followed by Tukey’s post hoc test when appropriate. Changes in body weight from baseline were assessed using repeated-measures analysis of variance. Kaplan‒Meier survival curves and log-rank analyses were used to compare survival outcomes between different groups. Statistical significance was defined at $P \leq 0.05.$
## ICM ameliorated sepsis-induced cognitive impairment
No mice in the sham + vehicle group died 14 days after surgery, while the 14-day survival rate after CLP was only $45\%$ (Fig. 1B). The survival rate slightly increased after ICM administration, but there was no significant difference between the two groups (Fig. 1B). Postoperative body weight significantly decreased after CLP, but there was no significant difference between the CLP and CLP + ICM groups (Fig. 1C). To assess locomotor activity and cognitive function, we performed OFT, Y maze, and NOR on days 14–18 after surgery. In the OFT, there was no significant difference in total distance traveled [Fig. 1D; interaction: CLP × ICM, F [1, 36] = 0.01335, $$P \leq 0.9087$$; CLP: F [1, 36] = 1.206, $$P \leq 0.8753$$; ICM: F [1, 36] = 2.537, $$P \leq 0.9391$$], distance traveled in the center [Fig. 1E; interaction: CLP × ICM, F [1, 36] = 1.246, $$P \leq 0.2717$$; CLP: F [1, 36] = 0.04634, $$P \leq 0.9996$$; ICM: F [1, 36] = 1.584, $$P \leq 0.7558$$], and time spent in the center [Fig. 1F; interaction: CLP × ICM, F [1, 36] = 0.3539, $$P \leq 0.5556$$; CLP: F [1, 36] = 0.5084, $$P \leq 0.8833$$; ICM: F [1, 36] = 0.1762, $$P \leq 0.7657$$] among the four groups (Fig. 1D, E). In the NOR test, the mice in the CLP + vehicle group spent less time with novel objects than those in the sham + vehicle group; this effect was reversed by ICM treatment [Fig. 1G; interaction: CLP × ICM, F [1, 33] = 8.644, $$P \leq 0.0060$$; CLP: F [1, 33] = 8.767, $P \leq 0.$ 05; ICM: F [1, 33] = 1.981, $P \leq 0.001$]. In the Y maze test, compared with the sham + vehicle group, the CLP + vehicle group showed a significant decrease in spontaneous alternation, and this effect was reversed by ICM treatment [Fig. 1H; interaction: CLP × ICM, F [1, 38] = 10.61, $$P \leq 0.0024$$; CLP: F [1, 38] = 9.633, $P \leq 0.0001$; ICM: F [1, 38] = 16.25, $P \leq 0.001$]. These results indicate that inhibiting HMGB1 rescues cognitive impairment in septic mice.
## ICM ameliorated the abnormal secretion of HMGB1 in the hippocampus of SAE mice
We examined the expression of nuclear HMGB1 (n-HMGB1) in the hippocampus to reveal the underlying mechanism of SAE. Compared with the sham + vehicle group, the mean gray value of n-HMGB1 in microglia was significantly decreased in the CLP + vehicle group in the hippocampus, and this effect was reversed by ICM treatment [Fig. 2A, B; interaction: CLP × ICM, F [1, 67] = 34.17, $P \leq 0.0001$; CLP: F [1, 67] = 20.24, $P \leq 0.$ 0001; ICM: F [1, 67] = 59.74, $P \leq 0.0001$]. However, there were no significant differences in n-HMGB1 expression levels in non-microglia cells among the four groups [Fig. 2C; interaction: CLP × ICM, F [1, 68] = 2.552, $$P \leq 0.1148$$; CLP: F [1, 68] = 3.991, $$P \leq 0.9504$$; ICM: F [1, 68] = 0.7091, $$P \leq 0.9920$$]. These results suggest that HMGB1 secreted from microglia increases after CLP.Fig. 2ICM ameliorated the abnormal secretion of HMGB1 in the hippocampus of SAE mice. A Representative images of immunofluorescence staining of Iba-1 (red), HMGB1 (green), DAPI (blue) and colocalization in the hippocampus, scale bar = 10 μm; B, C Quantification of the mean gray value of n-HMGB1 in microglia and non-microglia cells in the hippocampus among the four groups. Data are presented as the mean ± SEM ($$n = 3$$–4 mice/group). **** $P \leq 0.0001$ vs. the indicated groups
## ICM restored the changes in the number and morphology of microglia in the hippocampus of SAE mice
The number of microglia was significantly increased in the CLP + vehicle group compared with the sham + vehicle group, and ICM treatment reversed this effect [Fig. 3A, B; interaction: CLP × ICM, F [1, 60] = 0.3246, $$P \leq 0.5710$$; CLP: F [1, 60] = 41.44, $P \leq 0.05$; ICM: F [1, 60] = 13.71, $P \leq 0.0001$]. The microglial morphological analysis results showed that the average branch length and solidity of the microglia were significantly increased in the CLP + vehicle group compared to the sham + vehicle group; ICM treatment reversed this effect [Fig. 3C–E; D: interaction: CLP × ICM, F [1, 116] = 9.551, $$P \leq 0.0025$$; CLP: F [1, 116] = 14.96, $P \leq 0.001$; ICM: F [1, 116] = 10.36, $P \leq 0.0001$; E: interaction: CLP × ICM, F [1, 68] = 19.25, $P \leq 0.0001$; CLP: F [1, 68] = 28.26, $P \leq 0.0001$; ICM: F [1, 68] = 49.09, $P \leq 0.0001$]. The immunofluorescence results indicate that ICM reverses the microglial activation induced by CLP.Fig. 3ICM restored the changes in the number and morphology of microglia in the hippocampus of SAE mice. A Representative images of immunofluorescence staining of Iba-1 (green) and DAPI (blue) in the hippocampus, scale bar = 50 μm; B quantification of the number of Iba-1+ cells (microglia) in the hippocampus among the four groups; C representative images of immunofluorescence staining of Iba-1 (yellow) and skeletonization of Iba-1+ cells (microglia) in the hippocampus, scale bar = 50 μm; D quantification of the average branch length of Iba-1+ cells (microglia) in the hippocampus among the four groups; E quantification of the solidity of Iba-1+ cells (microglia) in the hippocampus among the four groups. Data are presented as the mean ± SEM ($$n = 3$$–5 mice/group). * $P \leq 0.05$, ***$P \leq 0.001$, ****$P \leq 0.0001$ vs. the indicated groups
## ICM reversed the enhanced phagocytic activity of microglia in the hippocampus of SAE mice
We measured microglial CD68 levels by immunofluorescence to further assess the changes in the phagocytic activity of microglia. The area percentage of CD68 expression in Iba1+ cells in the CLP + vehicle group was significantly increased compared with that in the sham + vehicle group, and this effect was reversed by ICM treatment [Fig. 4A, B; interaction: CLP × ICM, F [1, 184] = 22.61, $P \leq 0.0001$; CLP: F [1, 184] = 3.115, $P \leq 0.0001$; ICM: F [1, 184] = 13.04, $P \leq 0.0001$]. These data suggest that HMGB1 significantly increases microglial CD68 expression and provide evidence that HMGB1 may affect microglial phagocytic activity in SAE.Fig. 4ICM reversed the enhanced phagocytic activity of microglia in the hippocampus of SAE mice. A Representative images of immunofluorescence staining of CD68 (green) and Iba-1 (red) and colocalization in the hippocampus, scale bar = 5 μm; B quantification of the percentage of CD68 area within Iba-1+ cells (microglia) in the hippocampus among the four groups. Data are presented as the mean ± SEM ($$n = 4$$–6 mice/group). **** $P \leq 0.0001$ vs. the indicated groups
## ICM reduced microglia-mediated synapse elimination in the hippocampus of SAE mice
We evaluated the area of engulfed VGLUT1+ puncta within Iba1+ cells by immunofluorescence to investigate whether HMGB1-induced microglial activation leads to synapse engulfment in SAE. The results showed that Iba1+ cells from the hippocampus of the CLP + vehicle group engulfed significantly more VGLUT1+ puncta than hippocampal Iba1+ cells from the sham + vehicle group, and this effect was reversed by ICM treatment [Fig. 5A, B; interaction: CLP × ICM, F [1, 116] = 9.551, $$P \leq 0.0025$$; CLP: F [1, 116] = 14.96, $P \leq 0.001$; ICM: F [1, 116] = 10.36, $P \leq 0.0001$]. These data suggest that HMGB1 leads to aberrant pruning of hippocampal excitatory synapses through microglial activation. Fig. 5ICM reduced microglial-mediated synapse elimination in the hippocampus of SAE mice. A Representative images of immunofluorescence staining of VGLUT1 (green), Iba-1 (red) and colocalization in the hippocampus, scale bar = 5 μm; B quantification of the percentage of VGLUT1 engulfment within Iba-1+ cells (microglia) in the hippocampus among the four groups. Data are presented as the mean ± SEM ($$n = 3$$–5 mice/group). *** $P \leq 0.001$ and ****$P \leq 0.0001$ vs. the indicated groups
## ICM ameliorated abnormal neuronal morphology and loss of dendritic spines in the hippocampus of SAE mice
We next analyzed the morphology of Golgi-stained neurons and found that after CLP, the number of dendritic crossings of pyramidal neurons at 50 μm and 100 μm from the cell body was significantly reduced; ICM treatment reversed this effect [Fig. 6A, B; 50 μm: interaction: CLP × ICM, F [1, 68] = 10.06, $$P \leq 0.0023$$; CLP: F [1, 68] = 32.60, $P \leq 0.0001$; ICM: F [1, 68] = 34.23, $P \leq 0.0001$; 100 μm: interaction: CLP × ICM, F [1, 68] = 1.950, $$P \leq 0.1671$$; CLP: F [1, 68] = 28.30, $P \leq 0.0001$; ICM: F [1, 68] = 35.03, $P \leq 0.0001$]. Compared with the sham + vehicle group, the number of neuronal branches in the CA1 was significantly reduced in the CLP + vehicle group, and this effect was reversed by ICM treatment [Fig. 6C, D; interaction: CLP × ICM, F [1, 68] = 13.38, $$P \leq 0.0005$$; CLP: F [1, 68] = 32.02, $P \leq 0.0001$; ICM: F [1, 68] = 75.39, $P \leq 0.0001$]. Compared with the sham + vehicle group, the total length of neuronal branches in the CA1 in the CLP + vehicle group was significantly reduced, and ICM treatment reversed this effect [Fig. 6E; interaction: CLP × ICM, F [1, 68] = 20.44, $P \leq 0.0001$; CLP: F [1, 68] = 60.92, $P \leq 0.0001$; ICM: F [1, 68] = 76.45, $P \leq 0.0001$]. An analysis of dendritic spine density in the CA1 by Golgi staining revealed that the dendritic spine density of pyramidal neurons in the CA1 of the hippocampus was significantly reduced in the CLP + vehicle group compared with the sham + vehicle group, and ICM treatment reversed this effect [Fig. 6F, G; interaction: CLP × ICM, F [1, 68] = 42.89, $P \leq 0.0001$; CLP: F [1, 68] = 72.26, $P \leq 0.0001$; ICM: F [1, 68] = 69.57, $P \leq 0.0001$]. These findings suggest that HMGB1 mediates neuronal morphology damage and loss of dendritic spines. Fig. 6ICM ameliorated abnormal neuronal morphology and loss of dendritic spines in the hippocampus of SAE mice. A *Sholl analysis* pattern map of neuronal morphology; B quantification of dendritic intersections of neuronal dendrites in the hippocampus among the four groups; C representative images of hippocampal neuronal tracings; D quantification of the number of neuronal branches in the hippocampus among the four groups; E quantification of the total length of neuronal branches in the hippocampus among the four groups; F representative images of the dendritic spines of hippocampal neurons; G quantification of dendritic spine density of neurons in the hippocampus among the four groups, scale bar = 10 μm. Data are presented as the mean ± SEM ($$n = 4$$ mice/group). **** $P \leq 0.0001$ vs. the indicated groups
## ICM reversed the decreased theta oscillation in the hippocampus of SAE mice
We further evaluated the changes in neural oscillations in the hippocampus of SAE mice while the mice performed NOR. In the present study, the power of theta oscillation in the CLP + vehicle group was significantly decreased compared with that in the sham + vehicle group, and this effect was reversed by ICM treatment [Fig. 7D; interaction: CLP × ICM, F [1, 20] = 9.503, $$P \leq 0.0059$$; CLP: F [1, 20] = 1.198, $P \leq 0.01$; ICM: F [1, 20] = 9.623, $P \leq 0.05$]. In addition, there were no significant differences in alpha, beta, and gamma oscillation power among the four groups [Fig. 7E; interaction: CLP × ICM, F [1, 20] = 0.9751, $$P \leq 0.3352$$; CLP: F [1, 20] = 0.06195, $$P \leq 0.1000$$; ICM: F [1, 20] = 6.120, $$P \leq 0.9527$$; Fig. 7F; interaction: CLP × ICM, F [1, 20] = 0.2811, $$P \leq 0.6018$$; CLP: F [1, 20] = 0.2811, $$P \leq 0.5929$$; ICM: F [1, 20] = 1.594, $$P \leq 0.8757$$; Fig. 7G; interaction: CLP × ICM, F [1, 20] = 0.01186, $$P \leq 0.9144$$; CLP: F [1, 20] = 0.2811, $$P \leq 0.9894$$; ICM: F [1, 20] = 1.594, $$P \leq 0.9556$$]. These results suggest that inhibiting HMGB1 reverses abnormal theta oscillation in the CA1 of the hippocampus after CLP.Fig. 7ICM reversed the abnormal theta oscillation in the hippocampus of SAE mice. A Flow chart of the electrophysiological experiment. B Representative images of local field potential and filtered theta, alpha, beta, and gamma oscillations in the hippocampus among the four groups. C Example power spectra of local field potential in the hippocampus. D–G Quantification of average theta, alpha, beta, and gamma oscillation power in the hippocampus among the four groups. Data are shown as the mean ± SEM ($$n = 3$$–5 mice/group), *$P \leq 0.05$ and **$P \leq 0.01$ vs. the indicated groups
## ICM reversed the impairment of neuronal activity and synaptic dysfunction in the hippocampus of SAE mice
Given that neural oscillations are driven by fluctuations in the excitability of populations of neurons, we further examined neuronal activity in the hippocampus of SAE mice. C-Fos protein is the product of the immediate early gene that is a marker of neuronal activation (Morgan and Curran 1986; Dragunow and Faull 1989). The number of c-Fos+ cells in the CLP + vehicle group was significantly decreased compared with that in the sham + vehicle group, and ICM treatment reversed this effect [Fig. 8A, B; interaction: CLP × ICM, F [1, 68] = 20.56, $P \leq 0.0001$; CLP: F [1, 68] = 11.45, $P \leq 0.0001$; ICM: F [1, 68] = 5.935, $P \leq 0.0001$]. Meanwhile, HFS-induced SC-CA1 LTP was significantly reduced in the hippocampus of the CLP + vehicle group compared with the sham + vehicle group, and this effect was also reversed by ICM treatment [Fig. 8C–E; interaction: CLP × ICM, F [1, 17] = 1.560, $$P \leq 0.2287$$; CLP: F [1, 17] = 7.818, $P \leq 0.01$; ICM: F [1, 17] = 16.06, $P \leq 0.05$]. These results suggest that HMGB1 impairs neuronal activity and synaptic function after CLP.Fig. 8ICM reversed the impairment of neuronal activity and synaptic dysfunction in the hippocampus of SAE mice. A Representative images of immunofluorescence staining of c-Fos (red), DAPI (blue) and colocalization in the hippocampus, scale bar = 10 μm; B quantification of the number of c-Fos+ cells in the hippocampus among the four groups. ( $$n = 3$$ mice/group). C Schematic representation of fEPSPs before [1] and after [2] HFS among the four groups. Horizontal calibration bars, 10 ms; vertical bars, 0.5 mV. D Time-dependent changes in the slope of fEPSPs before [1] and after [2] HFS in hippocampal slices among the four groups. E Quantification of fEPSP slopes evoked by HFS among the four groups. ( $$n = 5$$–6 slices/group). Data are presented as the mean ± SEM. * $P \leq 0.05$, **$P \leq 0.01$, ****$P \leq 0.0001$ vs. the indicated groups
## Discussion
In the present study, we showed that in SAE mice, HMGB1 mediates microglial activation and induces excessive phagocytosis of synapses and neuron dysfunction, ultimately leading to cognitive impairment. Notably, inhibiting HMGB1 secretion reverses these aberrant changes and ameliorates cognitive impairment in SAE mice (Fig. 9). Fig. 9The schematic diagram illustrates that CLP induces increased secretion of HMGB1 and microglial activation, thus causing abnormal synaptic elimination and decreased theta oscillation. Abnormal theta oscillation reduces c-Fos+ cells and impairs LTP, ultimately leading to cognitive impairment in SAE mice SAE is a severe CNS complication of sepsis, manifesting clinically as diffuse brain dysfunction, with varying degrees of neurological symptoms ranging from lethargy to coma, and over $80\%$ of patients demonstrate EEG abnormalities [1, 24, 25]. SAE is the most common cause of encephalopathy in medical and surgical intensive care [26], and approximately half ($46\%$) of patients with sepsis have SAE [27]. After CLP, the surviving mice in our study exhibited impaired learning and memory, as assessed by the NOR and Y maze tests. Consistent with our results, it has been suggested that sepsis induced by CLP can lead to cognitive impairment [28]. Lipopolysaccharide (LPS) injection can also cause short- and long-term cognitive impairments through a mechanism that releases various inflammatory cytokines in the brain [29]. However, the CLP model induces a slower but more stable increase in plasma cytokines that resembles human sepsis more closely than endotoxin administration. The CLP model can also cause more prolonged and consistent cognitive impairment after sepsis relative to LPS administration. Therefore, CLP is considered the gold standard model for SAE [30].
The underlying pathological mechanisms of SAE are multifactorial and highly complex. BBB damage, cerebral microcirculation dysfunction, abnormal inflammatory response, and abnormal brain metabolism may all be involved in SAE development, but the specific mechanisms are not yet known [1]. Accumulating evidence suggests that neuroinflammation triggered by abnormal inflammatory cytokine expression plays a pivotal role in SAE. It has been reported that activation of the NLRP3 inflammasome via mitophagy inhibition in cerebral microvascular endothelial cells may promote the secretion of IL-1β into the CNS and induce neuroinflammation, leading to SAE [5]. In addition, a previous study reported that double-stranded RNA dependent protein kinase could physically interact with inflammasome components and mediates inflammasome activation, thus regulating release of HMGB1 [31]. Interestingly, HMGB1 is also a late mediator of inflammation in sepsis [13] and participates in the amplification of neuroinflammation [32]. However, there are few studies on the role of HMGB1 in SAE. HMGB1 translocates from the nucleus into the cytoplasm and is eventually released into the extracellular space. The nucleus–cytoplasm transfer of HMGB1 is a critical step in the active release of HMGB1, decreasing n-HMGB1 levels [33, 34]. In our study, we demonstrated that the expression of n-HMGB1 was significantly decreased in the hippocampus after CLP, while ICM treatment inhibited HMGB1 secretion and reduced cognitive impairment. These results indicate that inhibiting HMGB1 secretion may be a critical potential mechanism for treating SAE. Consistent with our data, HMGB1 release induces cognitive deficits in diabetes-related dementia and TBI [12, 35]. In an animal model of AD, HMGB1 induced cognitive impairment through the Sirtuin 3/superoxide dismutase 2 signaling pathway [11]. However, the mechanism by which HMGB1 induces cognitive impairment in SAE remains unclear.
Microglia are the primary immune cells in the brain and are the main source of cytokines in the CNS. Microglial activation induces a change from a resting state to a highly branched state (hyperramification), an increase in the average length of the branches and an amoebic state [36]. Our study showed that CLP increased the number and average length of microglial branches with a concomitant increased expression of CD68 in the hippocampus. These results suggest that the phagocytic ability of activated microglia is enhanced in SAE mice. Inhibiting HMGB1 secretion through ICM treatment reversed the above anomalies, suggesting that HMGB1 is a crucial factor in mediating microglial activation in SAE. Numerous studies have demonstrated that microglial activation is closely associated with cognitive impairment in AD, vascular dementia, postoperative cognitive impairment, cerebral ischemia-percussion injury, and TBI [37–40]. Additionally, under pathological conditions, the enhanced phagocytic ability of long-term activated microglia can lead to excess inflammatory mediator expression, aberrant synaptic pruning, neuronal dysfunction, or even death [41]. In our study, excitatory synapses were engulfed by microglia following CLP, and this effect was rescued by ICM administration. These findings indicate that HMGB1 causes cognitive impairment by enhancing microglial phagocytic ability and inducing excitatory synaptic engulfment. However, a previous study suggested that GABA-receptive microglia selectively prune inhibitory synapses in postnatal development and impair this microglial response, leading to behavioral abnormalities such as attention-deficit/hyperactivity disorder [42]. The reasons for this discrepancy are unknown but may be due to the different animal models used.
In the CNS, neurons can process thousands of different synaptic inputs, with dendrites playing an essential role in the brain’s neural network. Dendrites can receive input from other neurons, integrating and transmitting signals to the cell body, triggering the generation and propagation of neuronal action potentials, thus transferring information and performing complex cognitive tasks [43]. Dendritic spines are spiny protrusions of dendrites and are the primary site of synaptic connections between neurons [44]. In this experiment, we examined changes in neuronal morphology and dendritic spine density of pyramidal neurons in the hippocampus by Golgi staining. We found that the neuronal morphology was impaired, and the dendritic spine density was significantly reduced after the CLP operation, suggesting that the synaptic structural integrity of the hippocampus in SAE mice was impaired. Inhibiting the secretion of HMGB1 improved the abnormal morphological changes in pyramidal neurons and the loss of dendritic spines in the hippocampus of SAE mice. Conversely, HMGB1 can promote neurite outgrowth and cell migration during early brain development and is essential for processes such as forebrain development [45]. This discrepancy is most likely due to the the different redox state of HMGB1 which resulting in activation of different HMGB1 receptors and subsequent biological outcome [46]. The release of all-thiol (all-reduced) HMGB1 potentiates chemotaxis and promotes neurite outgrowth via binding to receptor for advanced glycation end products. During inflammation, the disulfide form of HGMB1 releasing from activated macrophage binds to Toll-like receptor 4 to induce cytokine production [46–48]. In addition, a disintegrin and metalloproteinase domain 10 is involved in dendritic spine shaping in an animal model of AD, which alters dendritic morphology, induces dendritic spine loss, and impairs information transfer between neurons [49]. Dendritic spines are essential for receiving neurotransmitters and regulating synaptic plasticity, which is the basis of learning and memory [50]. Activated microglia cause dendritic spine loss and neuronal damage or death by increasing synaptic phagocytosis, which can cause various neuropsychiatric disorders [51–53]. These results suggest a complex interaction between microglia and neurons under different conditions, promoting the formation of neuronal connections and maintaining an optimal neural network.
Neural oscillations are the rhythms of neural activity observed at different temporal and spatial scales, which are classified as delta oscillations (1–4 Hz), theta oscillations (4–8 Hz), alpha oscillations (8–12 Hz), beta oscillations (12–30 Hz) and gamma oscillations (30–90 Hz) [54]. Numerous studies have shown that neural oscillations play vital roles in perceptual, cognitive, motor, and emotional processes [55]. For example, in animal models of schizophrenia, diminished gamma oscillations have been shown to lead to impaired cognitive function [56, 57]. We detected neural oscillations of the hippocampus in mice performing NOR to further investigate the neural mechanisms underlying cognitive impairment in SAE. Interestingly, our study indicated that theta oscillation power was significantly decreased after CLP, and this effect was reversed by ICM treatment. In an animal model of AD, inhibitory and rhythmic septohippocampal activity are selectively reduced, and hippocampal theta oscillation power is impaired during a cognitive task, leading to behavioral disorders in information processing [58]. In addition, several intracranial studies have suggested that increased theta power is associated with successful encoding or retrieval [59, 60]. A previous study demonstrated that the reduction in LFP cross-frequency coupling between theta and gamma power in the hippocampus is associated with impaired LTP in an animal model of transient global cerebral ischemia [61]. Consistently, our study indicated that decreased theta oscillation was accompanied by reduced activity of neurons and impaired LTP in the hippocampus of SAE mice. These results suggest that HMGB1 mediates theta oscillation disruption and network dysregulation, thus leading to cognitive deficits in SAE.
## Conclusions
Our study demonstrated that increased HMGB1 secretion induces microglial activation, which in turn induces abnormal synaptic elimination and neuronal dysfunction in the hippocampus, ultimately leading to cognitive impairments in SAE mice. Administration of ICM effectively alleviated these pathological changes and sepsis-induced cognitive impairment. Our study suggests that HMGB1 may be a target for the treatment of SAE.
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|
---
title: Bone marrow-derived dedifferentiated fat cells exhibit similar phenotype as
bone marrow mesenchymal stem cells with high osteogenic differentiation and bone
regeneration ability
authors:
- Hirokatsu Sawada
- Tomohiko Kazama
- Yuki Nagaoka
- Yoshinori Arai
- Koichiro Kano
- Hiroshi Uei
- Yasuaki Tokuhashi
- Kazuyoshi Nakanishi
- Taro Matsumoto
journal: Journal of Orthopaedic Surgery and Research
year: 2023
pmcid: PMC10007822
doi: 10.1186/s13018-023-03678-9
license: CC BY 4.0
---
# Bone marrow-derived dedifferentiated fat cells exhibit similar phenotype as bone marrow mesenchymal stem cells with high osteogenic differentiation and bone regeneration ability
## Abstract
### Background
Mesenchymal stem cells (MSCs) are known to have different differentiation potential depending on the tissue of origin. Dedifferentiated fat cells (DFATs) are MSC-like multipotent cells that can be prepared from mature adipocytes by ceiling culture method. It is still unknown whether DFATs derived from adipocytes in different tissue showed different phenotype and functional properties. In the present study, we prepared bone marrow (BM)-derived DFATs (BM-DFATs), BM-MSCs, subcutaneous (SC) adipose tissue-derived DFATs (SC-DFATs), and adipose tissue-derived stem cells (ASCs) from donor-matched tissue samples. Then, we compared their phenotypes and multilineage differentiation potential in vitro. We also evaluated in vivo bone regeneration ability of these cells using a mouse femoral fracture model.
### Methods
BM-DFATs, SC-DFATs, BM-MSCs, and ASCs were prepared from tissue samples of knee osteoarthritis patients who received total knee arthroplasty. Cell surface antigens, gene expression profile, and in vitro differentiation capacity of these cells were determined. In vivo bone regenerative ability of these cells was evaluated by micro-computed tomography imaging at 28 days after local injection of the cells with peptide hydrogel (PHG) in the femoral fracture model in severe combined immunodeficiency mice.
### Results
BM-DFATs were successfully generated at similar efficiency as SC-DFATs. Cell surface antigen and gene expression profiles of BM-DFATs were similar to those of BM-MSCs, whereas these profiles of SC-DFATs were similar to those of ASCs. In vitro differentiation analysis revealed that BM-DFATs and BM-MSCs had higher differentiation tendency toward osteoblasts and lower differentiation tendency toward adipocytes compared to SC-DFATs and ASCs. Transplantation of BM-DFATs and BM-MSCs with PHG enhanced bone mineral density at the injection sites compared to PHG alone in the mouse femoral fracture model.
### Conclusions
We showed that phenotypic characteristics of BM-DFATs were similar to those of BM-MSCs. BM-DFATs exhibited higher osteogenic differentiation potential and bone regenerative ability compared to SC-DFATs and ASCs. These results suggest that BM-DFATs may be suitable sources of cell-based therapies for patients with nonunion bone fracture.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13018-023-03678-9.
## Background
The incidence of osteoporotic fractures increases in the elderly, mirroring their age-related decrease in bone mineral density (BMD). Autologous bone grafts have been frequently used to promote bone fusion in patients with osteoporotic fractures. However, there are problems with autologous bone grafting such as persistent pain at the donor site, infection of bone, and nerve injury that are associated with the bone harvesting surgical procedure [1].
Currently, the clinical utility of stem cell-based therapy has been shown to overcome the limitations of autologous bone grafting. Mesenchymal stem cells (MSCs) are considered to be an attractive cell source for bone tissue engineering. MSCs have the ability to self-renew and differentiate into various cell types such as adipocytes, chondrocytes, and osteoblasts [2]. The osteogenic potential of MSCs has already been applied in clinical situations such as fracture nonunion, osteogenesis imperfecta, and osteoarthritis [3, 4]. MSCs were originally isolated from bone marrow, but they also can be isolated from other connective tissues such as adipose tissue, umbilical cord, and placenta. MSCs isolated from adipose tissue are referred to as adipose tissue-derived stem cells (ASCs). ASCs have several advantages for clinical application compared to bone marrow MSCs, such as relatively larger stem cell population in the tissue and less invasiveness during collection. MSCs are known to have different differentiation potential depending on the tissue of origin. A previous study using donor-matched MSCs found that bone marrow MSCs showed higher osteogenic and chondrogenic capacity but less adipogenic capacity compared to ASCs [5, 6]. The number of MSCs in tissue and their proliferative ability is reduced according to their donor’s age [7–10]. In addition, MSCs isolated from osteoporosis patients exhibit low proliferative activity and low osteogenic differentiation ability [11, 12]. Therefore, an alternative cell source that can be easily isolated and expanded to adequate amounts for transplantation, especially in elderly subjects and osteoporosis patients, is still required.
Dedifferentiated fat cells (DFATs) are MSC-like multipotent cells that can be prepared from mature adipocytes by ceiling culture method. Our research group reported that DFATs have the potential to differentiate into multiple lineages including adipogenic, osteogenic, chondrogenic, muscular, and neurogenic lineages [13–16], and transplantation of DFATs showed therapeutic effects in a variety of animal models for human diseases [17–22]. Comparative analysis of DFATs with induced pluripotent stem cells using DNA microarray has shown that DFATs exhibit gene expression profiles more similar to mesenchymal progenitor cells than embryonic stem cells [23]. Because DFATs can be prepared from smaller amounts of adipose tissue with higher purity compared to ASCs [13, 24], the cells are thought to be well suited for cell-based therapy for a variety of diseases, including osteochondral diseases. It has not yet fully elucidated whether DFATs derived from adipocytes in different adipose tissue showed different phenotype and functional properties.
In the present study, we prepared bone marrow-derived DFATs (BM-DFATs) and MSCs (BM-MSCs), subcutaneous adipose tissue-derived DFATs (SC-DFATs) and ASCs from donor-matched tissue samples. We then compared their phenotypes and multilineage differentiation capacities in vitro. Furthermore, we evaluated the in vivo bone regeneration potential of these cells using a severe combined immunodeficiency (SCID) mouse femoral fracture model.
## Preparation of BM-DFATs, SC-DFATs, BM-MSCs, and ASCs
Femur bone marrow and subcutaneous adipose tissue were provided by knee osteoarthritis patients who had undergone total knee arthroplasty at Itabashi Hospital, Nihon University School of Medicine, Tokyo, Japan ($$n = 9$$, average age 62.2 ± 15.0 years). Patients with idiopathic osteoarthritis of the knee of grade 3 or 4 according to Kellgren–*Lawrence criteria* were included. Patients with aggressive synovitis were excluded. Informed consent was given before surgery, and all experiments were conducted with the approval of the Nihon University Clinical Research Review Board. Preparation of SC-DFATs was performed according to a previous report by Matsumoto et al. [ 13]. Briefly, approximately 1 g of adipose tissue was cut into small pieces and digested with $0.1\%$ (weight/volume) collagenase type I solution (Koken, Tokyo, Japan) at 37 °C for 1 h with gentle agitation. After filtration, the floating top layer containing mature adipocytes was collected by centrifugation at 135 g for 3 min. After washing with phosphate-buffered saline (PBS), the cells (5 × 104) were placed in 12.5-cm2 culture flasks (NUNC, Roskilde, Denmark) filled completely with Dulbecco’s modified Eagle’s medium (DMEM, Invitrogen, Carlsbad, CA) containing $20\%$ fetal bovine serum (FBS, JRH Biosciences, Lenexa, KS) and incubated at 37 °C in $5\%$ CO2. Mature adipocytes floated up and adhered to the top inner ceiling surface of the flask (Ceiling culture). After 7 days, the medium was removed and the flasks were inverted so that the cells were on the bottom. The medium was changed every 3 or 4 days until the cells reached confluency. For passage, the cells were harvested by treating the cells with a trypsin-ethylenediaminetetraacetic acid solution (Invitrogen), following which the cells were seeded in 100-mm dishes at a density of 1 × 106 cells per dish and cultured. ASCs were prepared according to the preparation method of Zuk et al. [ 25]. Briefly, approximately 1 g of the adipose tissue was treated with collagenase and centrifuged, and then, the sedimented stromal vascular fraction cells were seeded at a density of 1 × 105 cells/cm2 and cultured in DMEM containing $10\%$ FBS. For the preparation of BM-DFATs, isolation of mature adipocytes from bone marrow was performed according to the method described previously [26] with slight modification. Briefly, approximately 5 ml of bone marrow fluid was aspirated with a soft cannula in the femoral distal diaphysis and digested with collagenase type I (Koken) at 37 °C for 30 min. After filtration, the floating top layer containing mature adipocytes was collected by centrifugation at 135 g for 3 min. After washing with PBS, the cells were incubated by the ceiling culture method as described above. BM-MSCs were prepared by the method described previously [27]. Briefly, approximately 5 ml of the bone marrow fluid was centrifuged, and the precipitate fraction cells were seeded at a density of 3 × 104 cells/cm2 and cultured in DMEM containing $10\%$ FBS. These four cell types were used for experiments within passage 3. The population doubling time was determined at each passage by the formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Population}}\;{\text{doubling}}\;{\text{time}} = \ln 2/\left[{\ln \left({N/N_{0} } \right)/t} \right]$$\end{document}Populationdoublingtime=ln2/lnN/N0/twhere N is the cell number at harvest, N0 is the cell number at seeding, and t is the culture period in hours.
## Flow cytometry
The immunophenotypes of the BM-DFATs, SC-DFATs, BM-MSCs, ASCs at passage 2 were identified using flow cytometry as previously described [13]. The cells grown to $60\%$ confluence were suspended at a density of 5 × 105 cells per tube and incubated with various anti-human antibodies conjugated with phycoerythrin (PE) or allophycocyanin (APC). The following antibodies were used: anti-CD73-PE, anti-CD90-APC, anti-CD105-PE, anti-CD31-PE, anti-CD45-APC, anti-HLA-DR-PE, anti-CD106-PE, anti-CD54-APC, and anti-CD36-PE (all from BD Biosciences, San Jose, CA). Mouse IgG1-PE, mouse IgG1-APC, mouse IgG2a-PE, mouse IgG2b-APC, and mouse IgM-PE (all from BD Biosciences) were used as negative controls. The fluorescence intensity of the cells was evaluated by a FACSAria flow cytometer (Becton Dickinson, Bedford, NJ), and data were analyzed using FlowJo software (version 10.6.1, FlowJo, Ashland, OR). Positive cells were counted and compared with the signal of corresponding immunoglobulin isotypes. A minimum of 1 × 104 events were recorded for each sample, and analysis was performed at least three separate times for each condition tested.
## DNA microarray
Total RNA was extracted from SC-DFATs, BM-DFATs, ASCs, and BM-MSCs at passage 0 and 1 using an RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Briefly, 1 × 107 cells were lysed and passed through a prefilter to remove DNA, adjusted with an equal volume of $70\%$ ethanol and applied to the RNA column. After washing, RNA was eluted from the column with 100 µl of water. The quality of the extracted RNA was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Adequate RNA quality was identified with two clear ribosomal peaks (28S and 18S) and low extraneous noise. Labeling of total RNA was performed using the GeneChip™ 3′IVT PLUS Reagent Kit (Affymetrix, Santa Clara, CA) following the manufacturer’s protocol. Briefly, first- and second-strand cDNA were synthesized from 100 ng of total RNA from each cell sample, and in vitro transcription was performed using biotinylated ribonucleotide analogs to generate aRNA. The labeled aRNA was purified from the mixture and measured and fragmented with the GeneChip™ Hybridization, Wash, and Stain Kit (Thermo Fisher Scientific, Waltham, MA). The RNA samples were hybridized to probes using GeneChip™ Human Genome U133 Plus 2.0 Array (Affymetrix) according to the manufacturer’s instructions. Fluorescent images were visualized using a GeneChip Scanner 3000 (Affymetrix). Gene expression data were analyzed using Transcriptome Analysis Console software (version 4.0, Affymetrix) following the software guidelines. An adjusted p-value < 0.05 and log-FC ≥ ± 2.0 were set as the cut-off criteria to screen differentially expressed genes (DEGs).
## In vitro differentiation assay
The adipogenic and osteogenic differentiation assay was performed as described previously [13]. Briefly, 5 × 104 cells at passage 3 were seeded on 30-mm dishes (BD Falcon, Franklin Lakes, NJ) and cultured in DMEM containing $10\%$ FBS until reaching confluence. For adipogenic differentiation, the cells were cultured in DMEM containing $10\%$ FBS, 1 µM dexamethasone (Sigma-Aldrich, St. Louis, MO), 0.5 mM isobutylmethylxanthine (Sigma-Aldrich), and 1 × insulin-transferrin-selenium-X (ITS; Invitrogen) for one week. The culture medium was changed every three days. After fixing the cells with $4\%$ paraformaldehyde (Wako, Osaka, Japan), they were stained with Oil red O (Sigma-Aldrich) for 15 min. For osteogenic differentiation, the cells were cultured in DMEM containing $10\%$ FBS, 100 nM dexamethasone, 10 mM β-glycerophosphate (Sigma-Aldrich), and 0.05 mM L-ascorbic acid (Sigma-Aldrich) for one week. The culture medium was changed every three days. After fixing the cells with $4\%$ paraformaldehyde, the cells were incubated at 37 °C for 1 h with $0.16\%$ naphthol AS-TR phosphate (Sigma-Aldrich) and $0.8\%$ Fast Blue BB (Wako) dissolved in 0.1 M Tris buffer (pH 9.0) for detection of alkaline phosphatase (ALP) activity. The cells were also stained with $1\%$ alizarin red S (Sigma-Aldrich) for 3 min at room temperature. The samples were observed under a BX51 microscope (Olympus, Tokyo, Japan).
## Real-time reverse transcription-polymerase chain reaction (RT-PCR)
The mRNA expressions of cells were analyzed by real-time RT-PCR using TaqMan™ gene expression assay. Total mRNA was extracted from cells at passage 3 using an RNeasy Mini Kit, and 1 µg total RNA was reverse-transcribed using a High-Capacity cDNA Reverse Transcription Kit (Life Technologies) according to the manufacturer’s instructions. Subsequently, 5 ng cDNA was analyzed by real-time RT-PCR using TaqMan™ Fast Advanced Master Mix (Applied Biosystems, Foster City, CA) and a StepOnePlus Real-Time PCR System (Applied Biosystems). TaqMan™ probes (Life Technologies) for specific genes were as follows: PPARG (PPARγ), Hs001115513_m1; RUNX2, Hs00231692_m1; CEBPA (C/EBPα), Hs00269972_s1; ALPL (Alkaline phosphatase), Hs01029144_m1; SLC2A4 (GLUT4), Hs00168966_m1; BGLAP (Osteocalcin), Hs00168966_m1. Expression level of transcripts was normalized to endogenous human 18S ribosomal RNA (4319413E) mRNA levels according to the formulae comparative Ct. Each sample was analyzed in triplicate.
## Laboratory animals
Male SCID mice were purchased from Oriental Yeast Co., Ltd., Tokyo, Japan. The mice were bred in cages maintained in an optimal environment without restriction on eating and drinking. The animal experiments were performed with the approval of the Animal Experiment Committee of Nihon University School of Medicine. Animal breeding and experiments were conducted in accordance with the Animal Experiment Guideline of Nihon University School of Medicine.
## Mouse femoral fracture model
The mouse femoral fracture model was created according to the report by Bonnarens and Einhorn [28]. Under inhalation anesthesia with isoflurane, a left transverse femoral fracture was created at 10-mm distance from the knee joint using a micro-bone saw (Zimmer Biomet, Warsaw, IN). Then, 50 μl of peptide hydrogel (PHG) Pura Matrix™ (3-D Matrix, Tokyo, Japan) was injected locally into the fracture gap. PHG was prepared according to the manufacturer’s manual. A 25-G injection needle (Terumo, Tokyo, Japan) was inserted intramedullary from the distal femur to fix the bone fracture.
## Time course experiment in the mouse femoral fracture model
Male 8-week-old SCID mice ($$n = 6$$) were used for the experiment. At 4, 6, and 8 weeks after the femoral fracture, mice were euthanized and both femurs were removed. Micro-computed tomography (CT) images of both femurs were obtained with an R mCT system (Rigaku Co., Ltd., Tokyo, Japan) at 90 kV/100 μA to evaluate the morphological changes of the fracture sites.
## Cell transplantation experiment in the mouse femoral fracture model
Male 8-week-old SCID mice ($$n = 50$$) were divided into five groups, BM-DFATs group, SC-DFATs group, BM-MSCs group, ASCs group, and Control group, and the fracture model was created for the left femur using the above method ($$n = 10$$ in each group). After mixing 1 × 105 BM-DFATs, SC-DFATs, BM-MSCs, and ASCs (passage 2) with 50 μl of PHG, the solutions were immediately injected into the fracture gap. In the control group, only 50 μl of PHG was injected into the fracture gap. Four weeks after model creation, all mice were euthanized, and bilateral femurs were removed. Micro-CT images of both femurs were taken to evaluate the effects of transplantation of each cell type on fracture healing. Mice that had an oblique fracture when creating the fracture model were excluded (ASCs group: $$n = 1$$, Control group: $$n = 1$$).
## Bone structure analysis
Based on the micro-CT images, bone structure analysis was performed using image analysis software i-viewR (MORITA, Kyoto, Japan). Bone volume (BV) of the femurs was measured in a 4 × 4 × 4 mm3 area at the center of the fracture, and BMD was calculated.
## Statistical analysis
All data are expressed as mean ± standard error (SE). For comparison between groups, a test of significant difference was performed by one-way ANOVA and Tukey–Kramer multiple comparison test. A value of $p \leq 0.05$ was considered as statistically significant. GraphPad Prism Ver5.0 (GraphPad Software, La Jolla, CA) was used for statistical analysis.
## Phenotypic characteristics of human BM-DFATs, SC-DFATs, BM-MSCs, and ASCs
We first prepared BM-DFATs, SC-DFATs, BM-MSCs, and ASCs from patients with osteoarthritis and examined the phenotypic characteristics of these cell types. Schematic illustration of the preparation methods for these four cell types is shown in Fig. 1A. We confirmed that DFATs could be successfully prepared from mature adipocytes isolated from bone marrow aspirates as well as subcutaneous adipose tissue at day 13 of the ceiling culture, although the size of the mature adipocytes in bone marrow aspirates was much smaller than that in the subcutaneous adipose tissue (Fig. 1B). Morphological analysis revealed that the BM-DFATs, SC-DFATs, BM-MSCs, and ASCs cells exhibited similar spindle-shaped fibroblast-like morphology (Fig. 1C). These four cell types could be prepared reproducibly from all 9 donors examined. The cell growth rate did not differ between the 4 cell types, and the population doubling time was approximately 65 h. These cells could be subcultured for over 8 passages. Flow cytometric analysis showed that the BM-DFATs, SC-DFATs, BM-MSCs, and ASCs expressed MSC markers CD73, CD90, and CD105 at similar levels (Fig. 2). These cells did not express lymphocyte marker CD45, endothelial cell marker CD31, and immunogenic marker HLA-DR, which are known as negative markers for MSCs. HLA-DR+ cells were undetected in BM-DFATs, SC-DFATs, and ASCs, whereas they were slightly ($0.81\%$) detected in BM-MSCs. The expression of CD106, known as vascular cell adhesion molecule-1 (VCAM-1), was detected in BM-DFATs ($15.4\%$) and BM-MSCs ($35.5\%$), whereas it was almost non-detectable in SC-DFATs ($0.79\%$) and ASCs ($0.015\%$). The expression of CD54, known as intracellular adhesion molecule 1 (ICAM-1), was frequently observed in SC-DFATs ($94.4\%$) and ASCs ($88.6\%$) compared to that in BM-DFATs ($66.4\%$) and BM-MSCs ($59.6\%$). Similarly, the expression frequency of CD36 in SC-DFATs ($12.4\%$) and ASCs ($8.02\%$) was higher than that in BM-DFATs ($3.88\%$) and BM-MSCs ($0.83\%$). These findings indicate that the BM-DFATs, SC-DFATs, BM-MSCs, and ASCs showed similar morphology and immunophenotype corresponding to the MSC definition, although their immunophenotypes are slightly different in their derived tissue-specific manner. Fig. 1Preparation and morphological analysis of subcutaneous adipose tissue-derived dedifferentiated fat cells (SC-DFATs), adipose tissue-derived stem cells (ASCs), bone marrow-derived dedifferentiated fat cells (BM-DFATs), and bone marrow mesenchymal stem cells (BM-MSCs). A Schematic illustration of preparation methods for SC-DFATs, ASCs, BM-DFATs, and BM-MSCs. B Representative photomicrographs of adipocytes isolated from subcutaneous adipose tissue and bone marrow at days 4 and 13 after the ceiling culture. Scale bars represent 100 µm. C Representative cellular morphology of BM-DFATs, SC-DFATs, BM-MSCs, and ASCs. Scale bars represent 100 μm. TKA: total knee arthroplasty, SVF: stromal vascular fractionFig. 2Flow cytometric analysis of SC-DFATs, ASCs, BM-DFATs, and BM-MSCs. Cell surface antigens profiles of SC-DFATs, ASCs, BM-DFATs, and BM-MSCs were determined by flow cytometric analysis. Data are representative of at least three experiments
## Gene expression profiles of BM-DFATs, SC-DFATs, BM-MSCs, and ASCs
We next examined the gene expression profiles of the four cell types prepared from 4 donors using microarray analysis. Principal component analysis revealed that DEGs in SC-derived cells (ASCs and SC-DFATs) were clearly separated from those in BM-derived cells (BM-MSCs and BM-DFATs) (Fig. 3A). Scatter plot analysis confirmed similar expression levels of most genes between BM-DFATs and BM-MSCs ($99.6\%$ identical) and between SC-DFATs and ASCs ($99.49\%$ identical) (Fig. 3B). Heatmap analysis showed that gene expression profiles in SC-DFATs and ASCs were grouped in a same cluster and clearly separated from those in BM-DFATs and BM-MSCs, regardless of their passage numbers (Fig. 3C). The significantly up-regulated and down-regulated DEGs in BM-DFATs compared with SC-DFATs are listed in Additional file 1: Table S1, with fold change ranging from − 10 to 10. These findings suggest that there are derived tissue-associated differences in gene expression in DFATs and MSCs. Fig. 3Microarray analysis of SC-DFATs, ASCs, BM-DFATs, and BM-MSCs. Microarray analysis was performed to show differentially expressed genes between SC-DFATs, ASCs, BM-DFATs, and BM-MSCs. A Principal component analysis (PCA). B Scatter plot analysis. C Heat map analysis
## In vitro differentiation ability in BM-DFATs, SC-DFATs, BM-MSCs, and ASCs
We next performed in vitro differentiation assays to clarify the differences of multilineage differentiation ability in these cell types prepared from 4 donors. In the adipogenic differentiation culture, we found that Oil red O-positive lipid-filled adipocytes were observed in the four cell types, although the degrees of lipid droplet deposition in SC-DFATs and ASCs were greater than those in BM-DFATs and BM-MSCs (Fig. 4). In the osteogenic differentiation culture, we found that ALP activity and calcium deposition detected by alizarin red S staining were observed in the four cell types. The intensities of ALP staining in BM-DFATs and BM-MSCs tended to be higher than those in SC-DFATs and ASCs. The alizarin red S staining revealed that larger and thicker calcium deposition was observed in BM-DFATs and BM-MSCs compared to SC-DFATs and ASCs. Similar results were obtained from samples of three other donors. Fig. 4Comparison of in vitro differentiation ability in SC-DFATs, ASCs, BM-DFATs, and BM-MSCs. Representative photomicrographs of Oil red O, alkaline phosphatase (ALP), and alizarin red S staining in SC-DFATs, ASCs, BM-DFATs, and BM-MSCs after 1 week of adipogenic or osteogenic differentiation culture. Scale bars represent 200 μm To support these findings, expression of adipogenic marker genes such as PPARG (PPARγ), CEBPA (C/EBPα), and SLC2A4 (GLUT4) in SC-DFATs and ASCs were significantly ($p \leq 0.05$) higher than those in BM-DFATs and BM-MSCs under adipogenic differentiation culture condition (Fig. 5A–C). In contrast, expressions of osteogenic marker genes such as RUNX2, ALPL (Alkaline phosphatase), and BGLAP (Osteocalcin) in BM-DFATs and BM-MSCs were significantly ($p \leq 0.05$) higher than those in SC-DFATs and ASCs under the osteogenic differentiation culture condition (Fig. 5D–F). These results indicated that BM-DFATs and BM-MSCs have higher osteogenic differentiation capacity and lower adipogenic differentiation capacity in vitro compared to SC-DFATs and ASCs. Fig. 5Comparison of gene expression changes in SC-DFATs, ASCs, BM-DFATs, and BM-MSCs. SC-DFATs, ASCs, BM-DFATs, and BM-MSCs were cultured in osteogenic or adipogenic differentiation medium for 2 weeks. Total RNA was extracted at indicated time periods, and real-time RT-PCR analysis was performed. A-C Expression of adipogenic marker genes after adipogenic differentiation culture. Expressions of PPARG (A), CEBPA (B), and SLC2A4 (C) were evaluated. D–F Expression of osteogenic marker genes after osteogenic differentiation culture. Expressions of RUNX2 (D), ALPL (E), and BGLAP (F) were evaluated. * $p \leq 0.05$ (one-way ANOVA, Tukey’s multiple comparison test)
## The bone regenerative effect of BM-DFAT, SC-DFAT, BM-MSC, and ASC transplantation in the mouse femoral fracture model
To determine the optimal cell source for bone fracture healing, we next performed in vivo cell transplantation experiments using the mouse femoral fracture model. In this experiment, we used a femoral fracture model in which natural fusion and repair occur after creation of transverse cut in the femur [28]. We first examined time-course changes in bone structure after PHG injection into the fracture gap in this model. PHG is a self-assemble synthetic peptide that acts as a surrogate extracellular matrix to improve tissue regeneration in diverse tissue including bone [29]. The micro-CT images revealed that bone union and remarkable bony callus formation were observed at 4 weeks after the fracture (Fig. 6A). After that, the bony callus was gradually resorbed at 6 and 8 weeks. The bone structure analysis showed that BV at the center of the fracture site, a parameter of bony callus formation, was significantly ($p \leq 0.001$) increased by over twofold at 4 weeks of treatment compared to pretreatment (Fig. 6B). Then, the BV level gradually decreased at 6 and 8 weeks. The BMD at the fracture callus was significantly ($p \leq 0.001$) decreased at 4 weeks after the fracture compared to pretreatment followed by a gradual increase in the level with time (Fig. 6C). These results indicated that bone union and bony callus formation occurred by 4 weeks after the fracture, and bone remodeling began after that in this fracture model. Fig. 6Time-course changes of bone microarchitecture at fracture sites in a mouse femoral fracture model. A Representative micro-computed tomography images in coronal and axial views of fractured femurs are shown. The bone volume (BV) (B) and the bone mineral density (BMD) (C) of the fracture sites were quantified before (Pre) and at 4, 6, and 8 weeks after the femoral fracture. Bars indicate mean ± SE. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ (one-way ANOVA, Tukey–Kramer multiple comparison test) To investigate in vivo bone regenerative ability of the BM-DFATs, SC-DFATs, BM-MSCs, and ASCs, we next transplanted these four cell types prepared from a 48-year-old donor with PHG in the femoral fracture site and evaluated BV and BMD at 28 days after treatment. The results showed that bone union and bony callus formation were observed in all mice in each group (Fig. 7A). The axial images at the center of the fracture site revealed that new cortical bone thickness and cancellous BMD in the BM-DFATs and BM-MSCs groups tended to be greater compared to those in the control group (PHG alone). Bone structure analysis revealed that the BV in the transplantation groups of all four cell types tended to be lower compared to that in the control group (Fig. 7B). In particular, the BV levels in the BM-MSCs group were significantly ($p \leq 0.05$) lower than those in the control group. The BMD values in the transplantation groups of all four cell types tended to be higher compared to that in the control group (Fig. 7C), and those in the BM-DFATs group and BM-MSCs group were significantly ($p \leq 0.05$) higher. These results suggested that transplantation of BM-DFATs and BM-MSCs with PHG enhanced BMD at the injection sites compared to PHG alone in the mouse femoral fracture model. Fig. 7Comparison of bone regenerative effects of transplantation of SC-DFATs, ASCs, BM-DFATs, and BM-MSCs on bone volume (BV) and bone mineral density (BMD) at fracture sites. A Representative micro-computed tomography images of fracture sites in each group. B Comparison of BV at fracture sites between the 5 groups at 4 weeks after treatment. C Comparison of BMD at fracture sites between the 5 groups at 4 weeks after treatment. Bars indicate mean ± SE. * $p \leq 0.05$, **$p \leq 0.01$ (one-way ANOVA, Tukey–Kramer multiple comparison test)
## Discussion
In this study, we showed that phenotypic features of BM-DFATs are similar to those of BM-MSCs. In addition, the osteogenic differentiation potential and bone regenerative ability of BM-DFATs were greater than those of SC-DFATs and ASCs and were equivalent to BM-MSCs. To our knowledge, the present study is the first to show the phenotypic and functional differences between SC-DFATs and BM-DFATs. We successfully generated BM-DFATs from mature adipocytes in bone marrow aspirates by the conventional ceiling culture method. Although the size of the mature adipocytes in the bone marrow aspirates was much smaller than that in subcutaneous adipose tissue, the BM-DFATs generated showed similar fibroblast-like morphology and immunophenotype similar to SC-DFATs, which is consistent with the definition of MSCs [30]. In the support of a previous study [31], our data confirmed that BM-MSCs expressed higher amounts of CD106 (VCAM-1) and lower amounts of CD54 (ICAM-1) and CD36 compared to ASCs. Interestingly, the expression profiles of these markers in BM-DFATs and SC-DFATs were similar to those in BM-MSCs and ASCs, respectively. In addition, our microarray analysis revealed that the global gene expression profile of BM-DFATs is similar to that of BM-MSCs, whereas the gene expression profile of SC-DFATs is similar to that of ASCs. These findings suggest that there are differences in phenotypic characteristics between DFATs and MSCs that are related to their tissues of origin. Pizzute et al. [ 32] showed that there are site-dependent phenotypic and functional differences in MSCs. For example, MSCs isolated from bone marrow, adipose tissue, synovial tissue, and muscle tissue tend to differentiate into bone, fat, cartilage, and muscle, respectively.
Our results showed that BM-DFATs had higher osteogenic differentiation potential than SC-DFATs and ASCs. Interestingly, Tsurumachi et al. [ 33] reported that DFATs prepared from buccal fat pads have higher bone differentiation ability than SC-DFATs. Furthermore, we recently found that DFATs prepared from infrapatellar fat pads have a higher chondrogenic differentiation potential than SC-DFATs prepared from donor-matched samples [34]. These findings suggest that the differentiation tendency of DFATs differs among various fat depots in which the mature adipocytes originated. Notably, DFATs tended to differentiate into the neighboring mesenchymal tissues where their fat depots resided. To support this, Lee et al. [ 35] reported that adipogenic progenitors localized in different fat depots are intrinsically different, and they may be programmed through epigenetic modulation early during their development. Further studies are needed to explore the mechanisms that give rise to their fat depot-specific phenotypic and functional differences.
BM-MSCs are expected to use tissue engineering and regenerative medicine for patients with refractory bone fracture, and several clinical trials using BM-MSCs are ongoing [36]. MSCs have a low immunogenicity and are widely used not only autologous transplantation but also for allogenic transplantation. However, recent studies showed that allogenic MSCs have limited long-term benefits due to major histocompatibility complex class II upregulation after transplantation, which increases immunogenicity [37, 38]. In the cell replacement therapy for musculoskeletal disorders including refractory bone fracture, autologous MSCs may be more advantageous than allogenic MSCs because they can provide long-term viable cells that can be integrated into patient tissues that are no longer able to repair themselves [39]. However, it is known that the number of BM-MSCs decreases in accordance with a donor’s age and underlying condition such as osteoporosis [40, 41]. In contrast, it is possible to take a sufficient number of mature adipocytes to prepare DFATs by using bone marrow removed during arthroplasty, even in elderly patients. In addition, we found that the proliferative and multilineage differentiation ability of DFATs is not affected by donor age and underlying diseases [13]. These characteristics suggest that BM-DFATs may be a potential cell source for autologous cell therapy in patients with refractory bone fracture that occurs mainly in the elderly.
There are several limitations in this study. First, we studied only cells derived from osteoarthritis patients. It would be desirable to prepare the four cell types from bone marrow and fat of healthy subjects and compare them with cells derived from patients with osteochondral diseases. Second, we did not examine therapeutic effects of each cell type in the femoral fracture model. Further studies such as bone mechanical testing are needed. Third, we evaluated the bone structure change over a short period of time (4 weeks) because the femoral fracture model used in this study naturally recovers within 8 weeks. Long-term effects of cell transplantation should be evaluated using intractable bone fracture model animals.
## Conclusion
The phenotypic characteristics of BM-DFATs were similar to those of BM-MSCs but different from those of SC-DFATs and ASCs. The osteogenic differentiation potential of BM-DFATs was higher than that of SC-DFATs and ASCs and was equivalent to that of BM-MSCs. Transplantation of BM-DFATs and BM-MSCs with PHG increased BMD compared to PHG alone in the mouse femoral fracture model. These results suggest that BM-DFATs may be suitable sources of cell-based therapies for patients with nonunion bone fracture, similar to BM-MSCs.
## Supplementary Information
Additional file 1: Table S1. Identification of the up- and down-regulated differentially expressed genes in BM-DFATs compared with SC-DFATs.
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|
---
title: A pilot study of the association between maternal mid-pregnancy cholesterol
and oxysterol concentrations and labor duration
authors:
- Todd C. Rideout
- Jaclyn Wallace
- Xiaozhong Wen
- Vanessa M. Barnabei
- Kai Ling Kong
- Richard W. Browne
journal: Lipids in Health and Disease
year: 2023
pmcid: PMC10007829
doi: 10.1186/s12944-023-01800-8
license: CC BY 4.0
---
# A pilot study of the association between maternal mid-pregnancy cholesterol and oxysterol concentrations and labor duration
## Abstract
### Background
Previous animal model studies have highlighted a role for cholesterol and its oxidized derivatives (oxysterols) in uterine contractile activity, however, a lipotoxic state associated with hypercholesterolemia may contribute to labor dystocia. Therefore, we investigated if maternal mid-pregnancy cholesterol and oxysterol concentrations were associated with labor duration in a human pregnancy cohort.
### Methods
We conducted a secondary analysis of serum samples and birth outcome data from healthy pregnant women ($$n = 25$$) with mid-pregnancy fasting serum samples collected at 22–28 weeks of gestation. Serum was analyzed for total-C, HDL-C, and LDL-C by direct automated enzymatic assay and oxysterol profile including 7α-hydroxycholesterol (7αOHC), 7β-hydroxycholesterol (7βOHC), 24-hydroxycholesterol (24OHC), 25-hydroxycholesterol (25OHC), 27-hydroxycholesterol (27OHC), and 7-ketocholesterol (7KC) by liquid chromatography-selected ion monitoring-stable isotope dilution-atmospheric pressure chemical ionization-mass spectroscopy. Associations between maternal second trimester lipids and labor duration (minutes) were assessed using multivariable linear regression adjusting for maternal nulliparity and age.
### Results
An increase in labor duration was observed for every 1-unit increment in serum 24OHC (0.96 min [0.36,1.56], $p \leq 0.01$), 25OHC (7.02 min [1.92,12.24], $$p \leq 0.01$$), 27OHC (0.54 min [0.06, 1.08], $p \leq 0.05$), 7KC (8.04 min [2.7,13.5], $p \leq 0.01$), and total oxysterols (0.42 min [0.18,0.06], $p \leq 0.01$]. No significant associations between labor duration and serum total-C, LDL-C, or HDL-C were observed.
### Conclusions
In this cohort, mid-pregnancy concentrations of maternal oxysterols (24OHC, 25OHC, 27OHC, and 7KC) were positively associated with labor duration. Given the small population and use of self-reported labor duration, subsequent studies are required for confirmation.
## Background
An increase in maternal lipids during pregnancy is recognized as a normal physiological response to pregnancy, with large increases in triglycerides (~ 200–$400\%$), total-cholesterol (~ 25–$50\%$), and low-density lipoprotein (LDL-C, ~ $70\%$) beginning in early pregnancy and peaking in the third trimester [1, 2]. However, recent work has highlighted concerns that an abnormal dyslipidemic profile during pregnancy may have negative health implications for both the mother and fetus [3, 4]. We previously reported that total particle concentrations of very low-density lipoprotein/chylomicron remnants (VLDL/CM) and high-density lipoproteins (HDL) in mid-pregnancy have divergent associations with birth weight [5]. In this brief report, we extend our focus to examine the association between maternal pregnancy cholesterol and oxysterol concentrations and labor duration.
Labor dystocia, defined as an abnormally slow or protracted labor [6], increases the risk of both maternal and neonatal complications [7, 8] and is a primary indication for cesarean delivery [6]. Predictive maternal biomarkers of a labor dystocia phenotype may be useful in identifying at-risk pregnancies and initiating early monitoring and prevention strategies. A potential link between lipotoxicity and labor dystocia has been suggested by previous human and animal model studies demonstrating a critical role for cholesterol in successful parturition but impaired uterine function and contractile activity in hypercholesterolemia states [9–12].
Several regulatory proteins controlling myometrial contraction, including the oxytocin receptor (OXTR) and human ether-a-go-go-related gene (hERG) potassium channel proteins [13] are localized in cholesterol-rich microdomains (lipids rafts and caveolae) and are therefore sensitive to local and circulating cholesterol concentrations [10, 14]. The OXTR expresses a cholesterol binding site which is important for receptor stabilization and posttranslational processing [15–17]. Further, oxysterols, a class of highly reactive oxygenated derivates of cholesterol that induce inflammation have been shown to function as nuclear receptor ligands to regulate intracellular cholesterol concentration in the mouse uterus [18]. However, despite the known function of cholesterol and its oxidative derivatives in regulating uterine contractile activity, we are not aware of any previous human cohorts that have examined if associations exist between maternal sterol status during pregnancy and labor duration. Therefore, in this pilot study, we sought to examine if maternal mid-pregnancy cholesterol and oxysterol concentrations were associated with labor duration.
## Participants
We undertook a secondary analysis of serum samples and birth outcome data collected as part of a previous pregnancy pilot cohort study. All procedures were approved by the Institutional Review Board at the University at Buffalo (STUDY00001381). Details on the study design and cohort have previously been published [5]. Briefly, the cohort consisted of 25 adult pregnant women aged 18–35 years with singleton gestations recruited within the greater Buffalo, New *York area* between 2018 and 2019 through flyer distribution, social media posts, and local urban obstetrics clinics (Table 1). Women were excluded if they had a history of major chronic diseases (e.g., heart disease, diabetes) or were currently taking medications that affected appetite or insulin sensitivity. None of the mothers included in this cohort had premature infants or cesarean deliveries. Table 1Characteristics of mothers in the pilot cohortVariable ($$n = 25$$)Mean (SD)Median (IQ)n (%)RangeAge (yrs)30.7 (3.96)21.6–37.8Race White23 [92] African American1 (4.0) Asian1 [4]Education Level (yrs)16.1 (2.1) College graduate23 [92]Pre-Pregnancy BMI (kg/m2)a Normal10 [40] Overweight7 [28] Obese8 [32]Nulliparous16 [64]Length of active labor (hrs)7.0 (9.0)0.67–30Labor Labor induction8 [32] Use of pain medicationb19 [76]Gestational agec39 (1.0)32–41Birth weight (g)3543 [333]Mid-Pregnancy Serum Lipids (mg/dL) Total-C250.6 (49.3)131–374 LDL-C141.7 (41.1)40–250 HDL-C78.9 (20.8)33–117 Triglycerides172.8 (61.4)87–398Mid-Pregnancy Serum Oxysterols (ng/mL)d 24-hydroxycholesterol (24OHC)84.9 (29.3)(42.8–146.9) 25-hydroxycholesterol (25OHC)18.4 (3.5)(10.7–27.0) 27-hydroxycholesterol (27OHC)144.5 (39.3)(66.9–211.7) 7⍺-hydroxycholesterol (7⍺OHC)47.4 (27.0)(13.4–122.2) 7β-hydroxycholesterol (7βOHC)7.6 (5.0)(0.06–21.3) 7-ketocholesterol (7 KC)5.0 (2.9)(1.2–12.3) Total oxysterolsa307.2 (72.0)(145.9–473.9)aNormal BMI 18.5–24.9; overweight BMI 25–29.9; obese BMI 30–34.9bIncluding epidural analgesia ($$n = 16$$) and narcotic pain medications ($$n = 3$$)cGestational weeks at time of birthdRepresents the sum of 24OHC, 25OHC, 27OHC, 7⍺OHC, 7βOHC, and 7KC
## Data collection
At enrollment, prospective women completed an interviewer-administered questionnaire to obtain information regarding general socio-demographics as well as reproductive and medical history. Maternal height and pre-pregnancy weight were collected by self-report at the initial visit. Body mass index (BMI) was calculated as pre-pregnancy weight in kilograms divided by the square of height in meters. At mid-pregnancy, between gestation weeks 22–28, body weight and height were directly measured by research staff using a digital scale and digital stadiometer, respectively. At this same visit, a fasting blood sample was collected by a blood draw technician. The total duration of active labor (> 4 cm dilation) was obtained with a self-reported birth outcome questionnaire assessed at a home visit within 7 days of birth.
## Serum lipid analyses
Following a blood clotting period of 30 minutes at room temperature, serum samples were separated by centrifugation at 3000 x g for 20 min and stored at − 80 °C. Total-cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured by direct automated enzymatic assay using diagnostic reagent kits, calibrators, and quality controls.
Serum oxysterols including 7α-hydroxycholesterol (7αOHC), 7β-hydroxycholesterol (7βOHC), 24-hydroxycholesterol (24OHC), 25-hydroxycholesterol (25OHC), 27-hydroxycholesterol (27OHC), and 7-ketocholesterol (7KC) were determined by liquid chromatography-selected ion monitoring-stable isotope dilution-atmospheric pressure chemical ionization-mass spectroscopy (LC-SIM-SID-APCI-MS) as previously described [19, 20] with slight modification. Briefly, serum oxysterols were released from steryl esters by incubation with microbial cholesterol esterase (100ul containing 2 U for 20 minutes at 37 °C) and following solid phase extraction, were injected into an Ascentis 10 cm X 3 mm, 3-μm C-18 LC column (SupelCo Inc.) on a Shimadzu 10ADVP LC system interfaced with a Shimadzu 2010A MS by APCI interface. Deuterated (d7) 22HC, (d7) 7αHC and (d7) 7KC was used as stable, isotope-labeled, internal standards. We, and others [21], have found that oxysterol analytes are stable at -80 °C storage. The assay was previously validated in accordance with the FDA guidance for bioanalytical methods [22].
## Statistical analyses
The relationship of individuals oxysterols with serum cholesterol and maternal BMI were assessed with Pearson correlation coefficients. Associations between maternal second trimester lipids and labor duration were assessed using multivariable linear regression. As labor duration data was not normally distributed, the variable was log transformed for all analyses. We considered nulliparity (yes/no), birth weight, gestation age, maternal age and pre-pregnancy BMI (continuous variables) as potential confounding factors. Confounders with a (p-value < 0.05) were considered significant and were further adjusted in the models. Two models were used in the analysis: (i) Model 1 was the crude model with second trimester lipid exposure only; and (ii) Model 2 adjusted for maternal nulliparity and maternal age (the only two significant confounders). All analyses were conducted using SAS version 9.4 (SAS Institute, Inc., Cary, NC). Statistical significance was set at a two-sided alpha level of 0.05. A post hoc power analysis, based on linear bivariate regression between maternal total oxysterols and labor duration, with significance set at α = 0.05, yielded a power (1-β) of 0.766 (G*Power 3.1.5) [23].
## Results
Mothers included in this study were mainly White ($92\%$), highly educated ($92\%$ college graduate), and overweight ($28\%$) or obese ($32\%$) before pregnancy (Table 1). The majority of women 16 ($64\%$) in the cohort were nulliparous. Mean active labor duration was 8.9 ± 6.9 hours (13.9 ± 8.6 hours for nulliparous women; 6.2 ± 4.2 hours multiparous women).
Serum oxysterols were not correlated ($p \leq 0.05$) with maternal BMI at pre-pregnancy or mid-pregnancy (Table 2). Serum 24OHC ($r = 0.37$), 27OHC ($r = 0.49$), 7KC ($r = 0.33$), and total oxysterols ($r = 0.52$) were positively correlated with total-C, however, no correlation ($p \leq 0.05$) was observed between cholesterol and 25OHC, 7⍺OHC, and 7βOHC. Amongst the oxysterols, only 27OHC and total oxysterols had significant ($p \leq 0.05$) positive associations with LDL-C ($r = 0.43$ and 0.41) and HDL-C ($r = 0.51$ and 0.42).Table 2Correlations between maternal mid-pregnancy oxysterol concentrations, weight status and serum lipidsMid-Pregnancy Serum Oxysterols (ng/mL)Maternal characteristic ($$n = 25$$)24OHC25OHC27OHC7⍺OHC7βOHC7 KCTotal OxysterolsaWeight status Pre-pregnancy BMI (kg/m2)r−0.19−0.13− 0.21− 0.02− 0.040.00− 0.20p0.310.310.270.940.811.000.27 Mid-pregnancy BMI (kg/m2)r−0.15−0.11− 0.150.03− 0.080.05− 0.14p0.420.420.410.880.660.800.44Serum Lipids Total-C (mg/dL)r0.370.190.490.23−0.150.330.52p0.040.300.0040.210.410.070.002 LDL-C (mg/dL)r0.280.200.430.14−0.220.210.41p0.120.260.010.450.230.250.02 HDL-C (mg/dL)r0.220.100.510.10−0.100.320.42p0.230.600.000.570.600.070.02r, Pearson Correlation Coefficient; p, p-value; aRepresents the sum of 24OHC, 25OHC, 27OHC, 7⍺OHC, 7βOHC, and 7KC No significant associations between labor duration and serum total-C ($$p \leq 0.08$$), LDL-C ($$p \leq 0.07$$), and HDL-C ($$p \leq 0.93$$) were observed (Table 3). Among the oxysterols, with the exception of 7⍺OHC and 7βOHC, an increase in labor duration was observed for every 1-unit increment in serum serum 24OHC (0.96 min [0.36,1.56], $p \leq 0.01$), 25OHC (7.02 min [1.92,12.24], $$p \leq 0.01$$), 27OHC (0.54 min [0.06, 1.08], $p \leq 0.05$), 7KC (8.04 min [2.7,13.5], $p \leq 0.01$), and total oxysterols (0.42 min [0.18,0.06], $p \leq 0.01$] (Table 3).Table 3Associations between maternal oxysterol concentrations and labor durationLabor Duration (minutes)Serum lipidsModel 1β ($95\%$ CI)$$n = 25$$p-valueModel 2β ($95\%$ CI)$$n = 25$$p-valueLipid panel (mg/dL) Total-cholesterol0.36 (−0.18,1.02)0.220.48 (−0.06,0.96)0.08 LDL-cholesterol0.48 (−0.36,1.32)0.250.60 (−0.06,1.32)0.07 HDL-cholesterol0.06 (−1.2,1.26)0.97−0.06(−1.02,0.9.6)0.93Oxysterols (ng/mL) 24-hydroxycholesterol1.14 (0.42,1.86)< 0.010.96 (0.36,1.56)< 0.01 25-hydroxycholesterol6.9 (0.24,13.62)0.047.02 (1.92,12.24)0.01 27-hydroxycholesterol0.72 (0.06,1.32)0.030.54 (0.06,1.08)0.04 7⍺-hydroxycholesterol0.24 (−0.48,1.08)0.500.42 (− 0.18,1.02)0.20 7β-hydroxycholesterol1.56 (−3.36,6.48)0.531.02 (−3.06,5.1)0.62 7-ketocholesterol7.38 (0.42,14.34)0.038.04 (2.7,13.5)< 0.01 Total oxysterols0.48 (0.24,0.72)< 0.010.42 (0.18,0.06)< 0.01*Values are beta coefficients ($95\%$ CI) derived from linear regression models, representing the change in labor duration (minutes) with one unit increment in each lipid exposure; Model 1: crude model; Model 2: adjusted for maternal nulliparity and age
## Discussion
In this cohort, maternal mid-pregnancy serum oxysterol concentrations (24OHC, 25OHC, 27OHC, and 7KC) were positively associated with labor duration. However, the magnitude of these associations varied depending on the oxysterol species, possibly reflecting their specific routes of synthesis and functional roles. Interestingly, serum TC and LDL-C were not associated with labor duration. This contrast is perhaps surprising given the significant correlations we observed between serum total oxysterols and cholesterol and the known role of oxysterols in regulating intracellular cholesterol efflux and accumulation [18]. Alternatively, due to the specific roles of oxysterols as inflammatory mediators and nuclear receptor ligands, oxysterols may represent a more sensitive marker of uterine contraction than cholesterol.
Oxysterols are highly reactive oxygenated derivatives of cholesterol that differ in their oxidation sites (within the A or B rings or the side chain of cholesterol) and their routes of synthesis [24]. They can be generated through specific cytochrome P450 mitochondrial or microsomal enzymes that are coordinately regulated through substrate and co-factor availability or through random non-enzymatic autooxidation routes involving reactive oxygen species [25]. Oxysterols of enzymatic origin, including 7αOHC, 24OHC, 25OHC, and 27OHC, are most often identified as intermediates of bile acid synthesis, however, they also act as intracellular ligands for several nuclear receptors and thus play a functional role in regulating a broad range of metabolic pathways, including cholesterol metabolism. Alternatively, oxysterols of non-enzymatic origin, including 7βOHC and 7KC, are typically recognized for their pro-oxidant and pro-inflammatory biological activities through their induction of inflammatory cytokine expression [24].
Although oxysterols have been implicated in the pathogenesis of several disease states including atherosclerosis [26] and dementia [27], few studies have characterized oxysterol concentrations during pregnancy or examined their relationship with pregnancy outcomes. In a longitudinal pregnancy cohort of 33 women, Winkler et al. [ 2017] [28] reported an increase in maternal 27OHC throughout pregnancy but no association with several pregnancy complications including intrauterine growth restriction (IUGR), preeclampsia (PE), and hemolysis, elevated liver enzymes, low platelet count (HELLP) syndrome. However, Mistry et al. observed an increase in placental 27OHC concentration and enhanced maternal and fetal cholesterol-mediated serum efflux capacity in pregnancies affected by preeclampsia compared with normotensive controls [29]. A potential role of oxysterols in placental dysfunction is further supported through in vitro studies reporting the pro-inflammatory actions of 25OHC and 7KC through a toll-like receptor 4 (TLR4)-dependent mechanism [30] and cytotoxic effects of 25OHC in placental trophoblasts [31].
We are not aware of previous human studies that have examined the association between maternal cholesterol or their oxygenated derivatives and labor duration. The observed differences in the relative magnitude of the effect for individual oxysterol species may be of interest in their potential utility as predictive biomarkers of labor dystocia. At this point, the underlying reasons for these differential responses are not known. However, while the physiological effects of oxysterols as a class of compounds has been historically emphasized [32], more recent work has recognized the metabolic implications of individual oxysterol species which may be influenced by several factors including absolute concentrations and tissue distribution [33], differential ligand binding affinities [34], and a diverse range of metabolic activities [24].
Although our findings cannot provide causative evidence to support a direct role of maternal sterol status on labor duration, results from animal model studies suggest several mechanistic links between lipotoxicity (specifically, cholesterol and oxysterols) and labor dystocia. First, as the precursor substrate of steroid hormones, cholesterol is essential for the endocrine balance between estrogen and progesterone that activates the myometrium and regulates the onset of labor [35]. Second, under normal physiological concentrations, cholesterol is involved in the stabilization and posttranslational processing of the oxytocin receptor [15, 16], however, an abnormal increase in cholesterol has been shown to blunt oxytocin-induced contractile activity in mouse [12] and human [11] uterine tissue. Third, previous work by Mouzat et al. [ 2006] in mice suggests that oxysterols may have a role in preserving uterine contractile function under excessive cholesterol conditions by signaling increased transcription of cholesterol efflux genes through ligand activation of the liver X receptor β (LXRβ) [18].
The results of this pilot study should be interpreted with caution given several study limitations, First, due to resource constraints, this study included 25 pregnant women of which only $64\%$ were nulliparous and for whom labor dystocia is more commonly diagnosed. However, we hope the results provide insight into estimated effect size for the design of future randomized controlled trials to identify lipid biomarkers of labor dystocia. Second, our sample was limited with respect to race/ethnicity and education and may be biased due to potentially inaccurate assessment of labor duration with a self-reported birth outcome questionnaire. Finally, oxysterol analysis was limited to mid-pregnancy only, and it is unknown how concentrations at this timepoint correlate with those at early and late gestation phases.
## Conclusions
In this cohort, mid-pregnancy concentrations oxysterols (24OHC, 25OHC, 27OHC, and 7KC) were positively associated with labor duration. However, considering the small population and use of self-reported labor duration, subsequent studies are required to support the potential use of serum oxysterols as biomarkers of labor duration.
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|
---
title: 'Master protocol to assess the long-term safety in kidney transplant recipients
who previously received Medeor’s cellular immunotherapy products: the MDR-105-SAE'
authors:
- Sam Kant
- Dixon B. Kaufman
- Lenuta Micsa
- Daniel C. Brennan
journal: Trials
year: 2023
pmcid: PMC10007834
doi: 10.1186/s13063-023-07204-4
license: CC BY 4.0
---
# Master protocol to assess the long-term safety in kidney transplant recipients who previously received Medeor’s cellular immunotherapy products: the MDR-105-SAE
## Abstract
### Background
Immunosuppression in transplantation continues to be associated with a multitude of adverse effects. Induction of immune tolerance may be a viable strategy to reduce dependence on immunosuppression. Various trials are currently underway to assess the efficacy of this strategy. However, long-term safety data for these immune tolerance regimes has yet to be established.
### Methods/design
At the completion of primary follow-up of various Medeor kidney transplant studies, subjects receiving cellular immunotherapy products will be followed annually as per protocolized schedule for up to an additional 84 months (7 years) to evaluate long-term safety. Long-term safety will be assessed by summarizing incidence of serious adverse events, adverse events leading to study withdrawal and hospitalization rates.
### Discussion
This extension study will be an important step in evaluating safety issues pertaining to immune tolerance regimens, long-term effects of which are largely unknown. These data are essential for furthering an unrealized goal of kidney transplantation- graft longevity without the adverse effects from long-term immunosuppression. The study design utilizes the methodology of a master protocol, wherein multiple therapies can be assessed simultaneously with accompanied gathering of long-term safety data.
## Background
Thefield of transplantation is actively engaged in studying the induction of effective immune tolerance without the use of immunosuppression. Clinical trials to establish tolerance are using a combination of organ and hematopoietic cell transplantation to achieve immunological tolerance via either full or mixed donor chimerism [1–3]. Full donor chimerism is acquired when the entire recipient hematopoietic system is replaced by donor cells (donor cells > $98\%$) resulting in deactivation of donor T cells [4]. Mixed chimerism is defined as continued mixing of donor and recipient hematopoietic cells in recipient tissues after transplantation of donor cells. Achievement of mixed chimerism can result in tolerance of organ grafts from the hematopoietic cell donors without the need for immunosuppression [3]. Various centers have employed myeloablative or non-myeloablative conditioning strategies, with total body, local thymic, or lymphoid irradiation, and subsequent donor whole bone marrow transplantation or infusion of granulocyte colony stimulating factor (G-CSF) mobilized CD34 + T cells [5–7]. Establishment of mixed chimerism is associated with lower risk of graft versus host disease (GVHD) and immunodeficiency in comparison to full donor chimerism. The Stanford group reported the long-term outcomes of a pioneering, single-center study in which kidney transplant recipients were given post-transplant conditioning with 10 doses of total lymphoid irradiation (TLI) and 5 doses of anti-thymocyte globulin (ATG) [3]. On completion of TLI, a combination of enriched CD34 + hematopoietic progenitors and a defined number of donor T cells were injected immediately (G-CSF was administered to donors to mobilize progenitors into the blood such that collection was performed via apheresis). Fully HLA-matched patients were given T cells along with the CD34 + cells. The majority of the patients ($\frac{24}{29}$ fully HLA matched) achieved mixed chimerism for at least 1 year. In the mixed chimerism group, MMF was withdrawn 39 days after cell infusion following kidney transplantation, while calcineurin inhibitors were tapered beginning at approximately 6 months and ceased after approximately 1 year. In the Stanford study, $\frac{23}{24}$ ($96\%$) had no evidence of rejection in the first year after withdrawal of immunosuppression. Although two late rejections occurred at 44 and 60 months after rejection, no grafts were lost due to rejection [8]. For this strategy to achieve success, it is imperative to elucidate the long-term safety profile. The monitoring of events such as rejection, graft dysfunction/loss, graft vs host disease (GVHD), and opportunistic infections is important. The MDR-105-SAE study is designed to be a long-term safety monitoring extension trial involving Medeor’s cellular immunotherapy administered to kidney transplant recipients to induce immune tolerance via mixed lymphohematopoietic chimerism. MDR-105-SAE is a Master Protocol that is designed to allow current and future kidney transplant studies to enroll into a single design where safety data can be analyzed in a consistent manner. The initial clinical trial to be enrolled is a phase 3 open label, multicenter, randomized controlled trial to assess safety and efficacy of donor-derived CD34 + hematopoietic stem/progenitor cells and a specified dose of CD3 + T cells administered to recipients of HLA-matched, living donor kidney transplants post total lymphoid irradiation (TLI) and rabbit-anti-thymoglobulin induction, with progressive tapering of maintenance immunosuppression (NCT03363945).
This study protocol will provide up to 84 months (7 years) of additional follow-up to collect the data into a centralized database allowing for continuous monitoring of any important safety signals.
## Patient population
The study population will consist of kidney transplant recipients who received Medeor’s cellular immunotherapy in its previous and future kidney transplant studies. Upon completion of the initial study, the kidney transplant recipients who received the Medeor product are given an option to participate in the long-term follow-up study, MDR-105-SAE. Study participants with subsequently failed allografts, albeit now on kidney replacement therapy, will also be included in this study. The study will exclude subjects who have lost kidney allografts and have been subsequently retransplanted.
## Subject inclusion criteria
All of the following criteria must be met for study participants to be included in the study:Able and willing to fully comply with all study procedures and restrictions. Able to understand and provide written, signed, and dated informed consent to participate in the study in accordance with International Council for Harmonization (ICH) Good Clinical Practice Guidelines (GCP) and all applicable local regulations. Have previously completed a Medeor study and received a Medeor cellular immunotherapy product
## Subject exclusion criteria
Participants who meet any of the following criteria will not be eligible:Has any condition or circumstance, which in the opinion of the investigator would significantly interfere with the subject’s protocol compliance or put the subject at increased risk. The subject will be expected to comply with all study visits and procedures. The protocol requires annual study visits where a complete physical exam, vital signs, and laboratory samples will be obtained. Unable or unwilling to provide written, signed, and dated informed consent to participate in the study. Has undergone a second organ transplant with an organ derived from an individual other than the donor of the transplant kidney received during a Medeor study
## Baseline characteristics
Participant demographics, past medical history, and clinical course will be recorded. To be able to link the subject data from this study to the parent study, the study code, center number, subject number, and the last visit date from the parent study will be recorded in the case report form (CRF).
## Study design
At the time of enrolment in the initial study, the kidney transplant recipients are asked to acknowledge the expectation to participate in a multi-year, long-term follow-up study at the completion of the observation period in the initial study. Upon completion of the initial study, the kidney transplant recipients who received the Medeor product will sign the consent form and begin Study MDR-105-SAE. These patients are expected to participate in the MDR-105-SAE study. Medeor is working with the sites to be able to enroll these subjects into the MDR-105-SAE study upon completion of the initial study. At the completion of primary follow-up of various Medeor kidney transplant studies (called parent studies), subjects receiving Medeor’s cellular immunotherapy products will be followed annually for up to an additional 84 months (7 years) for long-term safety evaluation. No study treatment is involved. All immunosuppression and therapeutic drug monitoring will be managed by treating physicians according to local standard of care (SOC). Any changes in, and reinstitution of immunosuppression drug therapy, will be at the treating physician’s discretion. Post-kidney transplant biopsy will be performed for cause at any time at the discretion of a subject’s treating physician. MDR-105-SAE protocol assessments will be performed annually following study entry. All participants must provide informed consent prior to enrolling into the MDR-105-SAE study.
## Study visits and procedures
Within 3 months of completion of a Medeor clinical trial, the kidney transplant recipients who received a Medeor cellular immunotherapy product will begin annual study MDR-105-SAE visits at 12 months (with an allowed deviation of ± 1 month for scheduled annual visits).
Unscheduled study visits at the transplant center and at other health care delivery facilities may occur. Records from such visits would be obtained and reviewed for adequate safety reporting.
Extensions in study visit scheduling to accommodate non-working days will not be considered protocol deviations. Additionally, extensions in scheduling because of special circumstances, such as subject work issues, lack of transportation, and bad weather, if cleared in advance with the medical monitor, will also not be considered protocol deviations.
The schedule for clinical assessments is elucidated in Table 1 (below references).Table 1Schedule of monitoring over the course of MDR-105-SAEStudy visit year1234567Study visit number01234567Study visit window (months) + 3 ± 1 ± 1 ± 1 ± 1 ± 1 ± 1 ± 1AssessmentScreeningPost enrollmentInformed consent⦁Confirm study eligibility criteria⦁Demographics⦁Baseline characteristics⦁Medical history⦁Prior/concomitant medication⦁⦁⦁⦁⦁⦁⦁⦁Perform complete physical examination⦁⦁⦁⦁⦁⦁⦁⦁Record vital signs⦁⦁⦁⦁⦁⦁⦁⦁Record weight⦁⦁⦁⦁⦁⦁⦁⦁Record height⦁Hematology panelaLLLLLLLComprehensive metabolic panelaLLLLLLLUrinalysisLLLLLLLCalcineurin inhibitor trough levelsbLLLLLLLMixed chimerism testingcCCCCCCCRecord instances of acute rejection requiring treatment⦁⦁⦁⦁⦁⦁⦁Record instances of proteinuria⦁⦁⦁⦁⦁⦁⦁Kidney biopsydOnly for causeComplete subject status form⦁⦁⦁⦁⦁⦁⦁Record all SAEs⦁ (ongoing)Record AEs leading to study withdrawal⦁ (as needed)Record history of hospitalizations since transplant⦁⦁⦁⦁⦁⦁⦁Record development of NODATe⦁⦁⦁⦁⦁⦁⦁Record development of GVHD⦁⦁⦁⦁⦁⦁⦁Record instances of BK viremia development⦁⦁⦁⦁⦁⦁⦁Record instances of dnDSA⦁⦁⦁⦁⦁⦁⦁Record instances of cardiovascular eventsf⦁⦁⦁⦁⦁⦁⦁Record instances of opportunistic infection⦁⦁⦁⦁⦁⦁⦁Record instances of PTLD/other malignancies⦁⦁⦁⦁⦁⦁⦁Record instances of MDS⦁⦁⦁⦁⦁⦁⦁AE adverse effects, C central laboratory, dnDSA de novo DSA, L local laboratory, MDS myelodysplastic syndrome, NODAT new-onset diabetes after transplantation, PTLD post-transplant lymphoproliferative diseaseaTesting will be completed at each follow-up visit and will be analyzed locally using accepted laboratory methodologybFor subjects who have discontinued CNI, compliance check is performed as needed to assess trough levels locally, per standard accepted laboratory methodology. CNI trough levels will be assessed in all recipients at each follow-up visit for subjects taking CNIcThe degree of donor chimerism in recipient blood will be by central laboratory. Subjects who have lost mixed chimerism (< $5\%$) do not need to repeat testing at subsequent visitsdTransplant kidney biopsy will be performed for cause at the discretion of a subject’s treating physician and will undergo local pathology revieweNODAT will be determined based on need for use of an antidiabetic agent for more than 30 days, or 2 fasting plasma glucose levels ≥ 126 mg/dL in a subject who was not diabetic at study entryfCardiovascular events include acute myocardial infarction, stroke, acute peripheral arterial occlusion, and revascularization procedure
## Treatments
There will be no protocol mandated treatments or interventions outside of routine post-transplant monitoring as annotated in Table 1. Any interventions, alterations, and/or reinstitution of immunosuppression will be at the sole discretion of the treating physician. Reinstitution of transplant immunosuppression in subjects who successfully achieved functional immune tolerance as defined in the parent study should only be for cause (e.g., biopsy-proven acute rejection). Should immunosuppression be reinstituted, the subject should continue in the study and follow the schedule of assessment given in Table 1. The only non-SOC monitoring will be assessment of mixed chimerism on an annual basis in subjects who maintain the mixed donor chimerism (> $5\%$). As this is a strictly observational study, there is no rationale for early termination rules or criteria.
## Discontinuation and withdrawal
Withdrawal of individual subject(s) A subject is free to withdraw from the study at any time without giving reason for doing so and without penalty or prejudice. The investigator is also free to terminate a subject’s involvement in the study at any time if a subject’s clinical condition warrants it.
Subjects who withdraw all consent for further participation in the study will be withdrawn from all follow-up assessments and will only be followed for mortality through public records. Subjects should have an end of the study visit completed shown in Table 1 at the time of their discontinuation.[2]Discontinuation of the site If an investigator intends to discontinue participation in the study, the investigator must.immediately inform the sponsor. The sponsor will provide written notice for sitetermination.[3]Discontinuation of the study The sponsor may terminate this study prematurely, either in its entirety or at any study site, for reasonable cause provided that written notice is submitted in advance of the intended termination. If the sponsor terminates the study for noncompliance reasons, the sponsor will immediately notify the investigators and subsequently provide written instructions for study termination.
## Assessment of safety
Safety will be assessed using incidence of serious adverse effects (SAEs) and adverse effects (AEs) leading to study withdrawal (SAEs and AEs defined below); review of laboratory data, including hematology, biochemistry (comprehensive metabolic profile), and urinalysis; hospitalization rates; and vital signs yearly during the study follow-up period.
The coding dictionary for this study will be the Medical Dictionary for Regulatory Activities (MedDRA). It will be used to summarize AEs by system organ class and/or preferred term. The version of MedDRA to be used will be determined prior to final database lock.
## Safety parameters
Vital signs All recipients will have vital signs (systolic and diastolic blood pressure, pulse, respiratory rate, and temperature) collected at all yearly study visits. After the subject has been sitting for 5 min, with back supported and both feet placed on the floor, systolic and diastolic blood pressure will be measured with an appropriately sized cuff and calibrated machine.(b)Physical examination A complete physical examination will be performed on all recipient subjects as part of the annual safety evaluation study visits. Significant findings that are present prior to signing the Informed Consent Form must be included in the Medical History screen on the subject’s case report form (CRF). The investigator or a qualified designee will conduct the exams, determine findings, and assess any abnormalities as to clinical significance.
(c)Laboratory assessments Clinical laboratory tests, including hematology and comprehensive metabolic panel (chemistry), will be performed on recipients as specified in the Schedule of Assessments and Procedures in Table 1. Clinical laboratory tests are to be performed and reviewed by the investigator or qualified designee:Hematology: complete blood count (CBC) with differential will be measured locally for recipients annually. Blood chemistry (Comprehensive Metabolic Panel): A Comprehensive Metabolic Profile must include all of the following measurements: blood urea nitrogen (BUN), creatinine, sodium, potassium, chloride, bicarbonate, glucose, phosphorus, total protein, albumin, creatine kinase (CK), lactate dehydrogenase (LDH), amylase, and liver chemistries including total and direct bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP). The full chemistry panel will be analyzed locally using accepted laboratory methodology at each of the yearly visits. Urinalysis: Instances of proteinuria and/or microscopic hematuria will be recorded.
## Definition of adverse event (AE)
An AE is any unfavorable and unintended diagnosis, symptom, sign (including an abnormal laboratory finding), syndrome or disease which either occurs during the study, having been absent at baseline, or, if present at baseline, appears to worsen.
## Definition of serious adverse event (SAE)
An SAE is defined (21CFR 312.32) as any adverse event that results in any of the following outcomes:DeathA life-threatening adverse eventInpatient hospitalization or prolongation of existing hospitalizationPersistent or significant incapacity or substantial disruption of the ability to conduct normal life functionsA congenital anomaly or birth defect Other important medical events may also be considered an SAE when, based upon appropriate medical judgment, they may jeopardize the patient or subject and may require medical or surgical intervention to prevent one of the outcomes listed above.
## Events of special interest
Clinically significant opportunistic infections (grade 3 Common Terminology Criteria for Adverse Events (CTCAE) or higher or meeting criteria of an SAE), all cancers other than PTLD, and cardiovascular events including revascularization procedures will be reported on dedicated electronic case report forms (eCRFs) for these events of special interest. Active surveillance for myelodysplastic syndrome (MDS) Myelodysplastic syndromes (MDS) include a group of clonal myeloid neoplasms with presence of cytopenias due to ineffective hematopoiesis, abnormal blood, and marrow cell morphology [9]. These abnormal cells can undergo changes in clonality and progress to acute myeloid leukemia (AML). Some myelodysplastic syndromes have no known cause. Others are caused by exposure to cancer treatments, such as chemotherapy and radiation, or to toxic chemicals, such as tobacco, benzene, and pesticides, or to heavy metals, such as lead.
Active surveillance for MDS consists of full annual physical exam with medical history and evaluation for possible risk factors and CBC with platelets and differential. Additional tests such as vitamin B12 and folate may be required when results of CBC and physical exam are outside of normal parameters which often signal an underlying medical issue. MDS will not be reported as AEs, but as outcomes, and will be collected and reported on dedicated case report forms specifically designed to capture these events.(b)Reporting of pregnancy *Pregnancy is* neither an AE nor an SAE, unless a complication relating to the pregnancy occurs. All reports of congenital abnormalities/birth defects are SAEs. Spontaneous miscarriages should also be reported and addressed as SAEs. Elective abortions without complications will not be considered AEs. However, all pregnancies occurring during this study (in subjects or female partners of subjects) are to be reported to the sponsor at the subsequent scheduled visit.
(c)Events to be reported as outcomes rather than adverse events Rejection episodes, opportunistic infections, BK viremia, cardiovascular events, graft versus host disease (GvHD), post-transplant lymphoproliferative disorder (PTLD), MDS, and new-onset diabetes after transplantation, graft loss, and death are outcomes and will be reported on dedicated case report forms (CRFs) for these events. Causes of death should be reported as SAEs.(iv)Cardiovascular events Cardiovascular events will include documented acute myocardial infarction, stroke, acute peripheral arterial occlusion, and revascularization procedures. Revascularization procedures will include open and endovascular procedures intended to restore arterial blood flow. Such procedures include bypass, endarterectomy, thrombectomy, angioplasty, stenting, atherectomy, and aneurysm resection/exclusion. Cardiovascular events will be recorded during the study.(e)New-onset diabetes after transplantation (NODAT) NODAT is defined in this study as the use of an antidiabetic agent/insulin for more than 30 days or any of the following in a subject who was not diabetic at study entry [10]:Fasting glucose 126 mg/dL (7 mmol/L) in more than one occasionRandom glucose 200 mg/dL (11.1 mmol/L) with symptomsTwo-hour glucose after a 75-g oral glucose tolerance test (OGTT) 200 mg/dL (11.1 mmol/L)Hemoglobin A1C (HbA1c) $6.5\%$.
The development of NODAT will be assessed yearly during the study.(f)Transplant kidney biopsy Transplant kidney biopsy may be performed for cause at the discretion of a subject’s treating physicians. Biopsies will undergo local pathology review.(g)Estimated glomerular filtration rate (eGFR) Th eGFR will be calculated yearly during the study using the 4-variable equation from the CKD-EPI 2021 equation, using locally assessed serum creatinine data from the appropriate time points. The calculation will be done by Data Management based on the laboratory and demographic information collected during the course of the study.
CKD-EPI equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{eGFRcr}=142\times\min\;(\mathrm{Scr}/\kappa,1)\mathrm\alpha\times\max\;(\mathrm{Scr}/\kappa,1)-1.200\times0.9938\mathrm{Age}\times1.012\;\lbrack\mathrm{if}\;\mathrm{female}\rbrack$$\end{document}eGFRcr=142×min(Scr/κ,1)α×max(Scr/κ,1)-1.200×0.9938Age×1.012[iffemale] where: Scr = serum creatinine in mg/dL.
κ = 0.7 (females) or 0.9 (males) α = − 0.241 (female) or − 0.302 (male) min (Scr/κ, 1) is the minimum of Scr/κ or 1.0 max (Scr/κ, 1) is the maximum of Scr/κ or 1.0
Age (years).
(h)De novo DSA (DnDSA) Post-kidney transplant testing for dnDSA will be performed using single-antigen bead methodology using a Luminex or similar analytic instrument in a central laboratory. The dnDSA testing may also be performed locally at the time of any “for cause” transplant kidney biopsy per institutional standard of care.(i)Donor chimerism Mixed chimerism is defined as presence of at least $5\%$ donor cells in either whole blood or in at least one WBC lineage (CD3 + T cells, CD33 + myeloid cells, CD19 + B cells, and/or CD56 + natural killer [NK] cells). The degree of donor chimerism in transplant kidney recipient blood will be assessed centrally using accepted laboratory methodology yearly during the study. Subjects who have lost mixed chimerism (< $5\%$) do not need to repeat testing at subsequent visits.
## Severity of adverse events
Adverse events, including abnormal clinical laboratory values, will be graded using the National Cancer Institute—Common Terminology Criteria for Adverse Events (NCI-CTCAE) guidelines (Version 5 or higher). Events that are not stipulated in the NCI-CTCAE Version 5 or higher will be assessed according to the criteria below and entered into the eCRF.
It is important to distinguish between serious and severe AEs. An AE of severe intensity may or may not also be considered serious.
## Monitoring and reporting serious adverse events only
All subjects will be monitored closely for SAEs during study participation. Subjects who discontinue prematurely from the study will be encouraged to return for assessment of safety. Follow-up safety information may also be obtained directly from a subject’s physician with source document support whenever applicable (e.g., biopsy results) or from medical records, laboratory reports, and imaging reports.
Whenever possible, symptoms should be grouped as a single syndrome or diagnosis. The investigator should specify the duration (start and end dates) or if the event is ongoing, the severity grade, other medication or therapies have been taken (concomitant medication/non-drug therapy), outcome, and his/her opinion as to whether there is a reasonable possibility that the SAE was caused by the previous study treatment.
In the event of an SAE, the investigator should follow up with the outcome until the clinical recovery is complete and laboratory results have returned to normal or until progression has been stabilized.
The investigator must provide the sponsor appropriate information concerning any findings suggesting significant hazards, contraindications, side effects, or precautions pertinent to the safety of the study treatment. Direct contact information for the Study Medical Monitor will be provided to the institution at time of institutional review.
All SAEs must be reported to the Sponsor within 24 h of the first awareness of the event. The Investigator must complete, sign and date the SAE pages, verify the accuracy of the information recorded on the SAE pages with the corresponding source documents, and send to sponsor:The SAEs will be reported to the sponsor by completing an SAE Report Form. All sections on the form are to be completed. Information that is not available at the time of initial reporting should be submitted as a follow-up. The date of receipt of an initial SAE Report Form will be considered as day 0 for the purpose of determining the time for regulatory reporting of an expedited event and timelines of further study-related activities. The copy of all examinations carried out and the dates on which these examinations were performed will be attached. For laboratory results, the laboratory normal ranges will be included.
It is the Principal Investigator (PI)’s responsibility to notify the IRB of all SAEs that occur at his or her site. The sponsor is responsible for notifying the relevant regulatory authorities of certain safety events, including expedited safety reports. Investigators will also be notified by the Sponsor of all suspected, unexpected, serious, adverse reactions (SUSAR, $\frac{7}{15}$ Day Safety Reports) that occur during the clinical trial. Each site is then responsible for notifying its IRB of these additional SAEs.
## Safety endpoints
Long-term safety will be assessed by summarizing incidence of serious adverse events (SAEs), AEs leading to study withdrawal, and hospitalization rates. Summary statistics will be presented by visit for laboratory data, including hematology, renal function, biochemistry, and vital signs. Adverse events will be classified for seriousness using standard regulatory criteria and for severity according to the National Cancer Institute—Common Terminology Criteria for Adverse Events (NCI-CTCAE) version 5 or higher grading scale whenever feasible.
## Outcomes of special interest
The number and proportion of subjects with biopsy-proven acute rejection (BPAR), de novo donor-specific antibody (dnDSA) (if applicable), BK viremia, opportunistic infection, post-transplant lymphoproliferative disorder (PTLD), myelodysplastic syndrome (MDS), new-onset diabetes after transplantation (NODAT), graft versus host disease (GvHD), cardiovascular (CV) events, and initiation of immunosuppression will be presented by visit and overall. Transplant kidney loss and subject death will also be summarized by visit and overall.
Outcomes of special interest noted above will not be reported as AEs, but rather as outcomes, and will be collected and reported on dedicated case report forms specifically designed to capture these events; this will include severity and seriousness assessments.
## Study monitoring
Monitoring and auditing procedures approved by the sponsor will be followed, to comply with GCP guidelines. The study will be monitored by the sponsor or its designee. Monitoring may be done either in person or remotely.
The site monitor will ensure that the investigation is conducted according to protocol design and regulatory requirements by frequent communications (letter, e-mail, telephone).
Regulatory authorities, the IRB, and other appropriate institutional regulatory bodies, and/or the sponsor’s clinical quality assurance group may request access to all source documents, CRFs, and other study documentation for on-site audit or inspection. Direct access to these documents must be guaranteed by the Investigator, who must provide support at all times for these activities.
To ensure compliance with GCP and all applicable regulatory requirements, the sponsor or its designee may conduct a quality assurance audit.
## Ethics review
For the MDR-105-SAE study, a Central IRB has been approved. Some sites are using a Central IRB while others are using the local institutional review board (IRB). For sites that are using local IRB, the IRB approval must be obtained and the written approval to be submitted to the sponsor or its designee before the site can enroll any subject into the study. The final study protocol, including the final version of the Informed Consent Form, must be approved, or given a favorable opinion in writing by an IRB.
The investigator is responsible for informing the IRB of any amendment to the protocol in accordance with local requirements. The protocol must be re-approved by the IRB upon receipt of amendments and annually if local regulations require.
Initial IRB approval, and all materials approved by the IRB for this study including the subject consent form and recruitment materials must be maintained by the investigator and made available for inspection.
The PI at each study site is also responsible for providing the IRB with reports of any reportable serious adverse drug reactions from any other study conducted with the investigational product per the IRB requirements. Medeor Therapeutics or its designee will provide this information to the PI. Progress reports will be provided to the IRB according to local regulations and guidelines.
## Ethical conduct of the study
The study will be performed in accordance with ethical principles that have their origin in the Declaration of Helsinki and are consistent with ICH/GCP and applicable regulatory requirements.
## Written informed consent
The investigator(s) at each center will ensure that the prospective study subjects are given full and adequate oral and written information about the nature, purpose, possible risk, and benefit of the study. Subjects will also be notified that they are free to discontinue from the study at any time. The subjects will be given the opportunity to ask questions and allowed time to consider the information provided.
## Case report form completion
The Sponsor or its designee will provide the clinical sites with access to an electronic case report form (eCRF) for each subject. The PI is responsible for ensuring the accuracy, completeness, and timeliness of the data reported in a subject’s eCRF. Source documentation supporting the eCRF data should indicate the subject’s participation in the study and should document the dates and details of study procedures, AEs, and subject status.
The investigator or designated representative should complete the eCRFs as soon as possible after information is collected. The PI must sign and date the eCRF to endorse the recorded data.
## Inspection of records
The sponsor or its designee will be allowed to conduct site visits to the investigation facilities for the purpose of monitoring any aspect of the study. The investigator agrees to allow the monitor to inspect the subject charts and study source documents, and other records relative to study conduct.
## Retention of records
A study investigator must maintain all documentation relating to the study for a period of 2 years after the last marketing application approval, or if not approved 2 years following the discontinuance of the test article for investigation or according to applicable regulatory requirements.
If the investigator withdraws from the responsibility of keeping the study records, custody must be transferred to a person willing to accept the responsibility. Medeor Therapeutics must be notified in writing if a custodial change occurs.
## Protocol deviations
A protocol deviation is any noncompliance with the clinical trial protocol, International Conference on Harmonization Good Clinical Practice (ICH GCP), or Manual of Procedures (MOP) requirements. The noncompliance may be either on the part of the participant, the investigator, or the study site staff. As a result of deviations, corrective actions are to be developed by the site and implemented promptly.
It is the responsibility of the site investigator to use continuous vigilance to identify and report deviations. Protocol deviations must be sent to the reviewing Institutional Review Board (IRB) per their policies. The site investigator is responsible for knowing and adhering to the reviewing IRB requirement.
## Discussion
The continued success of transplantation has been limited by adverse effects associated with immunosuppression. Immunosuppression is associated with increased risk for infections, malignancies, cardiovascular and metabolic disease (hypertension, hyperlipidemia, post-transplant diabetes mellitus), and allograft nephrotoxicity, and independently increases morbidity and mortality in transplant recipients [11]. In addition, chronic rejection, which can be immune or non-immune, can occur despite or because of use of immunosuppression, leading to progressive graft loss [12]. *The* generation of immune tolerance is the next frontier in transplantation to achieve continued durability of the graft without use of immunosuppression laden with adverse effects.
Various trials are now underway to assess the novel methodology of donor-derived CD34 + hematopoietic stem/progenitor cells and a specified dose of CD3 + T cells administered to recipients of kidney transplants post total lymphoid irradiation. While preliminary findings from these trials are extremely encouraging, long-term effects of this immune tolerance regimen are still largely unknown. Extension or “roll-over” studies are especially beneficial in the setting and can provide early evidence of outcomes, along signals of tolerability and safety issues [13].
The current study is unique given it combines multiple studies as a “master protocol,” a recent innovation in research methodology that can aid in evaluation of multiple therapies [14]. These studies have the advantage of utilizing existing infrastructure of trials being included in the master protocol and provide concurrent comparisons of therapies in different population groups [14].
The MDR-105-SAE is a long-term study that will provide long-term safety data in patients currently enrolled in various kidney transplant trials involving administration of immune tolerance regimes. It is imperative that these regimes be shown not only to be effective, but also be associated with long-term safety for the field of transplantation to take the next prodigious and essential step into the future.
## References
1. Scandling JD, Busque S, Dejbakhsh-Jones S. **Tolerance and withdrawal of immunosuppressive drugs in patients given kidney and hematopoietic cell transplants**. *Am J Transplant* (2012) **12** 1133-1145. DOI: 10.1111/j.1600-6143.2012.03992.x
2. Scandling JD, Busque S, Shizuru JA. **Chimerism, graft survival, and withdrawal of immunosuppressive drugs in HLA matched and mismatched patients after living donor kidney and hematopoietic cell transplantation**. *Am J Transplant* (2015) **15** 695-704. DOI: 10.1111/ajt.13091
3. Busque S, Scandling JD, Lowsky R. **Mixed chimerism and acceptance of kidney transplants after immunosuppressive drug withdrawal**. *Sci Transl Med.* (2020) **12** aax8863. DOI: 10.1126/scitranslmed.aax8863
4. Podestà MA, Sykes M. **Chimerism-based tolerance to kidney allografts in humans: novel insights and future perspectives**. *Front Immunol.* (2021) **12** 791725. DOI: 10.3389/fimmu.2021.791725
5. Leventhal J, Abecassis M, Miller J. **Tolerance induction in HLA disparate living donor kidney transplantation by donor stem cell infusion: durable chimerism predicts outcome**. *Transplantation* (2013) **95** 169-176. DOI: 10.1097/TP.0b013e3182782fc1
6. Scandling JD, Busque S, Lowsky R. **Macrochimerism and clinical transplant tolerance**. *Hum Immunol* (2018) **79** 266-271. DOI: 10.1016/j.humimm.2018.01.002
7. Sasaki H, Oura T, Spitzer TR. **Preclinical and clinical studies for transplant tolerance via the mixed chimerism approach**. *Hum Immunol* (2018) **79** 258-265. DOI: 10.1016/j.humimm.2017.11.008
8. Issa F, Strober S, Leventhal JR. **The fourth international workshop on clinical transplant tolerance**. *Am J Transp* (2021) **21** 21-31. DOI: 10.1111/ajt.16139
9. Steensma DP. **Myelodysplastic syndromes current treatment algorithm 2018**. *Blood Cancer J* (2018) **8** 47-7. DOI: 10.1038/s41408-018-0085-4
10. First M, Dhadda S, Croy R, Holman J, Fitzsimmons W. **New-onset diabetes after transplantation (NODAT): an evaluation of definitions in clinical trials**. *Transplantation* (2013) **96** 58-64. DOI: 10.1097/TP.0b013e318293fcf8
11. Marcen R. **Immunosuppressive drugs in kidney transplantation: Impact on patient survival, and incidence of cardiovascular disease, malignancy and infection**. *Drugs* (2009) **69** 2227-2243. DOI: 10.2165/11319260-000000000-00000
12. Pascual M, Theruvath T, Kawai T, Tolkoff-Rubin N, Cosimi AB. **Strategies to improve long-term outcomes after renal transplantation**. *N Engl J Med* (2002) **346** 580-590. DOI: 10.1056/NEJMra011295
13. Burcu M, Manzano-Salgado CB, Butler AM, Christian JB. **A framework for extension studies using real-world data to examine long-term safety and effectiveness**. *Ther Innov Regul Sci* (2021) **56** 15-22. DOI: 10.1007/s43441-021-00322-8
14. Woodcock J, LaVange LM. **Master protocols to study multiple therapies, multiple diseases, or both**. *N Engl J Med* (2017) **377** 62-70. DOI: 10.1056/NEJMra1510062
|
---
title: An eco-friendly separation-based framework for quantitative determination and
purity testing of an antihypertensive ternary pharmaceutical formulation
authors:
- Hoda M. Marzouk
- Nada S. Ayish
- Badr A. El-Zeany
- Ahmed S. Fayed
journal: BMC Chemistry
year: 2023
pmcid: PMC10007836
doi: 10.1186/s13065-023-00926-1
license: CC BY 4.0
---
# An eco-friendly separation-based framework for quantitative determination and purity testing of an antihypertensive ternary pharmaceutical formulation
## Abstract
Designing new, verified methodologies with a focus on sustainability, analytical efficiency, simplicity, and the environment has become a major priority for pharmaceutical quality control units. In this way, sustainable and selective separation-based methodologies were designed and validated for the concurrent estimation of amiloride hydrochloride (AML), hydrochlorothiazide (HCT) and timolol maleate (TIM) in their fixed dose formulation (Moducren® Tablets) along with hydrochlorothiazide potential impurities, salamide (DSA) and chlorothiazide (CT). The first method is a high performance thin layer chromatographic method (HPTLC-densitometry). The first developed method employed silica gel HPTLC F254 plates as stationary phase using a chromatographic developing system composed of ethyl acetate–ethanol–water–ammonia (8.5:1:0.5:0.3, by volume). The separated drug bands were densito-metrically measured at 220.0 nm for AML, HCT, DSA and CT and at 295.0 nm for TIM. The linearity was assessed over a wide concentration range, 0.5–10 µg/band, 1.0–16.0 µg/band and 1.0–14 µg/band for AML, HCT and TIM, in order and 0.05–1.0 µg/band for each of DSA and CT. The second method is capillary zone electrophoresis (CZE). The electrophoretic separation was achieved using background electrolyte (BGE), borate buffer 40.0 mM with pH 9.0 ± 0.2, at applied voltage of + 15 kV with on-column diode array detection at 200.0 nm. The method linearity was reached over the concentration range of 20.0–160.0 µg/mL, 10.0–200.0 µg/mL, 10.0–120.0 µg/mL for AML, HCT and TIM, respectively and 10.0–100.0 µg/mL for DSA. The suggested methods were optimized to achieve best performance and validated agreeing with the ICH guidelines. Assessment of methods’ sustainability and greenness was performed using different greenness assessment tools.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13065-023-00926-1.
## Introduction
Due to the persistent contamination of the ecosystem and the surrounding atmosphere with different types of pollutants, the whole world recently suffers from climatic changes that has a potential impact on all living organisms. That is why environmental sustainability became an international concern in order to secure a healthy environment for the next generations to live in [1]. Lately, scientists all over the world are trying to stick to Green Analytical Chemistry concepts with the aim to eliminate or at least minimize the amount of hazardous chemicals used and generated daily to minimize the environmental impacts of the analytical methods [2]. For this reason different metrics were developed to assess the developed analytical method greenness and evaluate its environmental impact [3].
Diverse analytical approaches have been presented to resolve mixtures of multi-component pharmaceuticals as a result of the broad use of separation-based techniques that depend on the point of differential migration. High-performance thin layer chromatography (HPTLC) has grown significantly as an analytical technique, thanks to the introduction of modern instruments, enhanced stationary phases and automated procedures. It has been used widely in pharmaceutical industry for process development and quality control of the active pharmaceutical ingredients (API) and the final drug products. This can be credited to its simplicity, speed, sensitivity, and affordability [1, 4]. On the other hand, capillary zone electrophoresis (CZE) is considered to be a powerful analytical technique that can be used effectively for the analysis of many pharmaceutical and biopharmaceutical compounds [5, 6]. It is also known for its excellent separation efficiency using a very small sample volume with consumption of small amount of aqueous buffers in a short run time. Accordingly, it is considered to be a superior green, environmentally-benign alternative to many conventional chromatographic methods [7, 8]. Furthermore, coupling of DAD detection with CZE permits the analysis at multiple wavelengths at a time, and thereby increase the sensitivity [9], also it helps to identify peaks according to their UV–*Visible spectra* with peak purity assessment [10].
Hypertension is one of the main risk factors for almost all cardiovascular disorders [11]. That is to say good control of blood pressure is a must and it is the main goal of all healthcare providers. Despite the availability of contemporary, efficient antihypertensive medications, the majority of patients continue to have inadequate blood pressure control. To achieve the therapeutic objectives, the majority of hypertension patients will require a combination of antihypertensive medications [12]. Hydrochlorothiazide (HCT), Fig. 1a, 6-chloro-3,4-dihydro-2H-1,2,4-benzothiadiazine-7sulfonamide 1,1-dioxide [13], it belongs to benzothiadiazine class of diuretics. It is an extensively utilized thiazide diuretic that combines with several antihypertensive drugs for effective treatment of hypertension [14]. Amiloride hydrochloride (AML), Fig. 1b, is 3,5-diamino-6-chloro-N-(diaminomethylidene) pyrazine-2-carboxamide;hydrochloride [13]. It is a potassium sparing diuretic commonly used in the treatment of hypertension in order to preserve normal serum potassium level [15]. Timolol maleate (TIM), Fig. 1c, with chemical name (2S)-1-[(1,1-Dimethylethyl)amino]-3-[[4-(morpholin-4-yl)-1,2,5-thiadiazol-3-yl]oxy]propan-2-ol (Z)-butenedioate [16], is a non-selective blocker of the beta-adrenergic receptor utilized effectively in the management of elevated blood pressure especially in patients with mild to moderate hypertension. It works by blocking β2 receptors in the blood vessels decreasing the peripheral vascular resistance leading to a decrease in the blood pressure [17]. The co-formulation of the three drugs together was found to have an enhanced antihypertensive effect [12].Fig. 1Chemical structure of a Hydrochlorothiazide (HCT), b amiloride hydrochloride (AML), c timolol maleate (TIM), d salamide (DSA) and e chlorothiazide (CT) *It is* known that hydrochlorothiazide purity is a challenging problem for the pharmaceutical industry [18, 19]. Salamide (DSA), Fig. 1d, 4-amino-6-chlorobenzene-1,3-disulphonamide and chlorothiazide (CT), Fig. 1e, 6-chloro-2H-1,2,4-benzothiadiazine-7-sulphonamide-1,1-dioxide are stated as hydrochlorothiazide official impurities with specified pharmacopeial limit [16]. It was reported that DSA is considered to be the main degradation product obtained upon hydrolysis of HCT [20], while CT is believed to have less diuretic activity compared to the parent drug [21].
Upon surveying the literature, no method is designated for the determination of the cited antihypertensive drugs simultaneously in their commercially available dosage form or even their determination along with some of their potential impurities. Only few methods were reported recently for the determination of the studied drugs either in their synthetic mixtures or laboratory prepared dosage form, including chromatographic method [22] and spectrophotometric method [23]. Additionally a chemometric method for the determination of hydrochlorothiazide, amiloride hydrochloride and timolol maleate in addition to atenolol has been published [24].
This method endeavors to establish new eco-friendly HPTLC-densitometry and capillary zone electrophoresis methods for the concurrent determination of AML, HCT and TIM in their commercially available pharmaceutical formulation and in the presence of DSA and CT, replacing the common hazards of conventional HPLC methods. Additionally, assessment of the proposed methods greenness using commonly and recently introduced evaluation tools; Eco-scale, NEMI, GAPI and AGREE.
## Reagents and materials
HPLC grade solvents were used, and water was double distilled. Orthophosphoric acid, hydrochloric acid, sodium tetraborate decahydrate and ammonia solution ($25\%$) were all obtained from Sigma Aldrich (Steinheim, Germany). Ethyl acetate and ethanol were purchased from Fisher scientific (UK).
Pure amiloride hydrochloride, with purity of 99.81 ± $1.51\%$, according to the official method [13], was purchased from Sigma Pharmaceuticals Industries (El Monofeya, Egypt). Pure hydrochlorothiazide was obtained from October Pharma (Cairo, Egypt) and according to the official method [13], its purity was found to be 100.24 ± $1.42\%$. Timolol Maleate was purchased from Orchidia Pharmaceutical Industries (Obour City, Egypt) with 99.93 ± $1.12\%$ purity according to the official method [16]. Hydrochlorothiazide impurities; salamide and chlorthiazide, were acquired from Sigma Aldrich Chemie (Steinheim, Germany). According to supplier certificate of analysis, their purities were reported to be 99.70 and $99.60\%$, respectively. Moducren® Tablets were manufactured by GERDA (Paris, France), Batch No. AJC 066, with labelled claim of 2.5 mg AML, 25 mg HCT and 10 mg TIM per tablet and were purchased from the French local market.
## For HPTLC-densitometric method
Samples were spotted as separate bands on 20 × 10 cm aluminum sheets pre-coated with silica gel 60 F254 (Merck, Darmstadt, Germany), using sample applicator, Linomat 5 (CAMAG, Muttenz, Switzerland) supplied with a 100.0 µL microsyringe. Bands are separated from one other and from the top and bottom-end of the plate by10 mm with band width of 6.0 mm. Using a CAMAG glass chamber previously saturated with 60 mL mobile phase composed of ethyl acetate–ethanol–water–ammonia (8.5:1:0.5:0.3, by volume), a Linear ascending development was performed over a distance of 8 cm then removed from the chamber and left to dry at room temperature. The separated bands were scanned using a CAMAG TLC scanner at 220.0 nm for AML, HCT, DSA and CT and 295.0 nm for TIM. Scanning was performed using reflectance-absorbance mode of measurement with deuterium lamp as a radiation source and slit dimension kept at 3 × 0.45 mm at scanning speed of 20 mm/s. WinCATS® software was used for densitometric evaluation and the output consist of a densito-grams and integrated peak area.
Standard stock solutions of AML, HCT and TIM (1.0 mg/mL) were prepared by weighing and dissolving 50 mg of each in three separate 50-mL measuring flask, and making the volume to mark using methanol as a solvent.
Stock standard solution of DSA and CT (100.0 µg/mL) were prepared, separately, by weighing 10 mg of each into 100-mL measuring flask and completing the volume using methanol.
Into a set of 10-mL measuring flasks, serial dilutions of the studied drugs were separately performed using methanol as a solvent to prepare working solutions covering the concentration range of 50.0–1000.0 µg/mL for AML, 100.0–1600.0 µg/mL for HCT, 100.0–1400.0 µg/mL for TIM and 5.0–100.0 µg/mL of each of DSA and CT. A volume of 10 µL of respective concentration was spotted, in triplicate, as separate bands and mentioned chromatographic conditions was followed as mentioned. Calibration curves were constructed relating the average obtained peak areas to the corresponding concentrations and then regression equations were computed.
An equivalent amount of two tablets was accurately weighted and transferred to 50-mL measuring flask. A volume of 25 mL methanol was further added and then sonicated for 15 min. The flask was finalized to the mark using methanol and then filtered to get a concentration of 100.0 µg/mL for AML, 1000.0 µg/mL for HCT and 400.0 µg/mL for TIM. A 10 µL aliquot equivalent to 1.0 µg/band AML, (10.0 µg/band) HCT and (4.0 µg/band) TIM were spotted, and chromatographic conditions were followed.
Minimizing the environmental impact while maintaining method efficiency was one of the key issues faced throughout method development. For the separation of the aforementioned drugs together with HCT impurities, a number of development systems with various compositions and ratios were tested avoiding the most harmful solvents, such as benzene and toluene. Among the tried systems: ethyl acetate–methanol–ammonia with different composition ratios. Worthy separation was achieved between AML and the rest of the drugs, but no separation was observed between the two impurities DSA and CT, with bad resolution of HCT and TIM bands. Trying to replace ammonia with glacial acetic acid, AML peak was observed very close to baseline with bad peak symmetry. Other systems consisting of ethyl acetate–ethanol–ammonia and ethyl acetate–ethanol–water–ammonia in different ratios were tried. It was observed that good resolution and enhanced peak symmetry of the drug mixture and impurities was obtained upon using system consisting of ethyl acetate–ethanol–water–ammonia (8.5:1:0.5:0.3, by volume) as shown in Fig. 2. For densitometric measurement, various scanning wavelengths were tried for optimum quantification including (220.0, 270.0 and 295.0 nm), where 220.0 nm was selected for AML, HCT, DSA and CT determination and 295.0 nm for TIM. These two selected wavelengths found to have the highest sensitivity for the determination of the cited analytes and minimum baseline noise. Fig. 2HPTLC-densitogram of mixture of AML (1.0 µg/band), TIM (1.0 µg/band), HCT (1.0 µg/band), DSA (0.05 µg/band), CT (0.05 µg/band) using ethyl acetate–ethanol–water–ammonia (8.5:1.0:0.5:0.3, by volume) as a developing system at 220.0 nm
## For CZE-DAD method
Using an Agilent 7100 CE instrument supplied with an autosampler, and a diode array detector, electrophoretic separations were performed. The management of the CE system, data collection, and inspection were done using the ChemStation software (Agilent Technologies, Germany). Fused silica capillary, purchased from Agilent Technologies (USA), of total length 48.5 cm, 40 cm effective length and internal diameter of 50 µm was utilized. Separation was achieved using optimized background electrolyte (BGE) of 40.0 mM borate buffer at pH = 9.0, applied voltage of + 15 kV and detection wavelength of 200.0 nm. Each run pass through three main steps; first step is pre-conditioning where 0.1 mol/L NaOH was flushed for 1 min followed by deionized water and background electrolyte (BEG), in order, for 3 min each. Second step is sample injection where sample is injected hydro-dynamically at 50 mbar for 5 s. Finally, the capillary is flushed with deionized water for 5 min as post-run step. During working day, the capillary was flushed with running buffer for 120 s between each two sequential injections. To preserve the repeatability of the injections from run to run, buffer vials were replenished after every five subsequent runs.
Stock standard solutions of AML, HCT, TIM (1.0 mg/mL) and DSA (100.0 µg/mL), each was prepared separately as mentioned above using ethanol as solvent.
Aliquots of stock standard solutions AML, HCT, TIM and DSA were, separately, transferred into a series of 10-mL measuring flask and the volume was made up to the mark using water as a solvent to cover a concentration range of 20.0–160.0 µg/mL for AML, 10.0–200.0 µg/mL for HCT, 10.0–120.0 µg/mL for TIM and 10.0–100.0 µg/mL for DSA. Samples were injected under hydrodynamic mode. Calibration curves were plotted relating the resulting corrected peak areas to the corresponding concentrations of each component and regression equations were then determined.
An average weight of two tablets was transferred into 50-mL measuring flask using 25 mL ethanol, and the sample prepared solution was then sonicated (15 min). The volume was brought to the mark with the same solvent and subsequently filtered to obtain a concentration of 100.0 µg/mL for AML, 1000.0 µg/mL for HCT and 400.0 µg/mL for TIM. Appropriate aliquots from the sample prepared solution were then transferred into 10-mL measuring flasks and the volume was made up to the mark using water to obtain a concentration within the linearity range of each drug.
Additionally, standard addition technique was done to assess methods’ accuracy.
In CZE, buffer type and concentration, pH and applied voltage play a chef role in electrophoretic separation optimization. Two buffers were tested as background electrolyte, namely; borate and phosphate buffers. Poor electrophoretic separation of cited compounds was achieved using phosphate buffer. Therefore, borate buffer was chosen as BGE. Different pH values were tried taking in consideration the physico-chemical properties of studied analytes. It was observed that upon using acidic pH, poor separation was obtained between AML, HCT and TIM. Upon increasing pH to more basic values separation was greatly enhanced. Different borate buffer concentrations were tried under constant instrumental conditions. Finally, it was found that 40.0 mM borate buffer of pH 9.0 give better peak shapes with good resolution. Upon optimization of the applied voltage, applying voltage of + 20 kV resulted in increased current value and peaks deterioration, while upon decreasing voltage value to + 12 kV, increased run time was observed. Applying voltage of + 15 kV gave better separation with short run time. The dilution solvent used to prepare the samples was another significant factor that was investigated in order to improve the method’s sensitivity. Despite adjusting the pH, buffer salt, strength, and applied voltage, a problem of the co-elution of TIM and AML with ethanol (if used as a dilution solvent) was encountered with a decrease in sensitivity as well as a worsening of the peak shapes. Even though CT could be better solubilized and well resolved, Additional file 1: Fig. S1. As a result, the procedure was shifted to use water as a diluent in order to alleviate this issue, an improved peak shape of AML and TIM with better resolution was observed. Unfortunately, the CT peak abruptly vanished, demonstrating the need for an organic solvent for CT sensitive measurement. Accordingly, CT was excluded from further practical studies. Therefore, water was chosen as a solvent in order to efficiently determine the major compounds; AML, TIM, and HCT as well as DSA, Fig. 3. Finally, different detection wavelengths were investigated including 200.0 nm, 210.0 nm, 225.0 nm, 240.0 nm and 260.0 nm and it was found that best sensitivity was achieved at 200.0 nm. Fig. 3CZE-DAD electropherogram showing separation of mixture of AML (30.0 µg/mL), TIM (30.0 µg/mL), HCT (50.0 µg/mL), and DSA (10.0 µg/mL) using an uncoated fused-silica capillary with a total length of 48.5 cm and an effective length of 40 cm (50 μm i.d.); UV detection at 200.0 nm; sample injection: 50 mbar for 5 s; an applied voltage of + 15.0 kV; and a BGE of borate buffer (pH 9.0; 40.0 mM)
## Assay of pharmaceutical formulation
Ten tablets were weighed separately, powdered and uniformly mixed.
Using a straightforward extraction approach, the improved and validated procedures were successively employed for the study of the specified drugs combination in their commercially available pharmaceutical formulation, Table 4. Good results with minimal sample preparation were obtained, ensuring the reliability of the proposed methods for the determination of cited drugs in the presence of from the common tablets’ excipients without any conflict. Additionally, standard addition technique was performed and the proposed methods’ validity was further verified, Table 4.Table 4Results obtained by applying the proposed HPTLC-densitometry method and CZE method for the determination of amiloride hydrochloride, timolol maleate and hydrochlorothiazide in moducren® tablets and application of standard addition techniquePharmaceutical formulationHPTLC-densitometryCZE-DAD methodDrug%Found ± SDaStandard addition technique%Found ± SDaStandard addition techniqueClaimed (µg/band)Pure added (µg/band)%R of the pure addedbClaimed (μg/mL)Pure added (μg/mL)%R of the pure added amountbModucren® tablets B.N. AJC 066 (each tablet is labelled to contain 2.5 mg AML, 10 mg TIM and 25 mg HCT)AML97.96 ± 1.251.00.597.2098.34 ± 1.1040.020.098.951.098.6140.0100.172.099.9880.0100.22Mean ± SD98.59 ± 1.39Mean ± SD99.78 ± 0.72HCT99.76 ± 0.995.02.597.53100.47 ± 1.1850.025.0100.695.0100.2950.0101.1110.099.78100.099.26Mean ± SD99.20 ± 1.47Mean ± SD100.35 ± 0.97TIM100.77 ± 1.104.02.0100.4399.26 ± 1.0240.020.0100.134.099.5240.098.408.0101.4380.099.79Mean ± SD99.20 ± 1.47Mean ± SD99.44 ± 0.92aAverage of five determinationsbAverage of three determinations Additionally, the proposed methods are statistically compared to the different official ones for the determination of the cited drugs in their pure forms [13, 16]. As shown in Additional file 1: Table S1, the calculated student’s t and F-test demonstrate that no significant differences were obtained in terms of both precision and accuracy.
## Results and discussion
Separation-based techniques are well-established and irreplaceable techniques in worldwide quality control laboratories for routine drug assay. The literature survey revealed the lack of a reported analytical method for the simultaneous determination and purity testing of the studied drugs in their newly introduced pharmaceutical formulation. The presented contribution is devoted to spot the light on the analytical performance and opportunities existing for HPTLC-Densitometry and CZE-DAD (for the first time) in cited drugs' analysis and impurity profiling purposes.
## Assessment of the proposed methods’ greenness
Recently, many assessment tools were developed to evaluate the environmental impact of analytical method [25–28]. One of the simplest and oldest qualitative assessment tools is NEMI. It is represented as circle divided into four fields, each field represents different aspect of the analytical method [3]. It evaluates the analytical methodology in four key terms reflected in the four fields of the circle as follow: [1] PBT (Persistent, bio-accumulative and toxicity) of the chemicals used, [2] hazards of the used solvents, [3] corrosiveness and [4] the amount of generated wastes. The field is shaded green if it met the greenness requirements. The suggested CZE-DAD method is said to be a greener one since it exhibits the fortunately prevalent green color of the four fields, as none of the used solvents neither appears on the PBT list nor in the Environmental Protection Agency (EPA) Toxic Release Inventory (TRI) chemicals list [29] and the pH of the used systems lies in the acceptable range (between 2 and 12). Additionally, the amount of the generated wastes is less than 50 g per analyzed sample, Table 1. On the other hand, the proposed HPTLC-densitometric method displays 3 of the 4 fields as green shaded, indicating the use of hazardous solvents in the developing system, Table 1. Although, NEMI is considered a simple, easy to read assessment tool, it only provides qualitative information about the proposed method greenness besides ignoring some important aspects in analytical process like energy consumption and health hazards. Table 1Greenness assessment of the two proposed separation methods by analytical eco-scale, NEMI, GAPI and AGREE toolsFor HPTLC-densitometric methodEco-scale assessmentNEMI pictogramGAPI assessmentAGREE assessmentReagentsPenalty points (PPs) Ammonia ($25\%$)6 Water0 Ethanol4 Ethyl acetate4Instrument Energy consumption1 Occupational hazard3 Waste6Total PPs24Analytical eco-scale total score76CommentExcellent green analysisFor CZE-DAD methodEco-scale assessmentNEMI pictogramGAPI assessmentAGREE assessmentReagentsPenalty points (PPs) Sodium tetraborate decahydrate4 Water0Instrument Energy consumption2 Occupational hazard0 Waste4Total PPs10Analytical eco-scale total score90CommentExcellent green analysis Analytical Eco-scale system is a comprehensive semi-quantitative system that assigns penalty scores to each parameter, including the quantity and danger of the employed reagents, waste production, energy use, different waste treatment methods, and any potential occupational concerns, in the developed method that is not in agreement with the ideal green analysis [30]. Then, subtracting the total penalty score from a base of 100 (that is assigned for the ideal green method). An unsatisfactory green analysis is one that receives a score of less than 50 whereas an exceptional green analysis receives a score of greater than 75. The eco-scale scores displayed in Table 1 indicate that the two developed methods follow the green analytical chemistry concepts (GAC) and has an excellent green practice. The obtained scores reinforced the NEMI findings, ranking the proposed CZE-DAD method above the HPTLC-densitometric one with a higher score of 90 and the HPTLC-densitometric method with a lower score of 76, Table 1. Despite the fact that eco-scale system takes into consideration more experimental parameters than NEMI, it is still considered as semi-quantitative tool as the final score lacks sufficient information about the nature of hazards and thereby can’t be used to identify the analytical method’s drawbacks.
Additionally, Green Analytical Procedure Index (GAPI) is a recently designed tool that evaluate the greenness of the entire analytical method starting initially from sample preparation and collection till the final step of determination [31]. It is represented by a pictogram consisting of five pentagrams using three different color codes: green, yellow and red which can be translated to low, medium and elevated environmental influence of each parameter. The developed CZE-DAD method possess higher green shaded sections [5] and less red shaded ones [2] compared to the developed HPTLC-densitometric method which shows [4] green shaded sections and [3] red shaded ones, Table 1. GAPI tool provides a full evaluation of the analytical methodology, giving detailed information about every aspect of the analytical method and thus, facilitate identifying areas that still need to be improved in terms of greenness. Even though the primary disadvantage of the GAPI tool is that it is more complicated than NEMI and eco-scale ones.
AGREE is a downloadable software newly introduced to assess the environmental impact of the developed analytical method taking in consideration the 12 principle of the GAC [32]. The result is transferred into a pictogram with 12 sector and a final score appear in the middle scaling from 0 to 1. Each sector of the pictogram is colored using a color scale system from green to red according to the environmental impact. The best approach yields a score of 1, using the color dark green. The developed methodologies' AGREE pictograms in Table 1 demonstrate their greenness with an overall score of 0.63 and 0.61, for the proposed HPTLC-densitometry and CZE-DAD methods, in order. As a result, AGREE metric is regarded as being user-friendly, thorough, simple to use, and extremely quick.
Briefly, the usefulness of the proposed separation-based approaches depends on their capacity to reduce the usage and consumption of the solvents and instrumental energy. Since they may be used safely and without endangering the environment or the analyst, the suggested procedures are referred to as “green approaches.” Additionally, the benefit of small volumes of aqueous buffers usage in CZE-DAD is adequately demonstrated by the high score attained by the analytical eco-scale as well as by the predominance of green color in both NEMI and GAPI pictograms. Although the CZE-DAD method is thought to be more environmentally friendly, it has certain drawbacks, such as the requirement for expensive and sophisticated instrumentation and well-trained operator. In addition, CZE-DAD method failed to simultaneously determine CT with the other four compounds. On the other hand, HPTLC-densitometry is believed to be more straightforward with high sample throughput and independent on costly instrumentation or solvents with the unique capability of the determination of the cited drugs along with HCT impurities, CT and DSA.
## Validation of the proposed methods
Validation of the developed methods was conducted according to the ICH guidelines [33] using the finally optimized experimental conditions, Table 2.Table 2Regression and validation parameters of the proposed HPTLC-densitometry and CZE-DAD methodMethod parameterHPTLC-densitometric methodCZE-DAD methodAMLHCTTIMDSACTAMLHCTTIMDSARange (µg/band for HPTLC, and µg/mL for CZE)0.5–10.01.0–16.01.0–14.00.05–1.00.05–1.020.0–160.010.0–200.010.0–120.010.0–100.0Slope (b)a2160.7–2303.3––0.16960.19930.09230.2814Coefficient 1 (b1)b– − 85.058– − 11,534 − 1790.9––––Coefficient 2 (b2)b–3363.9–25,3586175.2––––Intercept (a)a, b1793.810,4045197 − 478.24161.031.89583.26170.65330.5594Correlation Coefficient (r)0.99990.99980.99980.99980.99980.99970.99980.99970.9996Accuracy (Mean ± SD)100.81 ± 0.8999.90 ± 1.59100.10 ± 1.3999.44 ± 1.36100.53 ± 1.02100.16 ± 1.6599.67 ± 1.1399.99 ± 1.75100.02 ± 1.21Precision(± %RSD)c0.800.470.680.640.300.881.290.620.99(± %RSD)d1.011.440.811.631.031.101.641.431.74LODe (µg/band for HPTLC, and µg/mL for CZE)–––0.010.01–––2.94LOQe (µg/band for HPTLC, and µg/mL for CZE)–––0.030.02–––8.92Robustnessf1.211.431.111.310.981.761.451.781.53aRegression equation for CZE-DAD: A = a + bc, where ‘A’ is the average corrected peak area and ‘c’ is the concentration (μg/mL). While for HPTLC-densitometry: A = a + bc, where ‘A’ is the average peak area and ‘c’ is the concentration (μg/band)bCoefficient 1 and 2 are the coefficients of x2 and x, respectively. Following a polynomial regression: A = b1x2 + b2x + a, where ‘A’ is the average peak area, ‘c’ is the concentration (μg/band), ‘b1’ and ‘b2’ are coefficients 1 and 2, respectively and ‘a’ is the interceptcIntra-day precision [average of three different concentration of three replicates each ($$n = 9$$) within the same day], for HPTLC the concentrations were (2.0, 4.0, 6.0 µg/band) for AML, (4.0, 6.0, 8.0 µg/band) for TIM, (8.0, 10.0, 14.0 µg/band) for HCT and (0.2, 0.5, 1.0 µg/band) for DSA and CT. For CZE: the concentrations were: (80.0, 100.0, 120.0 µg/mL) for AML, (80.0, 100.0, 120.0 µg/mL) for TIM, (140.0, 160.0, 180.0 µg/mL) for HCT and (40.0, 60.0, 100.0 µg/mL) for DSA and CTdInter-day precision [average of three different concentration of three replicates each ($$n = 9$$) repeated on three successive days], the concentrations were the same as in intra-day precisioneLOD and LOQ are calculated according to ICH, 3.3 × SD of the residuals/slope and 10 × SD of the residuals/slope, respectivelyfFor HPTLC−densitometry: average of the change in wavelength (±1 nm) and saturation time (± 5 min). For CZE−DAD: average of the change in applied voltage (± 1 kV)
## Linearity
Linearity of the proposed methods was estimated by plotting calibration curves relating the peak area or corrected peak area to the corresponding concentration of each component and regression parameters were computed Table 2.
## Limits of detection (LOD) and limits of quantification (LOQ)
Limits of detection (LOD) and limits of quantification (LOQ) of HCT officially specified impurities were evaluated in accordance to the ICH guidelines using the standard deviation of residuals and slope.
## Selectivity
The selectivity of the proposed methods is guaranteed by the successful resolution of the studied compounds as shown in Figs. 2 and 3. Additionally, the good percentage recoveries obtained for the analysis of AML, HCT and TIM in their co-formulated tablets suggest the absence of any interference from the common tablet excipients, Table 4 and Additional file 1: Fig. S2.
Peak identity and purity were confirmed using the winCATS spectral correlation tool for HPTLC-densitometric method and online by DAD for CZE-DAD method. The purity index of the studied analytes did not surpass the threshold limits in any of the analyzed samples along with the superimposed UV spectra, indicating the homogeneity of the separated peaks.
## Accuracy
For the estimation of the developed methods accuracy, a minimum of five different pure samples of each component covering the specified calibration range were analyzed in triplicates. The developed methods are found to exhibit acceptable accuracy based on the calculated percentage recoveries, Table 2.
## Precision
To evaluate method precision, three discrete levels of concentration of each component were analyzed in triplicates in the same day and on three consecutive days. The obtained results indicate good method precision as reinforced by RSD% values less than 2, Table 2.
## Robustness
Reliability of the developed method was checked by inducing minor changes in the separation conditions and then monitoring method performance. For HPTLC-densitometric method, changing the scanning wavelength (± 1 nm) or saturation time of the development chamber (± 5 min). For CZE-DAD method, changing the applied voltage (15 ± 1 kV). The low % RSD values obtained indicate robustness of the proposed methods, Table 2.
## System suitability parameters
Parameters of system suitability were then checked [13, 34], to assure the developed methods’ performance and the results were summarized in Table 3.Table 3System suitability parameters of the proposed HPTLC-densitomety and CZE-DAD methodsMethodParameterCTAMLTIMHCTDSAReference value [34]HPTLC-densitometryRetardation factor (Rf) ± 0.02a0.180.280.440.640.74Capacity factor (k′)b4.562.571.270.560.35Selectivity (α)c1.772.022.271.60 > 1Resolution (Rs)d1.892.732.811.76Rs > 1.5Tailing factor (T)0.991.000.921.011.12T ≤ 2Number of theoretical plates (N)246.49317.48825.25855.53816.49ParameterTIMAMLDSAHCTReference value [13]CZE-DADSelectivity (α)e1.121.941.18 > 1Resolution (Rs)e2.0024.005.70Rs > 1.5Tailing factor (T)1.151.121.031.12T ≤ 2Number of theoretical plates (N)f92164993.7851,07611,320.96N ˃ 2000Number of theoretical plates per meter (TPM)g19,002.0610,296.44105,311.3423,342.18Migration time (min ± 0.2)2.152.405.116.02aRetardation factor (Rf) = distance travelled by the analyte/distance travelled by the solvent frontbCapacity factor (k′) = (1 − Rf)/RfcSelectivity (α) = k′2/k′1 calculated for each of two successive peaksdResolution (Rs) = z2 − z$\frac{1}{0.5}$ (w1 + w2), where z2 − z1 is the distance between two adjacent spot centers and w is the peak width calculated for each of two successive peakseSelectivity and resolution as extracted from the softwarefNumber of theoretical plates (N) = 16(TR/W)2gNumber of theoretical plates per meter (TPM) = [1600(TR/W)2]/L, where L is the total capillary length in cm
## Conclusion
A fixed dose combination of amiloride hydrochloride, hydrochlorothiazide and timolol maleate is presented as a new therapy for hypertension. In the current work, sustainable, reliable and robust separation methods were developed for quantitative estimation of AML, HCT and TIM together with HCT related impurities, CT and DSA, presenting the first HPTLC-densitometry and capillary zone electrophoresis methods to be reported for simultaneous determination of this combination therapy. The usage of harmless and less dangerous solvents was carefully considered. The developed methods were successively applied for the determination and purity assessment of the cited drugs in Moducren® Tablets. The greenness of the proposed methods was comprehensively evaluated using recently introduced assessment tools. Clearly, the established methods display excellent analytical performance with low environmental impact which encourage their use for routine analysis of the cited drugs either in their pure powder form or in pharmaceutical formulation in quality control laboratories.
## Supplementary Information
Additional file 1:Table S1. Statistical comparison of the results obtained by the proposed HPTLC-densitometry and CZE-DAD method and those of the official methods for the analysis of pure AML, HCT and TIM. Figure S1. CZE-DAD electropherogram showing separation of mixture of AML (30.0 μg/mL), TIM (30.0 μg/mL), HCT (50.0 μg/mL), DSA (10.0 μg /mL) and CT (10.0 μg /mL) using ethanol as a diluent and an uncoated fused-silica capillary with a total length of 48.5 cm and an effective length of 40 cm (50 μm i.d); UV detection at 200.0 nm; sample injection: 50 mbar for 5 s; an applied voltage of +15.0 kV; and a BGE of borate buffer (pH 9.0; 40.0 mM). Figure S2: (a) HPTLC-densitogram of Moducren® Tablets extract, using ethyl acetate-ethanol-water-ammonia (8.5:1.0:0.5:0.3, by volume) as a developing system at 220.0 nm. ( b) CZE-DAD electropherogram of Moducren® Tablets extract, using an uncoated fused-silica capillary with a total length of 48.5 cm and an effective length of 40 cm (50 μm i.d); UV detection at 200.0 nm; sample injection: 50 mbar for 5 s; an applied voltage of +15.0 kV; and a BGE of borate buffer (pH 9.0; 40.0 mM).
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|
---
title: 'Foot health and quality of life among adults in Riyadh, Saudi Arabia: a cross-sectional
study'
authors:
- Abdulaziz Almaawi
- Hashim Alqarni
- Ahmed K. Thallaj
- Mohammed Alhuqbani
- Zyad Aldosari
- Omar Aldosari
- Naif Alsaber
journal: Journal of Orthopaedic Surgery and Research
year: 2023
pmcid: PMC10007839
doi: 10.1186/s13018-023-03677-w
license: CC BY 4.0
---
# Foot health and quality of life among adults in Riyadh, Saudi Arabia: a cross-sectional study
## Abstract
### Background
Foot conditions are frequent among the Saudi population. However, little is known regarding the effects of foot health on quality of life among the general Saudi population. This study aimed to assess foot health status, general health, and quality of life among the population of Riyadh using the Foot Health Status Questionnaire (FHSQ).
### Methods
In this cross-sectional study, out of the total number of participants approached, using a preset questionnaire, by trained medical students to participate in this study, 398 met the inclusion criteria. The questionnaire started with an informed consent followed by a set of questions regarding the sociodemographic and past medical characteristics of the participants. Foot health and overall health were assessed using a FHSQ.
### Results
A statistically significant positive correlation was observed between all the FHSQ domains, except for footwear. The strongest correlation was observed between foot pain and foot function, foot pain and general foot health, and foot function and general foot health. A statistically significant positive correlation was observed between general foot health and general health, vitality, social function. Our results also showed that foot pain, general foot health, vitality, and social function scores were significantly lower in women as compared to men.
### Conclusion
Significant positive correlation was observed between poor foot health and declining quality of life; thus, it is crucial to increase society’s awareness of the importance of medical foot care and continuous follow-up and consequences if left unrecognized and untreated. This is a major domain that can improve the well-being and quality of life of a population.
## Background
The feet are the most distinguishable enablers of bipedal locomotion. Bipedalism is a consequence of biological selection that allows greater freedom of upper limb motility, with the unintended sequelae of exerting more mechanical pressure on the lower body parts. The prevalence of degenerative joint diseases (DJDs) in weight-bearing joints suggests that human locomotion renders humans more susceptible to degenerative processes, a fact that is supplemented by the lower prevalence of DJDs in quadrupedal primates [1]. Taken together, it seems that humans are more inherently predisposed to an accelerated deterioration of locomotion, and given the incontestable importance of mobility on the quality of life (QoL), a study on foot health practices is therefore deemed warranted [2, 3].
To the best of our knowledge, no study is available regarding the effects of foot health on QoL among the general Saudi population residing in Riyadh; further, there is also a need to establish a baseline epidemiological basis for future preventive health policies. In addition, several studies are available showing the association between the disabling effects of chronic foot conditions, particularly diabetic neuropathy, on the QoL of patients with diabetes [4].
This study utilized the Foot Health Status Questionnaire (FHSQ), which is a validated tool to assess foot health in relation to QoL [5, 6]. The tool assesses the following four subscales: foot pain, foot function, footwear, and general foot health, with higher scores reflecting a more optimal status of foot health. Establishing the epidemiological background of the effect of health on the QoL is a prerequisite for the development of effective prevention policies and lays the foundation for the generation of evidence-based recommendations toward the improvement of foot health, and ultimately, health-related QoL. Our study aligns with the strategic objectives of transforming healthcare as part of Saudi Arabia’s Vision 2030 National Transformation Program aimed at improving preventive healthcare in the region [7].
## Study setting and population
This cross-sectional study was conducted between March 2022 and July 2022 to examine the adult population of Riyadh, Saudi Arabia. The participants were the citizens of Riyadh city and were approached at two locations: King Khaled University Hospital (KKUH) and a shopping mall in Riyadh. The participants were adults aged ≥ 16 years who were able to fully read and understand the questionnaire and write the appropriate response. Any participant who was under the set age or was suffering from cognitive impairment or difficulty in adequately understanding the questions was excluded from the study.
## Sample size calculation
The sample size was calculated based on the 2017 estimated population size of Riyadh Province of 8,216,284 people [8] with a $5\%$ margin of error and $95\%$ confidence level. Due to the limited studies on the proportion of good overall foot health status, especially in the region, the estimated proportion was set at $50\%$ to maximize the number of enrolled participants. The final sample size included 385 participants. More than 500 participants were approached to participate in the study to compensate for refusals and incomplete responses by some participants.
## Procedure
Four hundred and ten participants agreed to participate in the study and were approached by trained medical students using a preset questionnaire designed to measure foot health status; of these, 398 met the inclusion criteria. The medical students remained with the participants throughout the answering process to ensure a full understanding of the participants for every question written. The questionnaire started with informed consent, followed by a set of questions regarding foot health and sociodemographic and past medical characteristics of the participants. Foot health and overall health were assessed using the FHSQ. The FHSQ is a self-administered questionnaire that evaluates health-related QoL [6]. It is a validated tool intended specifically for the foot, and includes three sections. The first section included 13 items related to four foot health-related subcategories: foot function, foot pain, footwear, and general foot health (Table 1). The second section contained items related to the four subscales of overall health. The third section collected sociodemographic information. The questionnaire was available in English and was translated into Arabic by two translators. The first translator had a background in instrument construction, medical language, and clinical orthopedics. The second translator had no medical background or previous experience with the instrument construct. Both versions were reviewed by specialists in the field, and following consensus, the final version was adapted. Participants were provided with a suitable language based on their preferences. To ensure participants’ anonymity, no names or IDs were identified. Table 1Domains of foot health assessed by the Foot Health Status QuestionnaireDomainItemTheoretical constructMeaning of lowest score [0]Meaning of highest score [100]Foot pain4Type, severity, and duration. Evaluation of foot pain in terms of type of pain, severity, and durationExtreme pain in the feet and significant if acute in natureFree from pain, no discomfortFoot function4Evaluation of the feet in terms of impact on physical functionsSeverely limited for the performance of numerous physical activities due to their feet, such as walking, working and moving aboutPatients are able to carry out all physical activities desired, such as walking, working and climbing stairsGeneral foot health2Self-perception of the feet (assessment of body image with respect to feet)Perception of poor condition and status of the feetPerception of excellent condition and status of the feetFootwear3Lifestyle relating to footwear and feetGreat limitations to find suitable footwearNo problem obtaining suitable footwear. No limitations with respect to footwear
## FHSQ outcome measurement
This is a validated [6, 9, 10] self-administered tool on health-related QoL intended specifically for the foot. It consists of three sections that assess foot-specific and general health using version 1.03 of the FHSQ [6]. The first section consisted of 13 questions regarding four foot health-related domains: foot pain (four questions), foot function (four questions), footwear (three questions), and general foot health (two questions) (Table 1). This section has shown a high degree of content, criterion, and construct validity (Cronbach’s α = 0.89–0.95), as well as high retest reliability (interclass correlation coefficient = 0.74–0.92) [9]. It has been shown to be the most appropriate measurement of foot health-related QoL for a population with foot pain or pathological foot conditions and is frequently used in research and clinical settings [10–13]. The second section was measuring the health-related quality of life which was represented by: general health, physical activity, social capacity, and vigor, which were largely adapted from the Medical Outcomes Study 36-item Short-Form Health Survey [14]. Each domain was computed using statistical software and given a score ranging from 0 to 100. A score of 0 indicated a poor condition, while a score of 100 indicated the best possible condition.
## Sociodemographic and descriptive data
The sociodemographic section included age, gender, smoking status, general physical health, health insurance status, and completion of an educational certificate since leaving high school. Participants were also asked about the total number of illnesses that they were being treated for to provide a measure of comorbidity.
## Ethical consideration
This study was approved (No. E22-6679) by the institutional review board of King Saud University Medical City, Riyadh, Saudi Arabia. Participants were informed of the aims and objectives of the study, and informed consent was obtained from all participants after reassuring that their confidentiality was maintained by the exclusion of any identifying data from the questionnaire. All participants were informed of their right to withdraw from the study at any time.
## Statistical analysis
Statistical analyses were performed using R version 3.6.3. Categorical variables are summarized as counts and percentages. Continuous variables are summarized as medians ± interquartile ranges. A confirmatory analysis was performed to validate the underlying structure of the items related to foot health. Loadings > 0.5 were deemed acceptable [15]. Linear regression was used to assess the factors associated with each of the eight dimensions of the FHSQ. Cronbach’s alpha was used to assess the reliability of the foot health questionnaire [16]. Hypothesis testing was performed at the $5\%$ significance level.
## Results
The study sample consisted of 398 participants. Of these, $56.3\%$ were males, and $43.7\%$ were females. After leaving school, more than three-quarters of the respondents obtained an educational degree ($79.9\%$). Regarding medication use, $80.3\%$ of the respondents were medically free and $21.1\%$ reported receiving at least one medication for a chronic medical condition. One-quarter ($27.1\%$) of the respondents reported smoking and more than one-third ($36.9\%$) reported exercising regularly. Less than half of the respondents had private health insurance ($43\%$) (Table 2).Table 2Descriptive statistics for the study sample[ALL]$$n = 398$$Age28.3 (9.33)GenderMale224 ($56.3\%$)Female174 ($43.7\%$)MedicationsNo meds314 ($80.3\%$)One condition59 ($15.1\%$)Two conditions18 ($4.60\%$)SmokingNo290 ($72.9\%$)Yes108 ($27.1\%$)Regular exerciseNo251 ($63.1\%$)Yes147 ($36.9\%$)Private health insuranceNo227 ($57.0\%$)Yes171 ($43.0\%$)Educational qualification since leaving schoolNo80 ($20.1\%$)Yes318 ($79.9\%$)Data was summarized using counts and percentages The results showed that all loadings were > 0.7 (Fig. 1), which was considered excellent. The only exception was the functional domain, although the loadings were > 0.5, which was still above the minimally acceptable threshold of 0.5. A statistically significant positive correlation was observed between the four dimensions of the FHSQ foot health. The correlation was the lowest between footwear and the other three dimensions (Table 3).Fig. 1Confirmatory factor analysis resultsTable 3Coefficients of correlation between FHSQ questionnaire scalesPhysical functionFootwearSocial FunctionVitalityGeneral healthFoot FunctionGeneral Foot healthFoot painFoot pain0.134**0.251***0.277***0.282***0.289***0.580***0.625***–General Foot health0.156**0.303***0.368***0.381***0.359***0.572***––Foot Function0.226***0.175***0.257***0.248***0.360***–––General health0.303***0.0630.265***0.358***––––Vitality0.156**0.0950.401***–––––Social Function0.139**0.246***––––––Footwear0.038–––––––Physical function––––––––*$P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$ The reliability of the four dimensions was assessed using Cronbach’s alpha (α) and was > 0.7 for all four scales, which was above the minimum acceptable threshold (Table 4).Table 4Item internal consistency and factor loadingsFoot pain domainPainFunctionFootwearOverall healthWhat level of foot pain have you had during the past week?0.735How often have you had foot pain?0.886How often did your feet ache?0.893How often did you get sharp pains in your feet?0.759Foot function domainHave your feet caused you to have difficulties in your work or activities?0.855Were you limited in the kind of work you could do because of your feet?0.657How much does your foot health limit you in walking?0.519How much does your foot health limit you from climbing stairs?0.564Footwear domainIt is hard to find shoes that do not hurt my feet0.771I have difficulty finding shoes that fit my feet0.896I am limited in the number of shoes I can wear0.788General foot health domainHow would you rate your overall foot health?0.878In general, what condition would you say your feet are in?0.877Cronbach’s alpha0.890.80.860.87 A statistically significant positive correlation was observed between all the FHSQ domains, except for footwear. No association was observed between footwear and the three overall health dimensions of vitality, general health, and physical function. The highest correlation was observed between foot pain and foot function ($r = 0.58$, $P \leq 0.001$), foot pain and general foot health ($r = 0.625$, $P \leq 0.001$), and foot function and general foot health ($r = 0.572$, $P \leq 0.001$). A statistically significant positive correlation was observed between foot health and general health ($r = 0.359$, $P \leq 0.001$) (Table 3).
A statistically significant decreasing linear trend was observed in foot pain, foot function, general foot health, and footwear scores with increasing number of medications (Table 5). Smoking was associated with a lower score in the foot pain domain (B = −6.12, $$P \leq 0.023$$), but not in other domains. Neither health insurance nor regular exercise was significantly associated with scores for any domain. Better education was associated with a higher score in the foot function domain ($B = 8.75$, $$P \leq 0.001$$), but not in the other domains. Table 5Factors associated with foot healthPredictorsFoot painFoot functionGeneral foot healthFootwearB ($95\%$ CI)PB ($95\%$ CI)PB ($95\%$ CI)PB ($95\%$ CI)PGenderMaleReferenceReferenceReferenceReferenceFemale − 8.48 (− 13.21 to − 3.75) < 0.001 − 3.12 (− 7.34 to 1.11)0.148 − 6.96 (− 11.42 to − 2.51)0.002 − 5.80 (− 12.29 to 0.69)0.079MedicationsNo medsReferenceReferenceReferenceReferenceOne condition − 13.19 (− 19.36 to − 7.01) < 0.001 − 6.78 (− 12.30 to − 1.27)0.016 − 7.99 (− 13.81 to − 2.18)0.007 − 6.36 (− 14.82 to 2.11)0.141Two conditions − 17.30 (− 28.03 to − 6.57)0.002 − 11.33 (− 20.92 to − 1.74)0.021 − 20.15 (− 30.25 to − 10.05) < 0.001 − 16.29 (− 31.01 to − 1.58)0.030SmokingNoReferenceReferenceReferenceReferenceYes − 6.12 (− 11.38 to − 0.86)0.023 − 3.03 (− 7.73 to 1.67)0.206 − 2.61 (− 7.57 to 2.34)0.300 − 2.92 (− 10.13 to 4.30)0.427Regular exerciseNoReferenceReferenceReferenceReferenceYes − 2.49 (− 7.21 to 2.23)0.300 − 1.23 (− 5.45 to 2.98)0.5651.16 (− 3.28 to 5.61)0.6070.30 (− 6.17 to 6.77)0.927Health insuranceNoReferenceReferenceReferenceReferenceYes0.60 (− 3.94 to 5.15)0.794 − 0.25 (− 4.31 to 3.82)0.905 − 2.85 (− 7.12 to 1.43)0.192 − 6.01 (− 12.24 to 0.22)0.059EducationNoReferenceReferenceReferenceReferenceYes5.16 (− 0.48 to 10.79)0.0738.75 (3.72 to 13.79)0.0014.64 (− 0.66 to 9.94)0.086 − 0.25 (− 7.98 to 7.48)0.949High scores indicate a better health status in all four domains Only sex showed a statistically significant association with vitality (B = − 4.82, $$P \leq 0.011$$), with females showing lower scores compared to males. Gender also showed a statistically significant association with social functioning (B = − 6.52, $$P \leq 0.011$$). The median foot pain score was significantly lower in females than males with a median score of 78.12 and 71.88 for males and females, respectively ($$P \leq 0.009$$). The results for the general foot health, foot function, footwear, and vigor domains were not dissimilar, as all showed a significantly higher score for males than for females. Our results were equivocal with respect to the physical and social activity domains, lacking a statistically significant difference between the two groups (Table 6). The use of medications was not associated with physical functioning but showed a statistically significant association with social functioning. A statistically significant decrease in the score was observed with an increase in the use of medications from no medications to one (B = − 12.12, $P \leq 0.001$) and two (B = − 18.46, $$P \leq 0.002$$) medications. Neither smoking nor health insurance was associated with the four general health domains. Regular exercise was associated with higher scores on the physical function domain ($B = 9.75$, $$P \leq 0.003$$). Education was associated with a higher score in the general health ($B = 5.3$, $$P \leq 0.038$$) and physical function ($B = 8.69$, $$P \leq 0.027$$) domains (Table 7).Table 6Comparisons of the FHSQ scores between males and females in the study sampleFHSQ domainsTotal groupMedian ± IR(Range)$$n = 398$$MaleMedian ± IR(Range)$$n = 224$$FemaleMedian ± IR(Range)$$n = 174$$P value male versus female*Foot pain75.00 ± 28.13(0–100)78.12 ± 33.75(6.25–100)71.88 ± 30.63(0–100)0.008504Foot function93.75 ± 25.00(0–100)93.75 ± 25.00(0–100)87.50 ± 25.00(12.50–100)0.06109Footwear85.00 ± 27.5(0–100)92.50 ± 20.00(0–100)85.00 ± 40.00(0–100)0.000852General foot health66.67 ± 50.00(0–100)66.67 ± 58.32(0–100)59.24 ± 50.00(0–100)0.04657General health70.00 ± 30.00(20–100)70.00 ± 30.00(20–100)70.00 ± 30.00(20–100)0.5914Physical activity75.00 ± 50.00(0–100)77.78 ± 55.56(0–100)72.22 ± 44.44(0–100)0.4739Social activity75.00 ± 50.00(0–100)75.00 ± 37.50(0–100)75.00 ± 37.50(0–100)0.01304Vigor56.25 ± 50.00(0–100)56.25 ± 18.75(18.75–100)50.00 ± 18.75(6.25–100)0.01264FHSQ Foot Health Status Questionnaire, IR Interquartile range*Median ± interquartile range, range (min–max), and Mann–Whitney test were used ($99\%$ confidence interval; $P \leq 0.01$ is considered statically significant)Table 7Factors associated with general healthPredictorsGeneral healthPhysical functionSocial functionVitalityB ($95\%$ CI)PB ($95\%$ CI)PB ($95\%$ CI)PB ($95\%$ CI)PGenderMaleReferenceReferenceReferenceReferenceFemale − 1.54 (− 5.74 to 2.65)0.4700.65 (− 5.79 to 7.10)0.842 − 6.52 (− 11.56 to − 1.48)0.011 − 4.82 (− 8.55 to − 1.10)0.011MedicationsNo medsReferenceReferenceReferenceReferenceOne condition − 7.15 (− 12.63 to − 1.68)0.0110.30 (− 8.12 to 8.71)0.945 − 12.12 (− 18.70 to − 5.55) < 0.001 − 4.13 (− 9.00 to 0.73)0.096Two conditions − 4.51 (− 14.04 to 5.01)0.352 − 3.77 (− 18.40 to 10.86)0.613 − 18.46 (− 29.89 to − 7.02)0.002 − 2.85 (− 11.30 to 5.61)0.509SmokingNoReferenceReferenceReferenceReferenceYes − 3.37 (− 8.04 to 1.29)0.1561.32 (− 5.86 to 8.49)0.7180.84 (− 4.77 to 6.44)0.7690.98 (− 3.17 to 5.12)0.643Regular exerciseNoReferenceReferenceReferenceReferenceYes1.79 (− 2.40 to 5.97)0.4029.75 (3.31 to 16.18)0.0031.20 (− 3.83 to 6.23)0.6392.94 (− 0.78 to 6.66)0.121Health insuranceNoReferenceReferenceReferenceReferenceYes0.15 (− 3.89 to 4.18)0.9431.11 (− 5.08 to 7.31)0.724 − 0.11 (− 4.95 to 4.73)0.9640.08 (− 3.50 to 3.66)0.963EducationNoReferenceReferenceReferenceReferenceYes5.30 (0.30 to 10.30)0.0388.69 (1.00 to 16.37)0.027 − 0.33 (− 6.34 to 5.67)0.9130.17 (− 4.27 to 4.61)0.940High scores indicate a better health status in all four domains
## Discussion
This study aimed to determine the relationship between QoL and foot health among adults living in Riyadh, Saudi Arabia. The evidential effect of foot health on QoL also manifests itself in the degree of physical activity performed by individuals, which would ultimately be conducive to a healthier, more physically active lifestyle. Based on different studies of the population of Riyadh, the reported prevalence of different foot conditions is higher than that in other countries. For instance, it was reported that the prevalence of hallux valgus in the region was around $43\%$ which was higher than the global prevalence ($23\%$) [17].
This study gauges the perception of the enrolled participants as it pertains to foot health and well-being. Foot problems may plausibly affect QoL in the personal, social, and occupational domains. Our results demonstrated a correlation between higher general health score and better foot pain, foot function, and foot health domain scores indicating a greater quality of life. Furthermore, less foot pain was positively correlated with foot function, which enables greater control and autonomy over one’s lifestyles, and physical activity [18, 19]. Moreover, the results showed that foot pain, general foot health, vitality score, and social function score were significantly lower in females compared to males. Further, foot function and footwear were lower but not significant. Our results were consistent with previous reports, where lower scores were observed among females, particularly in the foot health-related domains, which was most significant in the general foot health and foot pain domains [20–23]. The aforementioned findings are suggestive of lower QoL as it pertains to foot health among females in our cohort, which may be explained by the higher prevalence of foot conditions among Saudi female population [17, 24]. Moreover, it is well documented that females are more keen about their appearance and foot health. A meta-analysis found that the majority of female participants were more active in watching over their feet, shoes, and slippers and demanded more professional assistance with foot care than that of male participants [25]. Further studies should scrupulously analyze the lifestyle and innate differences that contribute to these dissimilarities in order to formulate targeted therapeutic interventions or lifestyle changes for such disparities.
Previous studies have explored the impact of chronic medical conditions and different foot health and function parameters on general health, physical and social function, and QoL. Many of these studies have discussed chronic medical conditions such as diabetes [20, 26, 27], obesity [20], osteoarthritis [27], hemophilia [28], and even physiological changes in pregnancy [29] and menopause [30] or various foot diseases [2, 12, 20, 31], presenting with pain or foot dysfunction that ultimately and negatively impacts a person’s QoL. This could be avoided if those causes or risk factors are addressed early and managed, which will help to improve general health, QoL, and ultimately autonomy.
This study had few limitations. First, the scarcity of research addressing foot pain and deformities in the region made it difficult to formulate rigorous methodologies to minimize the confounding effects of foot conditions on the outcome variables. Second, the FHSQ is based on the subjective feelings of participants without any objective clinical correlation to solidify the presence of recognizable foot pathologies. Finally, the study settings were not suitable to access all age groups, especially older adults. A more diverse study setting is required to access enough participants of different age groups to ascertain sex-based differences across multiple age groups and further interpretation of associated factors that could impact various foot health parameters to better understand how foot health affects QoL.
## Conclusion
Our results emphasize the need for further research on foot problems in Riyadh region of Saudi Arabia. Clear associations were shown between poor foot health and declining QoL; thus, it is crucial to increase the awareness of society on the importance of medical foot care and continuous follow-up and the consequences of not addressing this issue. Hence, proper foot care and maintaining a better foot health represent major domains for improving the well-being and QoL of a population.
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---
title: Effects of Leea indica leaf extracts and its phytoconstituents on natural killer
cell-mediated cytotoxicity in human ovarian cancer
authors:
- Soek-Ying Neo
- Yin-Yin Siew
- Hui-Chuing Yew
- Yaqian He
- Keng-Ling Poh
- Yi-Chen Tsai
- Shu-Ling Ng
- Wei-Xun Tan
- Teck-Ian Chong
- Claire Sophie En-Shen Lim
- Samuel Shan-Wei Ho
- Deepika Singh
- Azhar Ali
- Yeh-Ching Linn
- Chay-Hoon Tan
- See-Voon Seow
- Hwee-Ling Koh
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10007844
doi: 10.1186/s12906-023-03904-1
license: CC BY 4.0
---
# Effects of Leea indica leaf extracts and its phytoconstituents on natural killer cell-mediated cytotoxicity in human ovarian cancer
## Abstract
### Background
The rich biodiversity of medicinal plants and their importance as sources of novel therapeutics and lead compounds warrant further research. Despite advances in debulking surgery and chemotherapy, the risks of recurrence of ovarian cancer and resistance to therapy are significant and the clinical outcomes of ovarian cancer remain poor or even incurable.
### Objective
This study aims to investigate the effects of leaf extracts from a medicinal plant *Leea indica* and its selected phytoconstituents on human ovarian cancer cells and in combination with oxaliplatin and natural killer (NK) cells.
### Methods
Fresh, healthy leaves of L. indica were harvested and extracted in $70\%$ methanol by maceration. The crude extract was partitioned with n-hexane, dichloromethane and ethyl acetate. Selected extracts and compounds were analyzed for their effects on cell viability of human ovarian cancer cells, NK cell cytotoxicity, and stress ligands expression for NK cell receptors. They were also evaluated for their effects on TNF-α and IL-1β production by enzyme-linked immunosorbent assay in lipopolysaccharide-stimulated human U937 macrophages.
### Results
Leaf extracts of L. indica increased the susceptibility of human ovarian tumor cells to NK cell-mediated cytotoxicity. Treatment of cancer cells with methyl gallate but not gallic acid upregulated the expression of stress ligands. Tumor cells pretreated with combination of methyl gallate and low concentration of oxaliplatin displayed increased levels of stress ligands expression and concomitantly enhanced susceptibility to NK cell-mediated cytolysis. Further, NK cells completely abrogated the growth of methyl gallate-pretreated ovarian cancer cells. The leaf extracts suppressed TNF-α and IL-1β production in human U937 macrophages. Methyl gallate was more potent than gallic acid in down-regulating these cytokine levels.
### Conclusions
We demonstrated for the first time that leaf extracts of L. indica and its phytoconstituent methyl gallate enhanced the susceptibility of ovarian tumor cells to NK cell cytolysis. These results suggest that the combined effect of methyl gallate, oxaliplatin and NK cells in ovarian cancer cells warrants further investigation, for example for refractory ovarian cancer. Our work is a step towards better scientific understanding of the traditional anticancer use of L. indica.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12906-023-03904-1.
## Introduction
Ovarian cancer remains the most lethal gynecological cancer among women [1, 2]. Projection estimates by GLOBOCAN 2020 indicated that by 2040, the number of women worldwide diagnosed with ovarian cancer will increase about $37\%$ to 428,966 [3]. Further, the number of deaths from the disease is projected to surge over $50\%$ to 313,617 from 2020. Current standard treatment for the most common ovarian cancer (i.e. epithelial ovarian cancer) includes surgery followed by platinum-based chemotherapy and radiation therapy [4]. The five-year survival rate of ovarian cancer is around $47\%$, mainly due to high risk of relapse and resistance to chemotherapy. Moreover, early-stage detection of the disease is difficult due to lack of promising screening tools, and most patients are typically diagnosed at advanced stage of the cancer. New therapeutic methods have emerged from various biomarker-driven initiatives such as poly ADP-ribose polymerase inhibitors and antiangiogenic therapy [5, 6]. Cancer immunotherapy, which is the modulation of the body’s innate immune system to treat cancer, has gained widespread interest as any immune-related adverse effects are relatively better tolerated than traditional chemotherapeutic agents [7]. Current immunotherapies for ovarian cancer fall into five broad categories: monoclonal antibodies, checkpoint inhibitors and immune modulators, therapeutic vaccines, adoptive T cell transfer and oncolytic viruses [4]. In particular, natural killer (NK)-cell based immunotherapy holds great promise for cancer treatment because NK cells can be easily isolated and expanded ex vivo for adoptive cell transfer therapy [8–10]. NK cells recognize a broad panel of several dozen ligands which can each induce a cytolytic response [10]. The advantage of NK cell-based therapy over T cells is that there is virtually no complication from graft-versus-host disease [8, 9]. There is no good treatment for late stage ovarian cancer after relapse from the treatment of bevacizumab and olaparib [5, 6]. NK cell therapy of relapsed cancer could potentially provide an alternative option [8–10].
Majority of the chemotherapeutic agents exert their cytotoxic effects by apoptosis which is typically deemed to be non-inflammatory and non-immunogenic [11]. However, it is now clear that certain agents such as anthracyclines and oxaliplatin, in addition to having cytotoxic properties, can also elicit immunogenic cell death [12]. Immunogenic cell death is mediated largely by damage-associated molecular patterns (DAMPs), most of which are recognized by pattern recognition receptors on immune cells. Some DAMPs are actively induced by cells undergoing immunogenic cell death, such as calreticulin, and adenosine triphosphate (ATP), whereas others are induced passively, such as high-mobility group box 1 (HMBG1). Some like members of the tumor necrosis factor (TNF)-family like FAS ligand (FASL), TNF and TNF-related apoptosis inducing ligand (TRAIL) can induce tumor-cell apoptosis upon the formation of immune synapses. These DAMPs play a beneficial role in anti-cancer therapy by interacting with the immune system [12, 13]. Chronic inflammation is typically associated with ovarian cancers, with high levels reactive oxygen species, cytokines, growth factors and inflammatory mediators [14]. An important member of cytokines is the interleukin-1 (IL-1) superfamily which has critical functions in proper maintenance of the innate and adaptive immune system [15]. Various genomic studies have shown that single nucleotide polymorphisms in the IL-1 superfamily can lead to higher susceptibility for immunological pathologies and disease presentation [15].
Medicinal plants have been traditionally used to treat numerous human health conditions and offer a vast resource as drug leads or novel therapeutic agents [16]. Despite the extensive biodiversity of medicinal plants around the world including Southeast Asia, there is scant documentation on the usage of fresh medicinal plants. Rapid urbanization poses a real threat to their natural habitat. Further, there is inadequate research on their pharmacological activities and scientific basis for their medicinal use. Leea indica (Burm. f.) Merrill, which belongs to the genus Leea and family Vitaceae, can be found in tropical and subtropical forests of Southeast Asia, China, India, and north Australia [17, 18]. In Singapore, the plant is distributed in the coastal areas, mangroves, secondary forests and the undergrowth of primary forests [19]. L. indica is also known as Bandicoot berry in English, or Yan Tuo 岩陀 in Chinese, Memali in Malay [17, 18]. The leaves, roots and fruits of L. indica have been traditionally used to treat a wide variety of ailments including cardiovascular diseases, cancer, diabetes, diarrhea, dysentery, eczema, fever, headache, and pain [18, 20, 21]. In vitro studies showed that the leaves of L. indica have various biological activities, including antihyperglycemic [22], antimicrobial [23], antioxidant [23], anticancer [24], anxiolytic [25], thrombolytic [26] and phosphodiesterase inhibitory effects [27]. Essential oils from the flowers may have antimicrobial activity [28], and the entire plant may have antioxidant and nitric oxide inhibitory activities [29]. In view that medicinal plants are good sources of novel therapeutics while treatment options for refractory ovarian cancer are limited and NK cell therapy looks promising, we wish to explore the effects of NK cell killing of ovarian cancer cells triggered by a phytoconstituent identified in a medicinal plant. We have previously shown that the maceration methanolic leaf extract of L. indica had good anti-proliferative activity against various human cancer cells, including ovarian cancer cells [30]. However, the effect of L. indica or its phytoconstituent on ovarian cancer cells and with chemotherapeutic drug oxaliplatin or NK cells are not known. Hence the objective of this study is to investigate the effects of L. indica leaves and its selected phytoconstituents on human ovarian cancer cells and in combination with oxaliplatin and NK cells.
## Plant source and preparation of leaf extracts
Fresh, healthy and mature leaves of L. indica leaves were obtained from the National University of Singapore Medicinal Plant Garden. A voucher specimen of L. indica (LI-0109) was deposited at the Department of Pharmacy Herbarium, National University of Singapore. The plant name was checked with The World Flora Online http://www.worldfloraonline.org [31] and identified with reference to the “World Checklist of Selected Plant Families” and the journal article “Leea L. (Vitaceae) Of Singapore” [19]. The leaves were washed, air dried and blended using a dry grinder, and macerated using $70\%$ v/v methanol [32]. The extracts were dried under vacuo and stored at 25 °C. All procedures were conducted in accordance to the guidelines:—https://www.biomedcentral.com/getpublished/editorial-policies#research+involving+plants.
## Isolation of chemical constituents from L. indica leaf extracts and chemical analyses
Leaf extracts from L. indica were prepared as described previously [32]. Briefly, the dried maceration $70\%$ methanol crude leaf extract was dissolved in water and partitioned with n-hexane, dichloromethane and ethyl acetate to yield hexane, dichloromethane, ethyl acetate, water-soluble and water-insoluble fractions. These fractions were analyzed for their effects on cell viability and NK cell cytotoxicity. The crude extract and ethyl acetate fraction were also investigated for their effects on cytokine production. The ethyl acetate fraction was subjected to two column chromatographic separations in silica gel 60 using hexane, dichloromethane and methanol, followed by a final column chromatographic separation in silica gel 60 using hexane and ethyl acetate. At each step of the purification, concurrent WST-1 and NK cell cytotoxicity assays were performed. A fraction that displayed significant sensitization of OVCAR-5 ovarian cancer cells to NK cell-mediated killing was selected for further purification. Methyl gallate was isolated at a final gradient of $30\%$ v/v ethyl acetate in hexane. To isolate gallic acid, the ethyl acetate fraction was subjected to a column chromatographic separation in silica gel 60 using hexane, dichloromethane and methanol, followed by a final column chromatographic separation in Sephadex LH-20 using water and methanol. Gallic acid was isolated at a final gradient of $20\%$ v/v methanol in water. Commercial chemical standards of gallic acid and methyl gallate were purchased from Sigma-Aldrich (USA). Chemical analyses of the leaf extracts, fractions and isolated compounds were performed as described previously [32].
## General cell culture
Human advanced ovarian cancer cell lines OVCAR-5 (NCI Frederick, USA) and SK-OV-3 (ATCC, HTB-77), human monocytic cell line U937 (ATCC, CRL-1593.2), human NK cell line NK-92 (ATCC, CRL-2407) were purchased. Genetically modified K562-mb15-41BBL cell line was generated by Professor Dario Campana. OVCAR-5, SK-OV-3, U937 and K562-mb15-41BBL cells were grown in RPMI-1640 medium (ThermoScientific, USA) supplemented with $10\%$ v/v heat-inactivated fetal bovine serum (FBS) (ThermoScientific, USA), while NK-92 cells were grown in RPMI-1640 medium containing 100 ng/mL rhIL-2 (Gibco, USA), $12.5\%$ v/v FBS and $12.5\%$ v/v horse serum (Gibco, Cat. no. 26050–088). All the cells were maintained at 37 °C and $5\%$ CO2 in a humidified atmosphere. Methyl gallate and gallic acid were purchased from Sigma-Aldrich (USA), while clinical grade oxaliplatin was purchased (Eloxatin®, Sanofi, France).
For macrophage differentiation, U937 cells grown in in RPMI-1640 medium supplemented with $2\%$ v/v FBS were treated with 5 ng/mL phobol-12-myristate-13-acetate (PMA) (Sigma-Aldrich, USA) for 24 h and washed with PBS as previously described [33]. These PMA-differentiated U937 macrophages were also referred to as U937 macrophages in this study.
The leaf extracts and chemical standards were dissolved in dimethyl sulfoxide (DMSO) (Sigma-Aldrich, USA) and diluted to the desired concentration before addition to cells. To examine the effect of leaf extract, fraction or standard compound on cytokine production, U937 macrophages were incubated for 6 h with the appropriate agent, and then activated overnight with 50 ng/mL lipopolysaccharide (LPS) (Sigma-Aldrich, USA) as previously mentioned [33].
Peripheral blood samples were obtained from discarded anonymized by-products of platelet donations from healthy adult donors at the Health Sciences Authority Blood Bank, Singapore. Studies were performed with approval from the Institutional Review Board, National University of Singapore. Human NK cells were expanded and activated according to the patented methods US 7,435,596 B2 and US 8,026,097 B2 that were established by Professor Dario Campana [34, 35]. Briefly, mononuclear cells collected by centrifugation on a Ficoll-Paque Plus (GE Healthcare Life Sciences) were washed twice in RPMI-1640 medium. To expand CD56 + CD3- NK cells, peripheral blood mononuclear cells and genetically modified K562-mb15-41BBL cell line (E:$T = 1$:1) were cultured with CellGro SCGM medium (CellGenix, Germany) supplemented with $10\%$ v/v FBS and 10 IU/mL human IL-2 (Affymetrix eBioscience, USA). Fresh medium was topped up every two days with IL-2. After 9 days of co-culture, CD3 + T cells were depleted by human CD3 MicroBeads (Miltenyi Biotec, Germany) from autoMACS Separator (Miltenyi Biotec, Germany), generating CD56 + CD3- NK cells with more than $95\%$ purity.
## Determination of cell viability by water soluble tetrazolium salts (WST-1) assay
This assay was performed as described previously [33, 36]. Briefly, exponentially growing cells were plated in 96-well plates at 3 × 104 cells/100 µL (OVCAR-5, U937), or 7 × 103 cells/100 µL (SK-OV-3). U937 cells were treated with PMA for 24 h to differentiate into macrophages and washed with PBS. These differentiated U937 macrophage cells and the adherent ovarian cancer cells were treated with the appropriate agent (extract/drug/vehicle control) for 48 h, and untreated cells were used as controls. After 48 h, the media was aspirated and replaced with $10\%$ v/v WST-1 (Roche, Switzerland) for 1 h. The formazan dye produced was quantified at 440 nm against a reference wavelength of 650 nm using a microplate reader (Tecan Infinite M200 PRO, Switzerland). Cell viability was expressed as a percentage of the control cells. The IC50 value (i.e. concentration of extract/compound required to inhibit $50\%$ growth of cells) from cell viability assay was used as a parameter for anti-proliferative potency [37, 38], while the IC20 value (i.e. concentration of extract required to inhibit $20\%$ growth of cells) was taken as an indicator for non-toxic dose of test sample [37]. The IC50 and IC20 values were determined using GraphPad Prism 9 (GraphPad Software, Inc., USA). The results were generated from three independent experiments and each experiment was performed in 5 replicates.
## Evaluation of cytokine production by ELISA assay
The production of TNF-α and IL-1β cytokines were measured in U937 macrophages by ELISA as previously described [33]. Briefly, U937 cells were plated in 6-well plates (Costar, USA) at 1 × 106 cells per well, treated with PMA for 24 h to differentiate into macrophages and washed with PBS. These PMA-differentiated cells were incubated with the appropriate agents as described above. Cell supernatant was collected at the end of incubation and analysed for the level of cytokines IL-1β and TNF-α using ELISA kit from Quantikine (R&D Systems, Minneapolis, USA) according to manufacturer’s instructions. Briefly, standards and samples were added to wells pre-coated with antibodies for 2 h, washed, and incubated with cytokine conjugate for 1 h. After washing, substrate solution was added for 20 min, followed by stop solution. The cytokine level present was quantified at 450 nm against a reference wavelength of 540 nm using a microplate reader (Tecan Infinite M200 PRO, Switzerland) and absolute concentrations of cytokines were interpolated from their respective standard curves. Standard curves were achieved using standard concentrations of the human IL-1β and TNF-α based on manufacturer’s instructions. The results were generated from three independent experiments.
## NK cell cytotoxic activity assay
This assay was performed as described previously [36]. Ten million cancer cells were washed several times after compound pre-treatment to deplete any minimum residue compound. Cells were labelled with a red fluorescent dye PKH-26 (Sigma-Aldrich, USA) that bound irreversibly to the cell membrane. After incubation for 15–30 min at room temperature, labelled cells were washed three times with RPMI-1640 medium and the viability of target cells was evaluated by trypan blue exclusion counting. Ten thousand viable OVCAR-5 and SK-OV-3 target cells were attached to a 96-well, flat-bottomed plate which was pre-coated with poly-L-lysine (Sigma-Aldrich, USA). Target cells were co-cultured with activated NK cells for 8 or 12 h, respectively, at the indicated effector-to-target (E:T) ratios. For NK-92 cells, the co-culture duration was 12 h. After incubation, lysed cells were gently washed away with PBS. Multiple reads per well were obtained with fluorescence at excitation wavelength 540 nm and emission wavelength 590 nm using a multimode microplate reader (Tecan Spark®, Switzerland). Cytotoxicity was assessed by measuring the viability of PKH-26 positive target cells that were still attached on the plate. The percentage of viability for target cells was calculated as (target plus effector cells fluorescence – maximum lysis fluorescence) / (target cells alone fluorescence – maximum lysis fluorescence) × $100\%$. The percentage of specific lysis for NK cells was calculated as $100\%$ – % viability [36]. Experiments were performed in triplicate and at least three independent experiments were done. To investigate the tumor cell proliferation dynamics of ovarian cancer cells in the presence of NK-92 cells, cancer cells were co-cultured with NK-92 at E:T ratio of 1:4, media was replaced daily partially and cell viability was evaluated by trypan blue exclusion counting every 3 days.
## Antibody staining and flow cytometry
Anti-human phycoerythrin (PE)-conjugated antibodies used in this study were: anti-CD112 (TX31, IgG1, BioLegend), anti-CD155 (TX24, IgG2a, BioLegend), anti-MIC-A/B (6D4, IgG2a, BioLegend), anti-ULBP-1 (Clone 170818, IgG2a, R&D Systems), anti-ULBP-2 (Clone 165903, IgG2a, R&D Systems), anti-ULBP-3 (Clone 166510, IgG2a, R&D Systems), anti-DR4 (CD261, TRAIL-R1, Clone DJR1, BioLegend), anti-DR5 (CD262, TRAIL-R2, Clone DJR2-4, BioLegend). The assay was performed as described previously [36]. Briefly, after drug treatment, cancer cells were incubated with antibodies in fluorescence-activated cell sorting (FACS) solution on ice for 30 min. Cells were washed three times with FACS solution and then fixed with $2\%$ paraformaldehyde (Sigma-Aldrich, USA). For intracellular staining, cells were first permeabilized and fixed in Cytofix/Cytoperm™ solution (BD Pharmingen) based on manufacturer’s instructions, washed and then incubated with antibody to intracellular targets for 30 min on ice. After staining, cell samples were acquired and recorded on a FACSCalibur (BD Biosciences, USA), and data was analyzed with BD CellQuest Pro and FlowJo software (Tree Star). For investigating the expression levels of stress ligands by flow cytometry, samples were stained and acquired together on the flow cytometer on the same day, using the same voltage settings. Data were presented as relative mean fluorescence intensity (MFI) calculated (MFI of treated cancer cells) / (MFI of non-treated cancer cells), and control cells (i.e. non-treated cancer cells) were taken as relative MFI of 1.
## Statistical analyses
All statistical analyses were performed with GraphPad Prism 9 (San Diego, California, USA). The correlation of ovarian cancer cell phenotypes expressing stress ligands and death receptors were analyzed with Student’s t-test. For evaluating combination therapy of methyl gallate and NK cells on the proliferation of ovarian cancer cells, one-way ANOVA with Bonferroni’s multiple comparison test was applied. For analyses of NK cell cytotoxicity, multiple comparisons were performed using two-way ANOVA with a Bonferroni test. For assessing the fold change of TNF-α and IL-1β levels, one-way ANOVA was used. Mean and SD of data from triplicate experiments were applied. Error bars show standard deviation (SD) as indicated in legend, and $p \leq 0.05$ was considered statistically significant. * indicates $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$, ns denotes not significant.
An overall flow chart of the methods is shown in Supplementary Fig. 1.
## L. indica leaf extract promoted sensitivity of ovarian carcinoma cells lines to NK cell-mediated cytolysis
Crude methanolic extract of L. indica leaves was partitioned by liquid–liquid partitioning to yield hexane, dichloromethane, ethyl acetate and water-soluble fractions as well as water-insoluble fraction. Human ovarian cancer OVCAR-5 cells were treated with various concentrations of crude leaf extract and fractions for 48 h and cell viability was measured by WST-1 assay. Their IC50 values are shown in Table 1. Amongst the fractions, the ethyl acetate fraction showed the lowest IC50 value (IC50 = 59.7 ± 1.9 µg/mL), followed by water-soluble fraction (IC50 = 260.8 ± 17.9 µg/mL). The IC50 value of the ethyl acetate fraction was about half of that of the crude extract (IC50 = 122.5 ± 13.8 µg/mL). Due to its low yield, the hexane fraction was not further studied. The crude leaf extract and four fractions were next assessed for their effects on human ovarian cancer cells in NK cell-mediated cytotoxicity with activated NK cells. Ovarian cancer cells were washed three to five times to deplete minimum residue of each test sample after treatment with either the extract or fraction. Compared to untreated cells, we found pretreatment of ovarian cancer OVCAR-5 cells with crude leaf extract significantly increased the susceptibility of the cancer cells to cytolysis by activated NK cells ($p \leq 0.001$, Fig. 1A). Similar phenomenon was observed for the ethyl acetate and water-soluble fractions. OVCAR-5 cells that were pretreated with either ethyl acetate fraction ($p \leq 0.001$, Fig. 1B) or water-soluble fraction ($p \leq 0.001$, Fig. 1D) exhibited enhanced susceptibility to cytotoxicity with activated NK cells, compared to untreated group. There was no significant difference between untreated OVCAR-5 cells and cells pretreated with dichloromethane fraction (Fig. 1C) or water insoluble fraction (Fig. 1E). Taken together, ovarian cancer cells pretreated with ethyl acetate fraction showed enhanced susceptibility to activated NK cell-mediated cytolysis, and the ethyl acetate fraction showed the strongest anti-proliferative activity in ovarian cancer cells relative to the other fractions. Therefore, the ethyl acetate fraction was subjected to subsequent further rounds of column chromatography purification, accompanied by concurrent WST-1 and NK cell cytotoxicity assays. At each step, a fraction that significantly sensitized the ovarian cancer cells to NK cell-mediated killing was selected for further purification. This sequential process of bioassay-guided fractionation led to the isolation and identification of gallic acid and methyl gallate [32].Table 1IC50 values of crude leaf extract and fractions of L. indica in OVCAR-5 cellsLI extract and fractionsIC50 values (µg/mL)Crude extract122.5 ± 13.8Dichloromethane fraction1542.0 ± 115.3Ethyl acetate fraction59.7 ± 1.9Hexane fraction818.6 ± 74.8Water insoluble fraction479.9 ± 52.7Water soluble fraction260.8 ± 17.9Data are presented as mean ± SD from 3 independent experiments, each carried out in triplicatesFig. 1L. indica leaf extract pretreated ovarian cancer cells showed enhanced sensitivity to cytolysis by activated NK cells. OVCAR-5 cells were pretreated overnight with or without 0.3 mg/mL (A) crude extract, (B) ethyl acetate fraction, (C) dichloromethane fraction, (D) water soluble fraction and (E) water insoluble fraction of L. indica. Subsequently cells were co-cultured with activated NK cells at the various E:T ratios. Cytotoxicity assay was determined by measuring the viability of PKH-26 labelled target cancer cells on 96-well plate. Experiment was performed in triplicates. Results from one representative experiment of three are shown and presented as mean ± SD. Blue line represents untreated cells, while red line represents treated cells. *** $p \leq 0.001$; ns, not significant
## Methyl gallate but not gallic acid enhanced sensitivity of ovarian carcinoma to NK cell-mediated cytolysis
Gallic acid and methyl gallate were analyzed for their anti-proliferative effects in OVCAR-5 ovarian cancer cells. The IC50 values of gallic acid and methyl gallate in OVCAR-5 cells were 14.4 ± 0.6 µg/mL (84.9 ± 3.4 µM) and 93.7 ± 3.3 µg/mL (509.0 ± 17.7 µM), respectively. The effects of gallic acid and methyl gallate on human ovarian cancer cells followed by co-culture with activated NK cells were next determined. We chose a concentration as close to the IC50 value as possible, namely 0.03 mg/mL gallic acid and 0.1 mg/mL methyl gallate. We examined both the isolated compounds and their respective chemical standards, and the results are shown in Fig. 2. Oxaliplatin, a third-generation platinum drug which is known to augment sensitivity of colon cancer [39] and ovarian carcinoma [36] to NK cell-mediated cytolysis, was used as positive control ($p \leq 0.001$, Fig. 2E). Compared to untreated cells, we found no significant difference in cytolysis of OVCAR-5 cells that were pretreated with the isolated gallic acid (Fig. 2A). In ovarian cancer cells pretreated with the chemical standard gallic acid, there was low level of NK cell cytolysis ($p \leq 0.001$, Fig. 2C). In contrast, the isolated methyl gallate ($p \leq 0.01$, Fig. 2B) and its chemical standard ($p \leq 0.001$, Fig. 2D) significantly elevated the susceptibility of ovarian cancer OVCAR-5 cells to cytolysis by expanded NK cells. These results suggest methyl gallate significantly enhanced the susceptibility of ovarian cancer cells to specific NK-cell mediated cytolysis, whereas gallic acid had low to no effect at the concentration investigated. Fig. 2Methyl gallate pre-treatment increased susceptibility of ovarian cancer cells to NK cell-mediated cytolysis. OVCAR-5 cells were pretreated overnight with or without (A) 0.03 mg/mL isolated gallic acid, (B) 0.1 mg/mL isolated methyl gallate, (C) 0.03 mg/mL gallic acid standard, (D) 0.1 mg/mL methyl gallate standard, (E) 20 µM oxaliplatin, and then co-cultured with activated NK cells at the various E:T ratios. Cytotoxicity assay was determined by measuring the viability of PKH-26 labelled target cancer cells on 96-well plate. Experiment was performed in triplicates. Results from one representative experiment of three are shown and presented as mean ± SD. Blue line represents non-treated cells, while red line represents treated cells. ** $p \leq 0.01$; ***$p \leq 0.001$; ns, not significant
## L. indica extract and methyl gallate upregulated the expression of stress ligands for NK cell receptors in ovarian cancer cells
To examine in ovarian cancer OVCAR-5 cells the ability of L. indica extract-mediated induction of activating ligands for NK cell receptors (DNAM-1 and NKG2D), the cancer cells were treated with the ethyl acetate fraction, and subsequently analyzed for the stress ligands by immunofluorescence conjugated specific monoclonal antibodies. We found that the ethyl acetate fraction of L. indica significantly induced higher expression of CD155 ($p \leq 0.01$), DR4 (TRAIL-1, $p \leq 0.05$) and DR5 (TRAIL-2, $p \leq 0.05$) in cancer cells treated with ethyl acetate fraction compared to untreated cells (Fig. 3A, Supplementary Fig. 2A). The other ligands for NK cell receptors did not show any significant difference between treated and untreated cancer cells (Fig. 3A, Supplementary Fig. 2A). We next investigated the ability of methyl gallate to mediate the induction of activating ligands for NK cell receptors DNAM-1 and NKG2D in ovarian cancer cells. Compared to untreated cells, methyl gallate-treated cells displayed significant induction of CD112, CD155, ULBP-1, ULBP-2, ULBP-3, DR4 (TRAIL-1), and DR5 (TRAIL-R2) ($p \leq 0.05$, $p \leq 0.01$, Fig. 3B, Supplementary Fig. 2B). Both DR4 and DR5 are death receptors for tumor necrosis factor-related apoptosis-inducing ligands (TRAIL), a cytokine produced by NK and T cells that exhibit specific tumoricidal activity against a variety of tumors. We had previously shown that oxaliplatin at 20 µM upregulated the expression of stress ligands for NK cell receptors in OVCAR-5 cells and enhanced NK cell cytolysis of ovarian cancer OVCAR-5 cells [36]. Oxaliplatin, like all other chemotherapeutic agents when used at high doses, are known to have clinically adverse side effects such as peripheral neuropathy and nausea [40–42]. We therefore chose a lower concentration of oxaliplatin (i.e. 10 µM), and asked if a combination of methyl gallate with low dose oxaliplatin had any effect on the stress ligand expression on ovarian cancer cells. Figure 3C (Supplementary Fig. 2C) shows that cancer cells treated with combination of methyl gallate and low dose oxaliplatin (i.e. 10 µM) displayed an overall significant induction of CD112, CD155, ULBP-1, ULBP-2, ULBP-3, DR4, and DR5 ($p \leq 0.05$, $p \leq 0.01$). We also investigated the effect of gallic acid treatment on the levels of stress ligands in ovarian cancer cells. We found that gallic acid-treated OVCAR-5 cells did not show any significant difference in the expression of these stress ligands (Supplementary Fig. 3). Taken together, these data (Figs. 2 and 3, Supplementary Figs. 2 and 3) indicate that ovarian cancer cells treated with methyl gallate, or with combination of methyl gallate and low concentration of oxaliplatin (i.e. 10 µM), showed increased expression of stress ligands for NK cell receptors and concomitantly enhanced sensitivity to NK cell-mediated cytolysis. Conversely, ovarian cancer cells treated with gallic acid showed no significant difference in the expression of these stress ligands and marginal susceptibility to NK cell killing. Fig. 3Increased expression of stress ligands for NK cell receptors on ovarian cancer cells after treatment with (A) ethyl acetate fraction of L. indica, (B) methyl gallate, and (C) combination of methyl gallate and oxaliplatin. OVCAR-5 cells were treated for 48 h with or without (A) L. indica ethyl acetate fraction (EA, 0.3 mg/mL), (B) methyl gallate (MG, 0.1 mg/mL), or (C) combination of methyl gallate (MG, 0.1 mg/mL) and oxaliplatin (10 µM), and then phenotype analyzed by FACS for the indicated ligands of NK cells: (a) CD112, (b) CD115, (c) MIC-A/B, (d) ULBP-1, (e) ULBP-2, (f) ULBP-3, (g) DR4 (TRAIL-R1), and (h) DR5 (TRAIL-R2). The relative mean fluorescence intensities of each stress ligand were compared between untreated cells (blue bars) and treated cells (red bars), and results presented are mean ± SD of three independent experiments. * $p \leq 0.05$; **$p \leq 0.01$; ns, not significant
## Combination treatment of methyl gallate and oxaliplatin augmented susceptibility of ovarian tumor cells to NK cell-mediated cytolysis
Methyl gallate was analyzed for its anti-proliferative effects in SK-OV-3 cells, and the IC50 value of methyl gallate in SK-OV-3 cells was found to be 21.7 ± 2.4 µg/mL (117.5 ± 12.8 µM). Clearly, the IC50 value of methyl gallate in SK-OV-3 cells was lower than that of the IC50 value in OVCAR-5 cells (93.7 ± 3.3 µg/mL or 509.0 ± 17.7 µM). We chose a concentration of methyl gallate as close to the IC50 value as possible, namely 20 µg/mL and 100 µg/mL for SK-OV-3 and OVCAR-5 cells, respectively. We had previously shown that oxaliplatin at 20 µM and 50 µM significantly increased specific cytolysis of activated NK cells against ovarian cancer OVCAR-5 and SK-OV-3 cells respectively [36]. As high drug concentration is typically associated with clinically undesirable side effects such as acute neuropathy [41, 42], we studied oxaliplatin at low concentration of 10 µM and 25 µM for ovarian cancer OVCAR-5 and SK-OV-3 cells respectively. We investigated the effects of methyl gallate combined with oxaliplatin or methyl gallate alone in these ovarian cancer cells on their sensitivity to NK cell-mediated cytotoxicity. Compared to untreated cells, we found OVCAR-5 and SK-OV-3 cancer cells pretreated with 10 µM ($p \leq 0.05$, Fig. 4A) and 25 µM ($p \leq 0.001$, Fig. 4B) oxaliplatin respectively exhibited increased susceptibility to NK-92 cells. Compared to untreated cells, pre-treatment of cancer cells with methyl gallate showed the OVCAR-5 ($p \leq 0.001$, Fig. 4A) and SK-OV-3 cancer cells ($p \leq 0.01$, Fig. 4B) were relatively sensitive to NK cell-mediated cytolysis, albeit at low levels. Interestingly, pre-treatment of OVCAR-5 cancer cells with combined methyl gallate and 10 µM oxaliplatin greatly augmented the susceptibility of these cancer cells to NK cell cytolysis ($p \leq 0.001$, Fig. 4A). Similarly, pre-treatment of SK-OV-3 cancer cells with combined methyl gallate and 25 µM oxaliplatin greatly enhanced the susceptibility of the cancer cells to NK cell cytolysis ($p \leq 0.001$, Fig. 4B). These results suggest that treatment of ovarian cancer cells with combined methyl gallate and low concentration of oxaliplatin can enhance the susceptibility of ovarian cancer cells to NK cell-mediated cytolysis. Fig. 4Combination treatment of methyl gallate and oxaliplatin augmented susceptibility of ovarian cancer cells to NK cell-mediated cytolysis. A OVCAR-5 cells were pretreated for 24 h with or without 20 μM oxaliplatin, 10 μM oxaliplatin, 0.1 mg/mL methyl gallate (MG) alone, or combination of 0.1 mg/mL MG and 10 μM oxaliplatin. B SK-OV-3 cells were pretreated for 48 h with or without 50 μM oxaliplatin, 25 μM oxaliplatin, 0.02 mg/mL methyl gallate (MG) alone, or combination of 0.02 mg/mL MG and 25 μM oxaliplatin. Cells were then co-cultured with NK-92 cells at the various E:T ratios. Experiment was performed in triplicates. Results from one representative experiment of three are shown and presented as mean ± SD. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$; ns, not significant
## Pre-treatment with methyl gallate reduced ovarian cancer cell proliferation when co-cultured with NK cells
To measure tumor growth dynamics of the ovarian cancer cells in the presence of NK-92 cells, we chose a very low E:T ratio of 1:4 and monitored the proliferation of these cancer cells which had been pre-treated with methyl gallate. Untreated cancer cells or cancer cells pre-treated with methyl gallate were grown in the absence of NK-92 cells and served as control groups. Results showed that in the absence of any treatment, ovarian cancer cells grew exponentially as expected (Fig. 5A and C). OVCAR-5 cancer cells that were pre-treated with methyl gallate, and then subsequently cultured in the absence of NK-92 cells, recovered their propensity to grow by day 15 (Fig. 5A). Similarly, SK-OV-3 cancer cells that were pre-treated with methyl gallate, and then subsequently cultured in the absence of NK-92 cells, showed signs of exponential growth by day 18 (Fig. 5C). In contrast, ovarian cancer cells that received treatment with both methyl gallate and NK-92 cells in a sequential manner were unable to initiate their growth potential ($p \leq 0.001$, Fig. 5B and D). Without methyl gallate treatment, OVCAR-5 and SK-OV-3 cancer cells that were co-cultured with low E:T ratio of NK-92 cells gradually showed signs of tumor cell proliferation, although at much lower levels compared with untreated tumor cells that were grown in the absence of NK cells (Fig. 5B and D). A schematic figure of some key findings is presented in Supplementary Fig. 4.Fig. 5Combination therapy of methyl gallate and NK-92 cells suppressed re-proliferation of ovarian cancer cells. A, B OVCAR-5 cells were pretreated for 24 h with 0.1 mg/mL methyl gallate (MG), and (C, D) SK-OV-3 cells were pre-treated for 48 h with 0.02 mg/mL methyl gallate (MG). Cells were then co-cultured in the presence or absence of NK-92 cells at E:T ratio of 1:4. Untreated cells co-cultured in the presence or absence of NK-92 cells served as control. Cells were counted every 3 days. Values presented are the average ± SD of three independent experiments performed in triplicates. *** $p \leq 0.001$
## L. indica extract and methyl gallate suppressed TNF-α and IL1-β cytokine release
We investigated the crude leaf extract and the ethyl acetate fraction of L. indica as well as methyl gallate and gallic acid in PMA-differentiated U937 cells for their effects on cytokine release. We first evaluated the crude leaf extract, ethyl acetate fraction, methyl gallate and gallic acid in U937 macrophages for their potential cytotoxicity using WST-1 cell viability assay. We found that L. indica crude leaf extract did not result in any appreciable difference in the cell viability of U937 macrophages even up to 100 µg/mL, while the ethyl acetate fraction displayed an IC50 value of 20.17 ± 0.46 µg/mL, with IC20 value of 9.7 ± 1.3 µg/mL. Methyl gallate and gallic acid displayed IC50 values of 8.2 ± 0.6 µg/mL (45.0 ± 3.3 µM) and 23.8 ± 2.5 µg/mL (139.8 ± 14.8 µM) in U937 macrophages respectively. The IC20 values of methyl gallate and gallic acid were 5.4 ± 0.4 µg/mL (29.3 ± 2.4 µM) and 18.0 ± 1.8 µg/mL (105.6 ± 10.9 µM) respectively. A criterion for examining the inflammatory effects of the leaf extract was that the concentration of leaf extract used should be the largest one in which the cells remained viable. We chose to use the IC20 values of the extracts [37], which represented the concentration at which at least $80\%$ of the cell population was alive. Based on their different IC20 values, we studied the ethyl acetate fraction at 10 µg/ml, and methyl gallate and gallic acid at 38 µM. In the absence of any treatment, U937 cells produced very low levels of TNF-α and IL-1β. Treatment with PMA significantly increased the production of TNF-α and IL-1β in U937 macrophages (Fig. 6). Stimulation of PMA-differentiated cells (also referred here as U937 macrophages) with LPS further doubled the production of both TNF-α ($p \leq 0.001$, Fig. 6A) and IL-1β ($p \leq 0.05$, Fig. 6C) compared to PMA treatment only. As expected, the increased TNF-α and IL-1β levels were abolished upon pre-incubation of cells with dexamethasone, a corticosteroid known to alleviate inflammatory conditions (Fig. 6). Interestingly, pre-incubation of U937 macrophages with either crude leaf extract or ethyl acetate fraction significantly suppressed TNF-α ($p \leq 0.05$, Fig. 6A) and IL-1β levels ($p \leq 0.01$, Fig. 6C). Pre-treatment of U937 macrophages with 38 µM methyl gallate significantly inhibited the production of TNF-α ($p \leq 0.01$, Fig. 6B) and IL-1β ($p \leq 0.05$, Fig. 6D). However, pre-treatment of U937 macrophages with 38 µM gallic acid did not show any significant difference on TNF-α and IL-1β levels (Fig. 6B and D).Fig. 6L. indica crude leaf extract, ethyl acetate fraction, and methyl gallate suppressed TNF-α and IL-1β production by human U937 macrophages. Fold change of TNF-α (A, B) and IL-1β (C, D) production relative to control in the supernatant of human U937 macrophages measured by ELISA. Cells were pretreated for 5 h with or without 40 μg/mL L. indica crude leaf extract, 10 μg/mL L. indica ethyl acetate fraction (EA), 38 μM methyl gallate (MG) or 38 μM gallic acid (GA), followed by LPS stimulation. Cytokine production by cells treated with both PMA and LPS was taken as 1. Dexamethasone (DEX) at 64.4 ng/mL was used as positive control, while DMSO was used as negative control. Results are presented as mean fold change ± SD of three independent experiments. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$; ns, not significant
## Discussion
L. indica is traditionally used to treat intestinal cancer and uterus cancer [43] and the leaf extracts of L. indica showed anticancer activity against Ehrlich Ascites Carcinoma (EAC) cells in Swiss albino mice, cervical epidermoid (Ca Ski) and most other human cancer cell lines [24, 25, 30]. Mollic acid arabinoside and mollic acid xyloside identified from the ethanol leaf extract were reported to be responsible for the cytotoxic effects against human cervical Ca Ski cancer cells [44], possibly via the stimulation of mitochondria-mediated apoptosis [45]. However, it is unclear if there are also other phytoconstituent(s) responsible and other mechanisms involved. Herein we report for the first time that leaf extracts of L. indica increased the susceptibility of human ovarian cancer cells to NK cell-mediated cytotoxicity (Fig. 1). The leaf extracts also suppressed the levels of TNF-α and IL-1β cytokines in human U937 macrophages (Fig. 6). We demonstrate for the first time that methyl gallate isolated and identified in L. indica can enhance the sensitivity of human ovarian cancer cells to NK cell-mediated cytotoxicity (Figs. 2 and 4), and this increased cytolysis was likely due to the associated elevated expression of various stress ligands for NK cell receptors (i.e. DNAM-1 and NKG2D) on these cancer cells (Fig. 3B, Supplementary Fig. 2B). Cancer cells pretreated with methyl gallate and subsequently cultured in the presence of NK cells were unable to initiate their growth potential (Fig. 5). Combined methyl gallate with low concentration of chemotherapeutic drug oxaliplatin enhanced the levels of stress ligand expression on tumor cells (Fig. 3C, Supplementary Fig. 2C), and this was accompanied by higher susceptibility to NK cell-mediated cytolysis (Fig. 4).
It is well documented that although NK cells are innate immune cells that play crucial roles in immunosurveillance and eliminating tumors, NK cell function is often impaired during tumor development and progression due to the presence of multiple immunosuppressive factors in the tumor microenvironment [46, 47]. Tumour variants can evade NK cell attack by mechanisms such as defective expression of activating ligands. Tumours may also upregulate ligands for inhibitory receptors and/or lose ligands for activating receptors, causing cells to be resistant against NK cell-mediated killing. For instance, high levels of circulating soluble MIC-A/B were associated with poor prognosis in a number of cancer types including colorectal, ovarian, liver, lung and prostate cancers [48, 49]. Compounds that enhance NK cell-mediated lysis of cancer cells are therefore highly beneficial and valuable, and they include bortezomib [50], doxorubicin [51] and oxaliplatin [52]. For example, the chemotherapeutic drug doxorubicin or the proteasome inhibitor bortezomib can trigger the upregulation of activating ligands for NKG2D receptor and DNAM-1 on multiple myeloma cells, thereby sensitizing them to NK cell-mediated lysis [53]. Bortezobmib inhibited proliferation of liver cancer cells and increased MIC-A/B expression [50]. Natural products that are able to induce immunogenic cell death could represent novel lead compounds for cancer therapy. Some examples of reported natural products that induce immunogenic cell death include digoxin from Digitalis species and capsaicin from Capsicum species, as well as those derived from marine organisms such as *Spirulina maxima* [54], resveratrol [55], daphnetin [56], and stemphol [57]. Resveratrol, a naturally occurring plant polyphenol, sensitized human leukemia KG-1a cells to NK cell killing through NKG2D ligands and TRAIL receptors [58]. Lee et al.[59] showed in mouse models that resveratrol upregulated NKG2D, NKp30 and CD107a expression, and effectively inhibited tumor growth and metastasis. Stemphol, a natural dialkyl resorcinol extracted from Stemphylium globuliferum, induced caspase-independent cell death and released high-mobility group box 1 (HMGB1) in leukemia cells [57]. Daphnetin, a dihydroxylated derivative of coumarin, is a potent stimulator of NK cells in that daphnetin enhances IFN-γ production and direct cytotoxicity in the presence of IL-12 [56]. Daphnetin also suppresses inflammatory cytokine production in experimental autoimmune encephalomyelitis mice [60]. In our study, we showed in ovarian cancer cells that methyl gallate significantly enhanced the expression of stress ligands for DNAM-1 and NKG2D NK cell receptors, i.e. CD112, CD155, MIC-A/B, ULBP-$\frac{1}{2}$/3, TRAIL-1 (DR4) and TRAIL-2 (DR5) (Fig. 3B, Supplementary Fig. 2B). Pre-treatment of ovarian cancer cells with methyl gallate rendered these cancer cells more susceptible to NK cell killing, compared to ovarian cancer cells that have not been previously exposed to methyl gallate (Figs. 2 and 4). However, gallic acid showed no significant effect on the expression of stress ligands in these cancer cells at the concentration tested (Supplementary Fig. 3), and therefore the relatively low level of NK cell-mediated cytolysis (Fig. 2). In contrast, Dedoussis et al.[61] demonstrated in human leukemia K562 cell line that pre-treatment with 200 µg/ml gallic acid rendered the cells significantly susceptible to NK cell-mediated necrosis. It is likely that the difference in findings could be due to differences in cell type and concentration of gallic acid used. Nevertheless, it is possible that the stress ligands investigated in this study may not account for the whole picture of sensitizing the ovarian cancer cells to NK cell-mediated killing. There may be other ligands and factors not studied here that could potentially contribute to the methyl gallate-associated NK cell lysis or combined methyl gallate and oxaliplatin-associated NK cell lysis, such as B7-H6, calrecticulin, HMGB1, cytokines, and chemokines [46, 47]. It is also unclear whether methyl gallate treatment of ovarian cancer cells inhibited specific signaling pathway, or dampened DNA methyltransferase or histone acetylases. Given the importance of dysregulation of epigenetic signaling pathways and cancer [62], future studies exploring these possibilities are warranted.
As far as we are aware, our group is the first to report the identification and isolation of methyl gallate from leaves of L. indica (reference [32] and this study). Also known as methyl-3,4,5-trihydroxybenzoic acid, methyl gallate is a polyphenolic compound reported in plants such as maple leaf [63], root bark of *Paenonia suffruticosa* [64], *Schinus terebinthifolius* [65], *Rosa rugosa* [66], and *Galla rhois* [67]. Methyl gallate has been reported to possess various biological properties including anti-oxidant [64, 68] and anti-microbial properties [69]. In human hepatocellular carcinoma, methyl gallate is reported to suppress cell proliferation via increasing the production of reactive oxygen species and apoptosis [70]. Methyl gallate is also shown to have anti-inflammatory activities in zymosan-induced experimental arthritis animal model, wherein methyl gallate impaired zymosan-stimulated macrophages by inhibiting IL-6 and nitric oxide production, cylooxegenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) expression [71]. Administration of methyl gallate in lipopolysaccharide-treated mice protected the mice against acute renal injury, increased anti-oxidant activity and decreased NF-kB activity [64]. In mouse RAW 264.7 cells, methyl gallate blocked inflammation induced by Toll-like receptor ligands through attenuating NF-kB signaling and mitogen-activated protein kinase (MAPK) pathway [72]. Methyl gallate also inhibited lipopolysaccharide-induced nitric oxide and IL-6 production in mouse-derived RAW 264.7 cells, most likely via the down regulation of extracellular-signal-regulated kinase $\frac{1}{2}$ (ERK$\frac{1}{2}$) pathway [73].
Combination of cisplatin-paclitaxel, which is a widely adopted “standard” treatment for advanced ovarian cancer, is frequently interrupted by the emergence of drug resistance cancer cells [74, 75]. Oxaliplatin but not cisplatin was shown to trigger immunogenic cell death of colorectal cancer cells, activated dendritic cells by expressing danger signals such as heat shock proteins, calreticulin, HMGB1, and efficiently generated a pool of tumor antigen-specific T cells [39]. Oxaliplatin is a third-generation platinum compound that is less studied and rarely used but promising in the treatment of ovarian cancer [76]. Oxaliplatin has been the backbone of treatment of colorectal cancer. Its cytotoxic effect is mediated mainly through DNA damage. Like other chemotherapeutic agents when used at high doses, oxaliplatin is reported to have clinically adverse side effects such as peripheral neuropathy and nausea [40–42]. We therefore studied oxaliplatin at low concentration and chose two ovarian cancer cell lines OVCAR-5 and SK-OV-3 with different spectrum of drug resistance. OVCAR-5 cells are known to show resistance to clinically relevant concentrations of adriamycin, melphalan and cisplatin, while SK-OV-3 cells are resistant to tumour necrosis factor and several cytotoxic drugs including diphtheria toxin, cis-platinum and adriamycin. Combination of low level oxaliplatin and methyl gallate in the presence of NK cells was capable of effecting cancer cell lysis despite the tumor resistance (Fig. 4). An E:T ratio of 1:4 was chosen to demonstrate that even at this low concentration of NK cells, the proliferation of these ovarian cancer cells which are resistant to drugs, can still be suppressed (Fig. 5). More importantly, our data suggests that a single treatment regime alone (either NK cell co-culture alone, or methyl gallate treatment alone) was insufficient to completely abrogate cancer cell growth. Likewise, oxaliplatin treatment alone was insufficient [36]. The re-proliferation of minimal residual cancer cells in in vitro cultures was subsequently detectable over time. These “residual” cancer cells can potentially form the next resistant colony in the long term and eventually render resistance to the existing treatment regime. Our work suggests combination of methyl gallate and oxaliplatin, which can trigger antitumor immunogenicity, in conjunction with activated NK cells, warrants further investigation.
The devastating effect of immune dysregulation is well recognized in cancers. Cytokine storm and cytokine release syndrome are life-threatening systemic inflammatory syndromes which involve high levels of circulating cytokines and immune-cell hyperactivation [77]. Multiple studies have demonstrated that ovarian cancer has immunosuppressive tumor micro-environment that poses serious challenge to existing treatment modalities. For instance, myeloid-derived suppressor cells were increased by vascular endothelial growth factor (VEGF) expression in human ovarian cancer, resulting in suppressed immunity [78]. The number of intraepithelial CD8+ tumor infiltrating lymphocytes and a high ratio of CD8+/Treg are associated with a positive prognosis in epithelial ovarian cancer [79]. Our study showed that methyl gallate inhibited TNF-α and IL-1β production in human U937 macrophages (Fig. 6). Our results are consistent with those findings observed using mouse macrophages [72, 73] in that methyl gallate exert anti-inflammatory effects. In our study, we observed that at the same concentration examined, methyl gallate significantly suppressed TNF-α production ($p \leq 0.01$), whereas gallic acid showed no appreciable effect on TNF-α production (Fig. 6B). Further, at the same concentration studied, methyl gallate significantly suppressed IL-1β production ($p \leq 0.05$), while gallic acid had no significant effect on IL-1β production (Fig. 6D). Gallic acid, also known as 3,4,5-trihydroxybenzoic acid, is a natural secondary metabolite and widely present in various plants [80]. Gallic acid is reported to suppress TNF-α and IL-1β levels in gouty arthritis mice model by inhibiting NLR family pyrin domain containing 3 (NLRP3) inflammasome activation [81]. Interestingly, studies on the levels of TNF-α using either RNA in-situ hybridization of tissue arrays or semi-quantitative reverse polymerase chain reaction of mRNA in ovarian cancer have shown that TNF-α expression was present at higher levels in ovarian carcinoma compared to normal tissues [82]. Future studies on understanding the pharmacological mechanism of methyl gallate and its effects on refractory ovarian cancer cells are warranted.
In conclusion, the leaf extracts of L. indica and its selected phytoconstituents were successfully investigated for their effects on human ovarian cancer cells and in combination with oxaliplatin and NK cells. The crude leaf extract of L. indica was found to enhance the susceptibility of ovarian cancer cells to NK cell cytolysis. Its phytoconstituent methyl gallate was found to upregulate the expression of stress ligands for NK cell receptors and elevate the sensitivity of drug-resistant human ovarian cancer cells to NK cell cytolysis. Our findings suggest that the combined effect of methyl gallate, oxaliplatin and NK cells in ovarian cancer cells warrants further investigation. Our work is a step towards better scientific understanding of the traditional anticancer use of L. indica.
## Supplementary Information
Additional file 1: Supplementary Fig. 1. Overall flow-chart of the methods. Supplementary Fig. 2. Increased expression of stress ligands for NK cell receptors in ovarian cancer cells after treatment with (A) L. indica ethyl acetate fraction, (B) methyl gallate, and (C) combination of methyl gallate and oxaliplatin. OVCAR-5 cells were treated for 48 h with or without (A) L. indica ethyl acetate fraction (EA, 0.3 mg/mL), (B) methyl gallate (MG, 0.1 mg/mL), or (C) combination of methyl gallate (MG, 0.1 mg/mL) and oxaliplatin (10 µM), and then phenotype analyzed by FACS for the indicated ligands of NK cells: CD112, CD115, MIC-A/B, ULBP-1, ULBP-2, ULBP-3, DR4 (TRAIL-R1), and DR5 (TRAIL-R2). The relative total mean fluorescence intensities (MFI) of each stress ligand were compared between untreated cells (blue solid line) and treated cells (red solid line). The isotype antibody controls are represented by the green dotted line. Numbers indicate the total MFI for each respective ligand. Histograms of one representative experiment of three are shown. Supplementary Fig. 3. Gallic acid had no significant effect on the expression of stress ligands for NK cell receptors in human ovarian cancer cells. OVCAR-5 cells were treated with or without gallic acid (0.03 mg/mL) for 48 h and then phenotype analyzed by FACS for the indicated stress ligands of NK cells: (a) CD112, (b) CD115, (c) MIC-A/B, (d) ULBP-1, (e) ULBP-2, (f) ULBP-3, (g) DR4 (TRAIL-R1) and (h) DR5 (TRAIL-R2). The relative mean fluorescence intensities of each stress ligand were compared between untreated cells (blue bars) and treated cells (red bars), and results presented are mean ± SD of three independent experiments. There was no statistical difference between treated and untreated cells for each stress ligand. ns, not significant. Supplementary Fig. 4. Schematic figure of some key findings.
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|
---
title: Anemonin reduces hydrogen peroxide-induced oxidative stress, inflammation and
extracellular matrix degradation in nucleus pulposus cells by regulating NOX4/NF-κB
signaling pathway
authors:
- Zhijia Ma
- Pengfei Yu
- Xiaochun Li
- Feng Dai
- Hong Jiang
- Jintao Liu
journal: Journal of Orthopaedic Surgery and Research
year: 2023
pmcid: PMC10007850
doi: 10.1186/s13018-023-03679-8
license: CC BY 4.0
---
# Anemonin reduces hydrogen peroxide-induced oxidative stress, inflammation and extracellular matrix degradation in nucleus pulposus cells by regulating NOX4/NF-κB signaling pathway
## Abstract
### Background
Excessive oxidative stress plays a critical role in the progression of various diseases, including intervertebral disk degeneration (IVDD). Recent studies have found that anemonin (ANE) possesses antioxidant and anti-inflammatory effects. However, the role of ANE in IVDD is still unclear. Therefore, this study investigated the effect and mechanism of ANE on H2O2 induced degeneration of nucleus pulposus cells (NPCs).
### Methods
NPCs were pretreated with ANE, and then treated with H2O2. NOX4 was upregulated by transfection of pcDNA-NOX4 into NPCs. Cytotoxicity was detected by MTT, oxidative stress-related indicators and inflammatory factors were measured by ELISA, mRNA expression was assessed by RT-PCR, and protein expression was tested by western blot.
### Results
ANE attenuated H2O2-induced inhibition of NPCs activity. H2O2 enhanced oxidative stress, namely, increased ROS and MDA levels and decreased SOD level. However, these were suppressed and pretreated by ANE. ANE treatment repressed the expression of inflammatory factors (IL-6, IL-1β and TNF-α) in H2O2-induced NPCs. ANE treatment also prevented the degradation of extracellular matrix induced by H2O2, showing the downregulation of MMP-3, 13 and ADAMTS-4, 5 and the upregulation of collagen II. NOX4 is a key factor regulating oxidative stress. Our study confirmed that ANE could restrain NOX4 and p-NF-κB. In addition, overexpression of NOX4 counteracted the antioxidant and anti-inflammatory activities of ANE in H2O2-induced NPCs, and the inhibition of the degradation of extracellular matrix induced by ANE was also reversed by overexpression of NOX4.
### Conclusion
ANE repressed oxidative stress, inflammation and extracellular matrix degradation in H2O2-induced NPCs by inhibiting NOX4/NF-κB pathway. Our study indicated that ANE might be a candidate drug for the treatment of IVDD.
## Introduction
Low back pain (LBP) is a common clinical disease, followed by a huge family health burden and social and economic burden [1]. About $70\%$-$80\%$ of people have to experience LBP at least once in their life [2]. Intervertebral disk degeneration (IVDD) is considered to be one of the prime reasons of LBP [3]. The intervertebral disk structure includes the endplates, the peripheral concentric fibrosis and the nucleus pulposus [4]. The nucleus pulposus (NP) tissue is the main functional structure of the intervertebral disk, whose tolerance to harmful stimuli is less than that of the fibrous ring and endplate [5]. NP degeneration plays a more and more important role in IVDD [6, 7]. Degeneration of NP is manifested by the loss of extracellular matrix and collagen, proteoglycan and the upregulation of matrix metalloproteinases (MMPs) family [8]. However, the pathological mechanism of NP degeneration is complex and has not been clearly defined. Hence, the study of the pathological mechanism of NP degeneration is critical for the potential therapeutic strategies of IVDD.
Oxidative stress refers to the downregulation of antioxidant defense system function in the body, thus Large amount of ROS is accumulated [8], which can amplify apoptosis and aging of nucleus pulposus cells (NPCs) and enhance inflammation, and then eventually cause to IVDD [9]. Moreover, the apoptosis rate was obviously reduced by downregulating the expression of ROS in human NPCs [10]. The NOX family consists of seven members (NOX1-5 and DUOX1-2), of which NOX4 possesses the wide distribution of cell types [11]. It has been reported that NOX4 existed in NPCs and participated in modulating cell senescence [12]. Knockdown of NOX4 gene could significantly slow down the IVDD [13].
Multiple proinflammatory factors (e.g., IL-6, IL-1β and TNF-α) were significantly increased in the development of IVDD [14]. These proinflammatory factors could further induce the production of MMPs and ADAMSTs, resulting in excessive decomposition of extracellular matrix and accelerating IVDD [14]. NF-κB widely distributed in cytoplasm and plays a crucial role in regulating oxidative stress and inflammatory [15]. NOX4 could activate NF-κB pathway, and then upregulated the inflammatory factors and oxidative stress [16, 17]. Therefore, reducing inflammation and oxidative stress in NPCs by reducing the NOX4/NF-κB axis is a key strategy for treatment of IVDD.
Anemonin (ANE) mainly exists in Ranunculaceae and Gramineae plants (such as Ranunculus, Pulsatilla, clematis, citronella root and Anemone japonica) [18]. ANE has multiple biological activities, such as anti-bacterial, anti-inflammatory and antioxidation [19–21]. ANE extracted from Clematis could significantly alleviate the inflammation of rheumatoid arthritis through percutaneous administration [20]. ANE showed neuroprotective effect by the antioxidant activity and inhibiting apoptosis [22]. Nevertheless, the effect of ANE in IVDD is still unclear.
In our study, we explored the effect and related mechanism of ANE on IVDD in vitro. NPCs degeneration was induced by H2O2 [23–25], and then, the effects of ANE on oxidative stress, extracellular matrix degeneration and inflammation were assessed. Mechanistically, the effect of NOX4/NF-κB on the protective effect of ANE on NPCs was also explored.
## Cell culture
NP tissues were obtained from 12 patients (gender, 5 women and 7 men; Pfirrmann grade, 3 III, 5 IV and 4 V; average age, 37 years; range 21–63 years) with IVDD at Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine for surgical treatment. All patients were informed and signed the informed consent form. Our research was approved by ethics committee of Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine (No: S377). Primary NPCs were isolated and cultured according to previous study [25]. Primary NPCs were cultured in DMEM/F12 medium including $10\%$ fetal bovine serum, 100 U/ml penicillin and 100 mg/L streptomycin. The cell culture condition was at 37 °C, $5\%$ CO2 in an incubator. The NPCs in second generation were used for subsequent experimental studies. Nucleus pulposus cells were verified by performing immunofluorescence staining of aggrecan and collagen II.
## Cell treatment
NPCs were inoculated into 96-well plates at the concentration of 5 × 103/well. After 24 h, the cells were treated with H2O2 (0, 25, 50, 100, 200, 500, 1000 μM) for 24 h or ANE (0, 1, 2, 5, 10, 20, 50, 100 μM) for 48 h. Cytotoxicity was detected by MTT method.
After 24 h in 96-well plates. NPCs were pretreated with ANE (0, 2, 5, 10 μM) [26, 27] for 24 h, and then treated with H2O2 (200 μM) for another 24 h. pcNDA-NOX4 was transfected into NPCs using Lipofectamine 2000 according to the application manual, which was applied to upregulate the expression of NOX4.
## MTT assay
NPCs (5 × 103/well) were inoculated into 96-well plates and cultured overnight. The cells were treated according to the above method. Then, 50 μl MTT reagent was added to each well, and the cells were incubated for 4 h at 37 °C. After discarding the supernatant, 150 μl/well of dimethyl sulfoxide (DMSO) solution was added into each well, and then the plates were shocked for three times (30 s each time). The absorbance value of 570 nm was detected with a microplate reader.
## ROS assay
The ROS level in NPCs was measured through the ROS test kit (Beijing Bioteke Company) according to the introduction. After washed twice with sterile PBS, the cells were treated with 10 μM DCFH-DA for 20 min at 37 °C in dark, and turned every 4 min. Then, the fluorescence intensity of DCFH-DA was assessed.
## ELISA assay
NPCs (1 × 105/well) were inoculated into 6-well plates and cultured overnight. Then, the cells were treated for 24 h according to the previous description. Then, malondialdehyde (MDA), superoxidedismutase (SOD), interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) contents were measured according to the instructions of the corresponding kit.
## RT-PCR
Total RNA of NPCs was isolated with Trizol lysate. The extracted RNA was reverse-transcribed into cDNA according to the instructions of cDNA synthesis kit. Synthetic cDNA, as template, was mixed with 0.4 μl of ROX Reference Dye II, specific upstream and downstream amplification primers (0.8 μl) and 10 μl of SYBR® Premix EX TaqTM II to construct RT-PCR system. RT-PCR was performed according to the following procedure: 95 °C for 30 s, cycle once; 95 °C for 5 s, 60 °C for 30 s, 40 cycles. β-actin was taken as the internal parameter, and the mRNA level of the target gene was evaluated by 2−ΔΔct. The primer sequence was as follows: β-actin, F: 5′-CACCATTGGCAATGAGCGGTTC-3′, R: 5′-AGGTCTTTGCGGATGTCCACGT-3′); NOX4, F: 5′-TGTTGG‐GCCTAGGATTGTGTT-3′ and R: 5′-AGGGACCTTCTGTGATCCTCG-3′; MMP-3, F: 5′-ATGATGAACGATGGACAGATGA-3′ and R: 5′-CATTGGCTGAGTGAAAGAGACC-3′; MMP-13, F: 5′-GGCCAGAACTTCCCAACCA-3′ and R: 5′-ACCCTCCATAATGTCATACCC-3′; ADAMTS-4, F: 5′-ACCCAAGCATCCGCAATC-3′ and R: 5′-CAGGTCCTGACGGGTAAACA-3′; ADAMTS-5, F: 5′-CGACAAGAGTCTGGAGGTGAG-3′ and R: 5′-CGTGAGCCACAGTGAAAGC-3′; collagen II, F: 5′-GGCAATAGCAGGTTCACGTACA-3′ and R: 5′-CGATAACAGTCTTGCCCCACTT-3′;
## Western blot
NPCs were seed in 6-well plates. When cell number reached 5 × 106/well, 200 μL RIPA reagent/well was used to extract the total proteins of NPCs in different treatment groups, and the proteins were quantitatively analyzed with BCA protein detection kit according to the instruction. The same amount of protein sample was separated through $12\%$ SDS-PAGE, and then, the protein was electrically transferred to PVDF membranes. The membranes were blocked with $5\%$ skimmed milk powder, and then, the corresponding primary antibody was added for overnight incubation at 4 °C. The membranes were added with secondary antibody and incubated for 1.5 h. ECL solution was added for visualizing the bands, GAPDH was used as internal reference, and Image J software was used for quantitative analysis of protein gray. The primary antibodies used in the experiment included: MMP-3 (ab52915, Abcam, 1:1000), MMP-13 (ab51072, Abcam, 1:1000), ADAMTS-4 (ab84792, Abcam, 1:1000), ADAMTS-5 (ab41037, Abcam, 1:1000), collagen II (ab188570, Abcam, 1:1000), NOX4 (ab154244, Abcam, 1:1000), NF-κB (ab32536, Abcam, 1:1000), p-NF-κB (ab76302, Abcam, 1:1000), GAPDH (ab181602, Abcam, 1:1000). The second antibody used in the experiment included: Goat Anti-Rabbit IgG H&L (1:5000, ab96899, Abcam, Goat Anti-Mouse IgG H&L (1:5000, ab96879, Abcam).
## Statistical analyses
The data was analyzed by SPSS 22.0 software. The results are expressed in mean ± standard deviation (SD). One-way ANOVA followed by Tukey’s test is used for three or more groups comparison. $P \leq 0.05$ was statistically significant. Every experiment was performed at least three independent measurements.
## ANE resisted H2O2-induced inhibition of NPCs activity
The chemical formula of ANE is exhibited in Fig. 1A. Our finding indicated that aggrecan and collagen II were expressed in more than $95\%$ of cells, which confirmed the cells obtained were NPCs (Fig. 1B). Subsequently, we explored the cytotoxicity of ANE and its protective effect in H2O2-induced NPCs. ANE (0, 1, 2, 5, 10, 20, 50 μM) was used to treat NPCs for 48 h. The results showed that only 20 μM and 50 μM ANE reduced the activity of NPCs (Fig. 1C). ANE in the concentration range of 2, 5, 10 μM was used for subsequent studies. When NPCs were exposed to 200 μM H2O2 for 24 h, the activity of nucleus pulposus cells was obviously repressed (Fig. 1D). Interestingly, pretreatment with ANE could effectively attenuate H2O2-induced inhibition of NPCs activity at a concentration dependent manner (Fig. 1E).Fig. 1ANE repressed the cytotoxic induced by H2O2 in NPCs. A The chemical formula of ANE is shown. B Immunofluorescence detection of aggrecan and collagen II was performed in isolated cells from IVDD tissues. C After treatment with ANE for 48 h, cell viability was measured by MTT assay. D After treatment with H2O2 for 24 h, cell viability was detected by MTT assay. E After treatment with or without ANE for 24 h, the cells were treated with or without H2O2 for 24 h, and then the cell viability was assessed. * $p \leq 0.05$ versus control group, **$p \leq 0.01$ versus control group
## ANE inhibited oxidative stress and inflammation in H2O2-induced NPCs
Next, the effect of ANE on oxidative stress and inflammation was measured in NPCs. NPCs exposed to H2O2 showed significantly higher DCFDA fluorescence intensity than control group, indicating that H2O2 obviously raised ROS levels in NPCs (Fig. 2A, B). Moreover, ANE attenuated the upregulation in ROS levels in H2O2-treated NPCs (Fig. 2A, B). At the same time, MDA and SOD were also detected. The results suggested that MDA level was amplified, while SOD level was lessened in the H2O2 treated group (Fig. 2C, D). These effects were recovered by ANE pretreatment (Fig. 2C, D). Inflammatory factors is commonly upregulated during IVDD. Furthermore, H2O2-induced increases of IL-6, IL-1β and TNF-α levels were attenuated by ANE pretreatment (Fig. 2E–G).Fig. 2ANE attenuated oxidative stress and inflammation in H2O2-induced NPCs. A, B The ROS level was assessed by DCFDA method. C–G ELISA assay showing MDA, SOD, IL-6, IL-1β, TNF-α levels. ** $p \leq 0.01$ versus control group. ## $p \leq 0.01$ versus H2O2 treatment group
## ANE reduced extracellular matrix degeneration in H2O2-induced NPCs
The degeneration of extracellular matrix of NPCs is a symbol of IVDD [28]. Therefore, whether ANE could restore the degeneration of extracellular matrix was explored in H2O2-induced NPCs. MMP-3, MMP-13, ADAMTS-4 and ADAMTS-5 were upregulated in H2O2-induced NPCs. Nevertheless, ANE blocked the effect of H2O2 on these enzymes. Conversely, H2O2 treatment reduced the expression of collagen II, which was repressed by ANE (Fig. 3A–F). The mRNA expression was also assessed. Similarly, mRNA expression of MMP-3, MMP-13, ADAMTS-4 and ADAMTS-5 were significantly increased and collagen II was obviously decreased, and which were reversed by ANE treatment (Fig. 3G–K).Fig. 3ANE inhibited H2O2-induced degeneration of extracellular matrix in NPCs. A–F The protein expression of MMP-3, MMP-13, ADAMTS-4, ADAMTS-5 and collagen II was measured by western blot. G–K The mRNA expression of MMP-3, MMP-13, ADAMTS-4, ADAMTS-5 and collagen II was measured by RT-PCR. ** $p \leq 0.01$ versus control group. ## $p \leq 0.01$ versus H2O2 treatment group. ** $p \leq 0.01$ versus control group. ## $p \leq 0.01$ versus H2O2 treatment group
## ANE restrained NOX4/NF-κB signaling pathway in H2O2-induced NPCs
Previous studies have demonstrated that NOX4/NF-κB signaling pathway participated in progress of NPCs degeneration, especially through affecting oxidative stress and inflammation [13]. Similarly, in this study, the NOX4 and p-NF-κB/NF-κB ratio were enlarged in H2O2 exposed NPCs. Interestingly, pretreatment with ANE significantly inhibited NOX4 expression and reduced the p-NF-κB/NF-κB ratio (Fig. 4A–C). Moreover, ANE could recover the enhanced effect of H2O2 on NOX4 mRNA expression (Fig. 4D). Hence, we suspected that the effect of ANE on NPCs might be achieved by inhibiting the NOX4/NF-κB signaling pathway. To demonstrate our hypothesis, NOX4 was overexpressed in NPCs by transfecting pcDNA-NOX4. Moreover, pcDNA-NOX4 transfection upregulated NOX4 expression in NPCs (Fig. 4E–G).Fig. 4ANE restrained the activation of NOX4/NF-κB signaling pathway. A–C The protein expression of NOX4, NF-κB and p- NF-κB were assessed by western blot. D The mRNA expression of NOX4 was assessed by RT-PCR. E–G After transfected with or without pcDNA-NC or pcDNA-NOX4, the mRNA and protein expression of NOX4 was detected by RT-PCR and western blot. ** $p \leq 0.01$ versus control group. ## $p \leq 0.01$ versus H2O2 treatment group
## ANE antagonized H2O2-induced degeneration of NPCs by inhibiting NOX4/NF-κB pathway
It was explored whether ANE protected NPCs by mediating NOX4/NF-κB pathways. Our finding indicated that ANE attenuated ROS and MDA levels and raised SOD level in NPCs induced by H2O2, which reflected that ANE reduced oxidative stress. Nevertheless, this effect was restored by NOX4 transfection (Fig. 5A–D). ANE repressed the inflammatory factors, which was restored by NOX4 overexpression (Fig. 5D, E). In the H2O2 treated group, extracellular matrix degeneration was enhanced through the reduction of collagen II and the upregulation of matrix enzymes MMP-3, MMP-13, ADAMTS-4 and ADAMTS-5. Pretreatment with ANE restrained the degeneration of extracellular matrix, while overexpression of NOX4 counteracted the effect of ANE on extracellular matrix (Fig. 6A–F). In addition, ANE-induced the inactivating of the NOX4/NF-κB pathway was also blocked by the overexpression of NOX4 (Fig. 6G–I). Those indicated that ANE resisted oxidative stress, inflammation and extracellular matrix degeneration by restraining NOX4/NF-κB pathway in H2O2-induced NPCs. Fig. 5ANE inhibited oxidative stress and inflammation in H2O2-induced NPCs through inactivating NOX4/NF-κB pathway. A, B The ROS level was assessed by DCFDA method. C–G ELISA assay showing MDA, SOD, IL-6, IL-1β, TNF-α levels. ** $p \leq 0.01$ versus control group. ## $p \leq 0.01$ versus H2O2 treatment group. && $p \leq 0.01$ versus ANE + H2O2 treatment groupFig. 6ANE suppressed H2O2-induced degeneration of extracellular matrix in NPCs through inactivating NOX4/NF-κB pathway. A–F The expression of MMP-3, MMP-13, ADAMTS-4, ADAMTS-5 and collagen II was measured by western blot. G–I Western blot showing the expression of NOX4, NF-κB and p- NF-κB. **$p \leq 0.01$ versus control group. ## $p \leq 0.01$ versus H2O2 treatment group. && $p \leq 0.01$ versus ANE + H2O2 treatment group
## Discussion
IVDD is the main cause of LBP, but its pathogenesis has not been completely cleared [29]. NPCs play a critical role in IVDD [30]. The recovery activity of damaged NPCs is related to the treatment and prognosis of IVDD [30]. However, oxidative stress promotes the degeneration of NPCs, and then affects the repair of intervertebral disk injury [8, 31]. Hence, it is critical to screen effective and low toxic drugs that could protect NPCs and improve IVDD.
In recent studies, it was showed that ANE possessed a widely biological activities, including anti-inflammatory, antioxidant, anti-bacterial and immune regulation [32–34]. It has been reported that ANE showed neuroprotective effect by providing antioxidant activity and inhibiting apoptosis in MCAO rats [22]. Other studies have reported that ANE could effectively alleviate osteoporosis and osteoarthritis. ANE could reduce the loss of extracellular matrix by inhibiting IL-1β/NF-κB pathway during osteoarthritis of mouse [27]. The treatment of ANE could significantly reduce oxidative stress and inflammation in osteoporosis, and further studies showed that ANE improved bone destruction by regulating MAPK-mediated NF-κB signaling pathway [26]. Nevertheless, the therapeutic effect of ANE on IVDD was still unclear. In this study, oxidative stress stimulation of NPCs was achieved by H2O2 treatment, and then we explored that the damaging effect of H2O2 on NPCs and whether ANE could protect NPCs under H2O2. The study revealed that H2O2 significantly depressed the cell viability of NPCs, and addition of ANE could gradually improve the viability of NPCs under H2O2. The findings indicated that ANE could decay the damage of H2O2 to NPCs.
Under normal circumstances, the body has a set of balanced antioxidant system to maintain the balance of free radical metabolism. However, when the body is affected by diseases, exogenous poisons and other factors, ROS is rapidly generated and accumulated, resulting in the imbalance between oxidative stress and antioxidation [35]. Excessive formation of ROS will lead to damage to cell function and pathological changes [36]. MDA is the metabolite of the peroxidation of unsaturated fatty acids in biofilm caused by ROS [37]. SOD is a natural antioxidant enzyme produced by organisms, which removes ROS through disproportionation reaction and blocks the chain reaction of lipid peroxidation [37]. The contents of ROS, MDA and SOD in cells are often used as the representative indicators to assess oxidative stress damage [38]. Melatonin prevented H2O2-induced decrease of NPCs activity and increase of ROS and MDA levels [39]. Plumbagine could reduce the expression of oxidative stress and inflammatory factors induced by H2O2 in NPCs [40]. Our study showed that ROS and MDA of NPCs were significantly raised, and SOD was reduced under the effect of H2O2, indicating the enhancement of oxidative stress. However, ROS and MDA in NPCs pretreated with ANE were significantly repressed, and SOD was amplified, indicating that ANE played a protective role on NPCs induced by H2O2.
It has been reported that inflammatory mediators could further promote IVDD [14]. IL-6, IL-1β and TNF-α are important inflammatory factors during IVDD [41]. IL-6 could aggregate inflammatory cells, regulate the overexpression of matrix metallolytic enzymes, promote the degeneration of extracellular matrix and enhance IVDD [42]. It was found that resveratrol improved NPCs growth and degeneration of extracellular matrix by IL-6/JAK/STAT3 pathway [43]. The study has confirmed that IL-1β and TNF-α are upregulated in IVDD tissues compared with normal intervertebral disk tissues [44]. In this study, H2O2 stimulated the significant increases inflammatory factors in NPCs. Moreover, ANE restrained IL-6, IL-1β and TNF-α levels in a concentration dependent manner in H2O2-treated NPCs. These results indicated that ANE could attenuated inflammation to protect NPCs.
The overexpression of NOX4 was directly related to ROS accumulation, cell aging, apoptosis and the upregulation of metalloproteinases [45]. Silencing the expression of NOX4 has been thought as a new targeting strategy for IVDD [13]. Meanwhile, ROS simulates inflammation and oxidative stress through regulating NF-κB signaling pathway [46]. It was reported that isoquercetin improves oxidative stress and neuronal apoptosis in ischemia–reperfusion model rats and oxygen glucose deprived neurons by inhibiting activation of NOX4/ROS/NF-κB pathway [47]. Feng et al. firstly found the presence of NOX4 in NPCs of intervertebral disk and confirmed that the important roles of NOX4/NF-κB and MAPK in IVDD [13]. In this study, we found that H2O2 upregulated NOX4 and p-NF-κB in NPCs, which is consistent with the previous study [13]. In this study, after pretreatment with ANE, NOX4 and p-NF-κB were significantly reduced. However, overexpression of NOX4 could counteract the effect of ANE on H2O2-induced NPCs. These results explained that the protective effect of ANE on NPCs was related with NOX4/NF-κB pathway.
There are still some limitations in this paper. The effect of ANE on the apoptosis of NPCs was not been explored. Moreover, whether ANE could alleviate IVDD has not been explored in vivo. These will be the focus of our future study.
## Conclusion
Our finding for the first time demonstrated that ANE prevented cell viability, reduced oxidative stress injury and repressed inflammatory factors in H2O2-induced NPCs. Mechanistically, ANE protected NPCs by depressing NOX4/NF-κB signaling pathway. This experimental study is conducive to the clinical application of ANE to alleviate IVDD.
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|
---
title: How does HbA1c predict mortality and readmission in patients with heart failure?
A protocol for systematic review and meta-analysis
authors:
- Jun-Peng Xu
- Rui-Xiang Zeng
- Xiao-Yi Mai
- Wen-Jun Pan
- Yu-Zhuo Zhang
- Min-Zhou Zhang
journal: Systematic Reviews
year: 2023
pmcid: PMC10007851
doi: 10.1186/s13643-023-02179-4
license: CC BY 4.0
---
# How does HbA1c predict mortality and readmission in patients with heart failure? A protocol for systematic review and meta-analysis
## Abstract
### Background
Accumulating evidence suggests that HbA1c levels, a common clinical indicator of chronic glucose metabolism over the preceding 2–3 months, are independent risk factors for cardiovascular disease, including heart failure. However, conflicting evidence obscures clear cutoffs of HbA1c levels in various heart failure populations. The aim of this review is to assess the possible predictive value and optimal range of HbA1c on mortality and readmission in patients with heart failure.
### Methods
A systematic and comprehensive search will be performed using PubMed, Embase, CINAHL, Scopus, and the Cochrane Library databases before December 2022 to identify relevant studies. All-cause mortality is the prespecified primary endpoint. Cardiovascular death and heart failure readmission are secondary endpoints of interest. We will only include prospective and retrospective cohort studies and place no restrictions on the language, race, region, or publication period. The ROBINS-I tool will be used to assess the quality of each included research. If there were sufficient studies, we will conduct a meta-analysis with pooled relative risks and corresponding $95\%$ confidence intervals to evaluate the possible predictive value of HbA1c for mortality and readmission. Otherwise, we will undertake a narrative synthesis. Heterogeneity and publication bias will be assessed. If heterogeneity was significant among included studies, a sensitivity analysis or subgroup analysis will be used to explore the source of heterogeneity, such as diverse types of heart failure or patients with diabetes and non-diabetes. Additionally, we will conduct meta-regression to examine the time-effect and treatment-effect modifiers on all-cause mortality compared between different quantile of HbA1c levels. Finally, a restricted cubic spline model may be used to explore the dose-response relationship between HbA1c and adverse outcomes.
### Discussion
This planned analysis is anticipated to identify the predictive value of HbA1c for mortality and readmission in patients with heart failure. Improved understanding of different HbA1c levels and their specific effect on diverse types of heart failure or patients with diabetes and non-diabetes is expected to be figured out. Importantly, a dose-response relationship or optimal range of HbA1c will be determined to instruct clinicians and patients.
### Systematic review registration
PROSPERO registration details: CRD42021276067
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13643-023-02179-4.
## Background
Heart failure is a major cardiovascular complication of diabetes mellitus (DM), and they beget each other, while the links between the 2 conditions are not fully elucidated. In the heart failure cohort, including heart failure with reduced and preserved left ventricular ejection fraction (LVEF), the prevalence of DM ranges from 10 to $47\%$ [1, 2]. In particular, the prevalence of DM is more than $40\%$ in newly hospitalized patients with heart failure [3]. In addition, robust evidence suggests that DM is associated with a nearly 2- to 4-fold increase in the risk of incident heart failure, even after adjustment for other cardiovascular risk factors [4]. Furthermore, poor glycemic control is related to an increased incident of heart failure: for a $1\%$ increase in HbA1c, the risk of incident heart failure increases by $15\%$ [5]. Thus, elevated HbA1c might be closely related to incidence and prognosis of heart failure patients with DM. However, the question how does HbA1c predict mortality and readmission in patients with heart failure has not been figured out. Although current guidelines recommend a target level of HbA1c < $7\%$ in type 2 DM patients [6], some studies proved that HbA1c could independently predict mortality with U-shaped relationship, and the lowest mortality HbA1c range was 6.5–$7.9\%$ [7, 8], while the other showed a direct relationship with HbA1c < $6.5\%$ or inverse relationship with HbA1c ≥ $8.7\%$ [9, 10]. Furthermore, Lejeune et al [11]. demonstrated HbA1c > $7\%$ was protective for patients following heart failure with preserved ejection fraction, whereas some studies indicated HbA1c > $7\%$ was associated with higher rate of adverse outcomes, including mortality, in patients following heart failure with reduced ejection fraction [12, 13]. These mean the effect of different HbA1c levels on adverse outcomes may be various in response to the types of heart failure. In spite of accumulating evidence, there are no further pooled analysis to figure out what HbA1c levels should be controlled for heart failure patients with pre-DM or non-DM. Thus, we aim to present a qualitative and quantitative analysis from observational cohort studies with conclusive evidence and summary existing information on the relationship of HbA1c levels with mortality and readmission in patients with heart failure.
## Review design
Reviewing and synthesizing the observational research will address a critical gap in the evidence on the potential association between HbA1c exposure and mortality and readmission in patients with heart failure. Furthermore, observational research typically allows for longer follow-up time with fewer ethical concerns and can examine the outcomes of a broader participant group under naturalistic circumstances. Thus, we will include prospective and retrospective observational cohort studies defined according to the Cochrane study design guide [14] and will perform meta-analyses in cases of sufficient available data. Otherwise, we will undertake a systematic review. The study has been registered to the International Prospective Register of Systematic Reviews (PROSPERO registration number: CRD42021276067). This protocol adheres to the latest Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement (see Additional file 1) [15].
## Eligibility criteria
The inclusion and exclusion criteria are defined according to the Population of interest, Exposure, Comparator and Outcome (PECO) statements and outlined in Table 1. Our population of interest will be adult participants with all-type heart failure regardless of glucose metabolic state. In accordance with the rationale and objective described earlier, exposure, comparator, and outcomes were various HbA1c levels and the related risk of all-cause mortality, cardiovascular mortality, and heart failure readmission. Diagnosis and classification of heart failure are following the guidelines: heart failure with reduced (LVEF < $40\%$), mid-range (LVEF: 40 to < $50\%$), or preserved ejection fraction (LVEF ≥ $50\%$) [16]. Definition of different glucose metabolic state according to HbA1c using the International Diabetes Expert Committee criteria: [1] without DM, < $6.5\%$, and with DM, ≥ $6.5\%$ [17].Table 1Summary of inclusion and exclusion criteriaInclusion criteriaExclusion criteriaParticipantsAdult patients with heart failure regardless of glucose metabolic statePatients with acute coronary syndrome or cardiovascular revascularization less than 3 monthsExposureReporting HbA1c as continuous and/or categorical variablesOtherComparatorVarious categorical HbA1c levelsNAOutcomesAll-cause mortality, cardiovascular death and heart failure readmissionNAStudy designProspective and retrospective cohortCase-control, cross-sectional, randomized controlled trials, case report, case series, and any other non-relevant studies
## Literature review
A literature search will be conducted using PubMed, Embase, CINAHL, Scopus, and the Cochrane Library databases from the date of inception until December 2022. The following Mesh terms will be used in search: heart failure, systolic heart failure, and diastolic heart failure combined with glycated hemoglobin a. Corresponding free texts are as follows: cardiac failure, heart decompensation, congestive heart failure, right-sided heart failure, myocardial failure, left-sided heart failure, hemoglobin a1c, hemoglobin a1c, HbA1c, glycated hemoglobin, glycated hemoglobin, glycosylated hemoglobin, glycosylated hemoglobin, glycohemoglobin a, or glycohemoglobin a. The search strategy for PubMed is presented in Additional file 2. Additional eligible studies from previous version of established reviews and the reference lists of retrieved articles will be manually reviewed, in cases of using other databases simultaneously. Search results will be exported, and duplicates will be removed using software NoteExpress.
## Study screening and selection
According to the inclusion and exclusion criteria, the process of identifying, screening, and including of those studies is shown in the PRISMA flow chart (Fig. 1). All titles and abstracts will be independently reviewed by two authors to determine appropriate studies included. Similarly, other two independent authors will review full texts and compare these against predefined eligibility criteria. A senior author will resolve any conflicts regarding inclusion or exclusion of articles through discussion. All authors hold expertise relevant to the review subject matter. An excluded study list will record articles that did not meet inclusion criteria, but that may provide information of interest to readers. Fig. 1Literature search PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) consort diagram
## Data extraction and risk of bias assessment
We will extract data of interest from all included studies using revised versions of a previously piloted data extraction form (Additional file 3). All review screening, bias assessment, and data extraction will be managed on the excel software. Two authors will blindingly extract the data on first author’s name, publication year, age range and/or mean age (years), number of participants, mean follow-up duration, the types of heart failure and glucose metabolic state, HbA1c exposure levels, and RRs and their $95\%$ CIs as well as event numbers for each exposure category (Table 2). The quality of the studies will be also assessed by the two authors and using the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I), a validated quality assessment instrument for observational cohort studies [18]. It encompasses 7 domains: confounding, selection of participants, classification of the intervention, deviation from the intended intervention, missing data, measurement of outcomes, and selection of the reported results. Each domain will be judged as either low, moderate, serious, or critical risk of bias or no information available. If agreement in bias assessment cannot be reached, the dispute will be resolved with the help of other two investigators. Any discrepancies will be resolved through discussion under supervision of a senior author. Publication bias will be assessed using funnel plots’ asymmetry and tested with Egger’s asymmetry test and Begg’s test ($p \leq 0.05$) when there are at least 10 studies. Table 2Characteristics of included studiesFirst authorYearNumber screenedAge at screeningMen (%)Follow-upHF typesGlucose metabolic stateCountry/continentQuality scoreAuthor nameYear of publicationNumber of participantsMean age of participantsMen (%)Mean follow-up timeHFpEF, HFmrEF, and HFrEFWith DM and without DM (or including without known DM)Country/continentROBINS-I scoresHF heart failure, HFpEF heart failure with preserved ejection fraction, HFmrEF heart failure with mid-range ejection fraction, HFrEF heart failure with reduced ejection fraction, DM diabetes mellitus
## Data synthesis
Evidence regarding the association with HbA1c exposure of the three outcomes, including all-cause mortality, cardiovascular mortality, and heart failure readmission, will be reported according to the latest PRISMA criteria [15] and satisfy the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) for Meta-analyses of Observational Studies [19]. The predictive value of HbA1c was measured in the form of a relative risk [risk ratio (RR), odds ratio (OR) or hazard ratio (HR)] and their $95\%$ confidence interval (CI) or the event number and total number of each group. If there are sufficient studies, we will conduct meta-analysis with pooled relative risks and corresponding $95\%$ CI to assess the possible predictive value and optimal range of HbA1c on mortality and readmission. Otherwise, we will undertake a narrative synthesis, or in cases of primary studies with unavailable data, and extremely high heterogeneity between their populations and design.
## Statistical analysis
We will calculate the average RRs for various categorical HbA1c levels using a fixed-effects or random-effects model. If RRs and their $95\%$ CI cannot be available in the papers, the unadjusted ones will be calculated through original data published in the studies or contacting the studying authors. The I2 statistic and Cochrane’s Q test are used to estimate the heterogeneity. The I2 value is from 0 to $25\%$ and 26 to $50\%$, indicating little and acceptable moderate heterogeneity, respectively. If heterogeneity is significant among studies (I2 > $50\%$, $p \leq 0.1$), a sensitivity analysis or subgroup analysis will be used to explore the source of heterogeneity. The strength of the body of evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool [20]. All analysis will be conducted with Stata 12.0.
## Subgroup analyses and meta-regression
First, given different cardiac and glucose metabolic dysfunction will influence the risk of survival and readmission, we will abstract relevant data and conduct subgroup analyses to validate the distribution of the most at-risk population if possible. Second, subgroup analyses will be performed to evaluate the specific effect of different HbA1c levels on different types of heart failure and patients with DM or non-DM. Furthermore, the meta-regression will be performed based on time-effect and treatment-effect, including follow-up duration, anti-heart failure, and anti-diabetic drugs, because these may be major factors influencing the primary and secondary outcomes and contribute to high or complete inter-research variance [21]. The main reason may be attenuation of medicine adherence over time [21]. Finally, a dose-response relationship or optimal range of HbA1c to predict adverse outcomes in these population will be determined if possible.
## Discussion
The predictive value of HbA1c levels as a prognostic marker for mortality and readmission is uncertainty in spite of numerous studies with conclusive evidence. Patients with DM represent a large portion of adults with chronic heart failure. Also, patients hospitalized with acute heart failure are always concomitant with hyperglycemia and high levels of HbA1c in cases of no previous diagnosis of DM. Besides, a narrative review on this topic have been previously published and showed that there was certain variation between included studies [22]. Although a previous protocol also studied the predictive effect of HbA1c on the cardiovascular events and mortality [23], it did not either entirely analyze the possibly various effect on different types of population, such as diverse types of heart failure, or explore the dose-response relationship between HbA1c and adverse outcomes. Therefore, it is necessary to conduct a systematic review to figure out what HbA1c levels and how to predict mortality and readmission in patients with heart failure, including the specific effect on diverse types of heart failure or patients with diabetes and non-diabetes.
We anticipate some possible limitations to our systematic review and meta-analysis. Firstly, there may be some problems in primary studies, such as publication bias, information bias, poor statistical analyses, and inadequate reporting of different levels of HbA1c. Second, the follow-up duration of observational studies may be longer but always inconsistent. Third, heart failure itself may also contribute to increased risk of mortality and readmission. However, it is vital to pool all available information on this issue. In order to overcome potential limitations, we will strictly follow the MOOSE and PRISMA guidelines.
## Supplementary Information
Additional file 1. Additional file 2. Additional file 3.
## References
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|
---
title: Promoting physical activity-related health competence to increase leisure-time
physical activity and health-related quality of life in German private sector office
workers
authors:
- Simon Blaschke
- Johannes Carl
- Klaus Pelster
- Filip Mess
journal: BMC Public Health
year: 2023
pmcid: PMC10007852
doi: 10.1186/s12889-023-15391-7
license: CC BY 4.0
---
# Promoting physical activity-related health competence to increase leisure-time physical activity and health-related quality of life in German private sector office workers
## Abstract
### Background
Office workers (OWs) are at risk of low levels of health-enhancing physical activity (HEPA) and impaired health-related quality of life (HRQOL). Interventions based on physical activity-related health competence (PAHCO) aim to facilitate long-term changes in HEPA and HRQOL. However, these assumptions rely on the changeability and temporal stability of PAHCO and have not been tested empirically. This study therefore aims to test the changeability and temporal stability of PAHCO in OWs within an interventional design and to examine the effect of PAHCO on leisure-time PA and HRQOL.
### Methods
Three hundred twenty-eight OWs ($34\%$ female, 50.4 ± 6.4 years) completed an in-person, three-week workplace health promotion program (WHPP) focusing on PAHCO and HEPA. The primary outcome of PAHCO as well as the secondary outcomes of leisure-time PA and HRQOL were examined at four measurement points over the course of 18 months in a pre-post design by employing linear mixed model regressions.
### Results
PAHCO displayed a substantial increase from the baseline to the time point after completion of the WHPP (β = 0.44, $p \leq 0.001$). Furthermore, there was no decrease in PAHCO at the first ($$p \leq 0.14$$) and the second follow-up measurement ($$p \leq 0.56$$) compared with the level at the end of the WHPP. In addition, the PAHCO subscale of PA-specific self-regulation (PASR) had a small to moderate, positive effect on leisure-time PA (β = 0.18, $p \leq 0.001$) and HRQOL (β = 0.26, $p \leq 0.001$). The subscale of control competence for physical training (CCPT) also had a positive small to moderate effect on HRQOL (β = 0.22, $p \leq 0.001$).
### Conclusion
The results substantiate PAHCO’s theoretical characteristics of changeability and temporal stability, and underline the theoretically postulated effects on leisure-time PA and HRQOL. These findings highlight the potential of PAHCO for intervention development, which can be assumed to foster long-term improvements in HEPA and HRQOL in OWs.
### Trial registration
The study was retrospectively registered in the German Clinical Trials Register, which is an approved Primary Register in the WHO network, at the $\frac{14}{10}$/2022 (DRKS00030514).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15391-7.
## Background
Globalisation and persistent advances in information as well as communication technologies have shaped the modern working world. Also, as a result of these advances, the number of office workers (OWs) has steadily increased in recent decades in western societies [1, 2] and represents, for example, a share of more than $40\%$ of all employees in Germany [3]. OWs are primarily engaged in desk-based and digitally assisted on-site or remote occupational activities, such as reading, preparing and giving online or onsite presentations [4].
Partially as a result of these typically inactive and mostly digital occupational activities, OWs are exposed to an increased risk of developing chronic diseases, such as mental disorders [5], musculoskeletal disorders [6] or metabolic syndrome [7]. One crucial indicator for the prevention of chronic diseases [8, 9] is individuals’ health-related quality of life (HRQOL), which considers individuals’ perceived physical, mental, and social health status [10]. Alongside HRQOL as an indicator for detecting mental disorders and chronic diseases [11], HRQOL in OWs is positively linked to work-related measures, including higher employee productivity [12] and lower sickness absenteeism [13]. These findings highlight the importance of HRQOL as a health indicator for OWs and as an indicator for organisational outcomes.
To promote HRQOL, the World Health Organisation (WHO) addresses the health behaviour of individuals and specifically underlines the value of physical activity (PA) [14]. This public health strategy might be especially relevant for OWs, as Biernat and Piatkowska [15] report that fewer than $50\%$ of OWs fulfil the WHO recommendation of at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity PA per week [16]. Moreover, OWs show a lower amount of light and moderate PA and accumulate more sedentary time in comparison with other occupational groups [17]. In addition, OWs do not compensate for the high amount of work-related sedentary time by engaging in longer periods of leisure-time PA [18], which increases the risk of diminished HRQOL [19]. Next to reducing sedentary behavior and the overall need to increase the PA levels in OWs, precisely targeting health-enhancing PA (HEPA) might be crucial, as occupational PA – in contrast with leisure-time PA [15] – shows no or even an inverse relationship with HRQOL in OWs [20, 21]. HEPA comprises all types of PA that benefit health without causing undue harm or risk [22]. The potential of HEPA promotion in OWs is also indicated by a current review, which demonstrates a large positive effect of HEPA interventions on HRQOL in this target group [11]. However, owing to short follow-up periods in most of the studies included, as well as great heterogeneity across the applied interventions, Nguyen et al. [ 11] provide no detailed insights with regard to long-term benefits of HEPA interventions or HEPA intervention development in OWs. In summary, the relevance of OWs in the working population and the risk of impaired HRQOL in this occupational group along with the potential of HEPA highlight the need to promote HEPA among OWs.
Whereas these findings illustrate the potential of HEPA promotion in OWs, explaining PA behaviour is intricate [23, 24]. The complexity of explaining this behaviour is particularly evident for the maintenance of PA [25], with relapse posing a common challenge in this health behaviour [26]. Along with social and environmental factors, current studies substantiate the importance of individual factors, such as dispositions, attitudes and expectations, to change [27] and maintain PA [28]. The specific value of these individual factors towards increasing HEPA is also highlighted in the WHO’s “Global Action Plan on Physical Activity 2018–2030” [29], which underscores the importance of promoting competencies, physical literacy and health literacy, in order to increase individuals’ HEPA.
The Physical Activity-related Health Competence (PAHCO) model specifies competencies that are required in order to lead a physically active, healthy lifestyle and can be placed at the scientific intersection of physical literacy and health literacy [30, 31]. PAHCO consists of three integrated sub-competencies, which facilitate a physically active, healthy lifestyle (see Fig. 1) [32]. First, movement competence comprises direct motor-related requirements, which enable individuals to participate in planned exercise sessions (e.g. cycling and swimming) and to perform the activities of daily living (e.g. lifting heavy objects) [30]. Second, control competence ensures that PA achieves gains for health and is divided into a physical component consisting of the task to apply an adequate load and intensity (control competence for physical training – CCPT) and a psychological component for mental health benefits (PA-specific affect regulation; PAAR) [33]. Third, PA-specific self-regulation (PASR) includes psychological dispositions and motivational-volitional requirements to guarantee regular performance of physical activities [34].Fig. 1The Physical Activity-related Health Competence (PAHCO) model [32] In turn, these three sub-competencies arise from the specific coupling and integration of basic elements for PA behaviour [32]. Movement competence derives from motor abilities, motor skills as well as movement and body awareness (e.g., endurance, strength and balance). The sub-competence of control competence relies on individuals’ action knowledge and effect knowledge of PA to structure PA exercise subsequently in respect of the desired health-related outcome. PA-specific self-regulation is composed of self-efficacy, beneficial motive structures and positive attitudes toward PA.
In addition to the basic elements and sub-competencies that foster a physically active, healthy lifestyle, the authors of this model postulate that PAHCO holds the characteristics of a personality trait [30]. In line with modern personality trait theories [35] and competence research in educational sciences [36], PAHCO is assumed to display both changeability and relative temporal stability, which includes the potential for lasting promotion through structured interventions. Such interventions should address one or – following the idea of the integration of skills, knowledge and attitudes – ideally multiple basic elements to target the sub-competencies of the PAHCO model [30]. The possibility of promoting and maintaining PAHCO through interventions consequently holds the potential to facilitate long-term promotion of HEPA and HRQOL in OWs [37] and to mitigate the behavioural and work-related health risks of this occupational group.
Several empirical studies endorse the theoretical assumptions of this model with respect to their relationship to indicators of HEPA and HRQOL by displaying positive connections with PAHCO in various adult samples [31, 38, 39], with one study focusing on the target group of OWs [20]. On the sub-competence level, PA-specific self-regulation competence shows strong connections with leisure-time PA [20, 31, 39], whilst control competence for physical training demonstrates a moderate to strong connection with indicators of HRQOL in non-clinical adult samples [20, 38]. In line with the theoretical assumptions of the PAHCO model, these findings indicate the particular relevance of PA-specific self-regulation for regular participation in PA and point towards the value of control competence to facilitate effective HEPA and improvements in HRQOL.
However, the findings regarding the effect of PAHCO on HEPA and HRQOL remain tentative, as most of the research on this model has been cross-sectional [20, 31, 32, 34, 37, 38] with only few studies employing a longitudinal design in adult populations [39, 40]. Alongside this, two interventional studies [41, 42] yielded initial results concerning the changeability of this model in secondary school students showing improvements in PAHCO. Yet the assumption of changeability was unsupported in vocational students, who demonstrated no changes in PAHCO following a co-created PA intervention [43]. In addition to these mixed results with respect to changeability, Schmid et al. [ 39] investigated the temporal stability of PAHCO in a person-oriented approach over a period of 4 months and supported the authors’ assumptions of time-stable PAHCO sub-competencies [30]. In summary, Rosenstiel et al. [ 41] found no stable increase in PAHCO in secondary school students eight to 12 weeks after a physical education intervention but demonstrated a higher stability of PAHCO patterns in the students assigned to the intervention group in comparison with the control group students. This result might indicate higher temporal stability of PAHCO resulting amongst others from intervention components. However, the temporal stability of PAHCO could not be substantiated in an interventional study on PAHCO in secondary school students, which indicated initial changes in PAHCO after an intervention but no universally stable effects on PAHCO after 3 months [42]. While the findings of these studies are inconclusive in respect of PAHCO’s temporal stability over shorter periods, temporal stability over a longer time frame beyond 4 months has not yet been investigated. In addition to this research gap on temporal stability over longer periods, investigating PAHCO’s temporal stability over longer periods and the changeability of this construct resulting from HEPA interventions in OWs would empirically corroborate the conceptual assumptions of this model against the backdrop of modern personality trait theory and general competence research [35, 36]. The empirical examination of these assumptions is of particular relevance for the development of PAHCO interventions in OWs, as the changeability and temporal stability of PAHCO represents the cornerstone of this model for long-term improvements in HEPA and successful transfer for the promotion of HRQOL [33, 37, 39, 42].
The theoretical potential of PAHCO to target the need for promoting HEPA and HRQOL in OWs, in combination with the dearth of longitudinal research on this model, underscores the relevance to testing changeability and temporal stability of the sub-competencies as important conceptual assumptions of the PAHCO model. More specifically, the relevance with respect to temporal stability is particularly indicated for the period beyond 4 months, which was the maximum follow-up period investigated in previous studies. Owing to the importance of these theoretical assumptions for the development of interventions, the first research goal of this study concerns changeability and temporal stability of PAHCO and the sub-competencies of this model in OWs over the course of 18 months, following an in-person, three-week HEPA intervention. The second aim of the present study is to add empirical evidence regarding the postulated effect of the PAHCO subscales on HEPA and HRQOL over the period of 18 months. This second research goal will extends previous cross-sectional findings concerning the connections between PAHCO, HRQOL and indicators of HEPA over the course of the in-person intervention and the 18 month follow-up period.
## Study design
This study was part of a project to evaluate the occupational health management system of a large, global, private-sector company in Germany from December 2020 to June 2022. The company’s employees received information about the aims of the study and the scope of the evaluation project orally, by email or from brochures. The participants of the WHPP were asked to complete the survey on paper at the start and after completion of the intervention, as well as online at follow-up measurements six and 18 months after the intervention. Identification of the participants across the four measurement times was achieved by means of an individual pseudonym, which was presented before completing the questionnaires.
The participants of this study were asked to provide their informed consent at the start of the intervention and before completing the online version of the survey at the follow-up stages. This study complied with the company’s data privacy guidelines. The Ethics Committee of the School of Medicine at the Technical University of Munich gave its ethical approval for this study (IRB number: $\frac{645}{20}$ S-KH). The study was registered in the German Clinical Trials Register (DRKS00030514).
## Intervention description
The WHPP was developed by a German private-sector company to increase HEPA and HRQOL in employees and was held in-person at a wellness hotel in the district of Hof, Bavaria, Germany. The program of this WHPP was delivered over a period of 3 weeks by three exercise therapists, a physician and a psychologist, with a maximum of 60 participants per program. The intervention content of the WHPP is reported in accordance with the behaviour change technique taxonomy of Michie et al. [ 44].
At the start of the WHPP, the physician conducted a 60-minute initial physical examination and consultation to determine the health status and to advise on PA intensity as well as exercise session content in cooperation with the exercise therapist and the participant. Among other things, this medical screening served to inform participants about health consequences and to set personalised participative HEPA behavioural goals for the intervention period. The participants received a printed summary of the physical examination, which displayed e. g. blood pressure and triglycerides as well as the goals for HEPA during the intervention determined amongst others by physical working capacity test. On the next day, the exercise therapists introduced the participants to the indoor, outdoor and water aerobic exercise facilities in three 90-minute sessions and the participants took part in one 90-minute workshop focusing on the connection between PA and health, which was run by the physician. These sessions provided the participants with instructions and demonstrations of exercise behaviour and basic knowledge of HEPA.
Over the following 15 days, the participants followed a structured program, which comprised a 45-minute running or walking workout in the morning and a 90-minute exercise session after breakfast, both guided by the exercise therapists. The psychologist delivered a 90-minute relaxation technique session (e.g. autogenous training) or HEPA workshop after lunch. In the evening, the participants could voluntarily spend their leisure-time in the wellness clinic facilities or attend the psychologist’s presentations on the relationship between mental health and PA. The exercise therapists ran guided hiking trips for the participants near the wellness clinic twice a week in the afternoons over the course of the WHPP. There were no sessions on the three Sundays during the WHPP but the training facilities were accessible to all individuals. Participants focused on learning different types of HEPA (e.g. nordic walking, swimming, functional training) depending on their personal preferences and medical profile. Furthermore, they received information on the current state of knowledge with respect to biomechanics, behavioural psychology and exercise science. This phase of the WHPP was shaped by the participants’ behavioural practice with respect to HEPA, feedback on HEPA behaviour and instruction to self-monitor HEPA behaviour by the exercise therapists, as well as social support from other participants. This process was supported by the combination of theoretical and practical intervention components as well as by an interprofessional approach, which included HEPA and HRQOL from a psychological, medical and exercise science perspective [45].
In the last week, the physician conducted a second physical examination to screen changes in the participants’ medical profile (e.g. blood pressure and trigylcerides) and to set participative goals for maintaining HEPA after completion of the WHPP. The physicians and exercise therapists gave instructions for the participants’ goal setting and action planning as well as problem solving strategies. Furthermore, the participants refined HEPA behaviour implementation intentions in workshops with the exercise therapist and psychologist on the last 2 days of the WHPP. This phase of the WHPP focused on transferring HEPA behaviour change into the participants’ daily lives, so that HEPA behaviour could be maintained. The entire curriculum, material overview and WHPP schedule can be accessed by contacting the corresponding author.
## Sampling procedure and description
Before participants were recruited, the minimum sample size was calculated using a repeated measures within-between interaction analysis of variance design in GPower 3.1 [46]. According to this calculation, a sample size of $$n = 98$$ was needed to detect an effect of f2 = 0.20 with the statistical power of 1-β = 0.95 and a type one error of $$p \leq 0.05$$ under the assumption of repeated measures correlation of $r = 0.50$ at four measurement times. This calculation served as the lower threshold for data collection with attrition rates in longitudinal studies ranging from 30 to $70\%$ [47].
During the data collection period, 446 employees took part in the intervention. Three hundred eighty-seven employees registered their interest to participate in the study. 328 ($85\%$) participants met the first inclusion criterion of being engaged in office work occupation, and 45 ($12\%$) people were ineligible owing to assembly work tasks; a further 14 ($4\%$) were ineligible owing to construction work duties. All 328 participants met the second inclusion criterion, i.e. absence from acute mental or physical disease, and completed the survey at the start of the intervention. In total, 149 ($39\%$) participants completed the survey after the intervention as well as at the first follow-up measurement after 6 months and the second follow-up measurement after 18 months. A detailed overview of participant enrolment is shown in Fig. 2.Fig. 2Overview of participant enrolment and drop out over the course of the data collection period
## Measures
The participants gave information on their sociodemographic status by reporting their gender, age, relationship status and educational level. To assess the primary and secondary outcomes, this study utilised valid and reliable instruments for the population of German adults [32, 48, 49].
The primary outcome of this study was assessed with the PAHCO questionnaire developed by Sudeck and Pfeifer [31]; consisting of the three subscales control competence for physical training (CCPT), PA-specific affect regulation (PAAR) and PA-specific self-regulation (PASR), with a total of 13 items rated on a 4-point Likert scale. The first subscale, CCPT (e.g. “If my muscles are tensed up, I know exactly how to counter this through physical activity”), contains six items (Cronbach’s α = 0.86). The second subscale, PAAR (e.g. “I am able to regulate my mood through physical activity”), comprises four items (Cronbach’s α = 0.82). The third subscale, PASR (e.g. “I stick with my plan to do exercise and am not easily distracted from that plan”), comprises three items (Cronbach’s α = 0.84). Mean scores are calculated for the three subscales and for all 13 items of the PAHCO questionnaire, with values ranging from one to four and higher mean scores indicating better PAHCO (Cronbach’s α = 0.92).
The secondary outcome, HEPA, was operationalised with the Godin-Shepard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [50]. This questionnaire examines PA during leisure time at light, moderate and vigorous intensities (e. g. “Over the last 7 days (i.e., the last week), how many times on average did you do the following kinds of exercise for more than 30 min during your free time?”) resulting in the cumulative weighted leisure score index (LSI). In agreement with the procedure of calculating LSI in healthy adults proposed by Amireault et al. [ 51], LSI was examined by multiplying the bouts at moderate intensity by five and nine at vigorous intensity, and adding up the two products of this calculation. The values of the LSI start at zero, and values above 24 indicate sufficient leisure-time PA [51].
HRQOL was examined using Short-Form Health Survey (SF-12) version 2.0 [10, 49]. SF-12 version 2.0 has a total of twelve items, which examine HRQOL with a weighted and standardised component score in a physical (Cronbach’s α = 0.80) and a mental (Cronbach’s α = 0.86) dimension. For the total score on HRQOL, the mean of the two component scores was calculated. The scores on HRQOL range from zero to 100, with a mean (M) of 50 and a standard deviation (SD) of 10; high values indicate better HRQOL.
## Statistical analysis
The research questions of this study were tested in a per-protocol (PP) analysis, which included all participants that remained within the main study until completion of the follow-up [52]. PP analysis serves to investigate the potential efficacy of the intervention for participants who adhere to the study protocol [53], as the research questions addressed the theoretical assumptions of PAHCO and not the effectiveness of the intervention, which is typically analysed by an intention-to-treat approach. Data preparation as well as descriptive and inferential statistical analyses were performed with R and RStudio (Version 4.2.1; RStudio Inc., Boston, MA, USA) [54].
In the data preparation process the handling of low quality data, for example due to illogical LSI scores, was operationally defined by the first author, approved by the authors of this study and excluded from the analysis. If all authors were unsure about the exclusion of potentially unreasonable survey responses the data was retained. Missing data, for example owing to a partially incomplete survey response, were imputed by applying multivariate-chained equations, when the assumption of data missing at random was met [55]. Multivariate outliers were excluded before the analysis, using Mahalanobis distance, based on the recommendations by Tabachnick and Fidell [56]. The assumptions of linearity, normality, homoscedasticity and independence of the residuals were examined on the basis of current guidelines for linear regression analyses [57].
Four linear, mixed-model (LMM) regressions, one for the total PAHCO score and three for the PAHCO subscales, were run with the participants’ pseudonym as the random intercept, to test the first research goal of changeability and temporal stability of PAHCO [58]. For the second research goal, the secondary outcomes of leisure-time PA and HRQOL were tested separately over the four measurement times, with the three PAHCO subscales as fixed effects and the participants’ pseudonym as a random effect. The measures of HRQOL, leisure-time PA, age, gender, relationship status and educational level served as fixed effects in the LMM regressions.
Standardised estimates (β), confidence intervals (CI $95\%$) and p-values were examined to determine the influence of the fixed effects on the outcome. The final regression models are presented in comparison with the null model with Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC) and adjusted R-squared (R2), on the basis of one imputed data set. Tukey’s post hoc test was used to test the temporal stability of PAHCO and the models’ subscales at the first and second follow-up measurement compared with the level after HEPA intervention completion [59]. Standardised estimates of the LMM analyses and post hoc tests were interpreted as small (≈0.10), moderate (≈0.30) or strong effects (≈0.50) [60]. On the basis of multiple testing, we adjusted the significance level of the LMM analyses and post hoc testing to $p \leq 0.01$ [61].
## Basic descriptive analyses
The assumptions of linearity, normality, homoscedasticity and independence of the residuals were maintained before the regression analyses. The sociodemographic characteristics of the sample at baseline are shown in Table 1. From the 328 participants at baseline, 149 ($39\%$) participants were included in the PP analysis to answer by completing the survey after the intervention as well as at the first follow-up measurement and the second follow-up measurement. Table 1Sociodemographic characteristics of the participantsTotal ($$n = 328$$)Men ($$n = 216$$)Women ($$n = 112$$)Age, M (SD)50.4 (6.4)50.7 (6.5)49.7 (6.1)*Relationship status* Relationship, n (%)240 (73.2)170 (78.7)70 (60.5) No relationship, n (%)88 (26.8)46 (21.3)42 (37.5)Education Tertiary, n (%)200 (61.0)155 (71.8)45 (40.2) Secondary, n (%)114 (34.7)56 (25.9)58 (51.8) Primary, n (%)14 (4.3)5 (2.3)9 (8.0)M Mean, SD Standard deviation, N Total sample, n Subsample Descriptive statistics (M and SD) for the overall PAHCO score and the PAHCO subscales, the HEPA indicator and the HRQOL score across the four measurement times are shown in Table 2. The descriptive analysis points towards an improvement in the primary and secondary outcomes from the first to the second measurement point. Descriptively, the primary outcomes of PAHCO and the respective subscales remain relatively constant at the first and the second follow-up measurement in comparison with the level after completion of the intervention. While PAAR seems to be descriptively most stable temporarily after the intervention, PASR displays the biggest decrease in the descriptive analysis. In contrast, the secondary outcomes of leisure-time PA and HRQOL show a more substantial descriptive decline at the follow-up measurement time points compared with the level after completion of the intervention. In particular, leisure-time PA descriptively shows a substantial decrease after the intervention. Table 2 M and SD of the primary and secondary outcome parameters for the measurement pointsT0T1T2T3PAHCOM2.683.263.223.17SD0.430.390.430.46CCPTM2.623.273.273.22SD0.560.400.450.48PAARM2.803.143.153.17SD0.610.590.580.58PASRM2.623.343.243.11SD0.620.570.630.71Leisure-time PAM23.4984.2336.5232.90SD17.0534.4820.9919.57HRQOLM47.2454.8851.1450.72SD6.093.755.475.48M Mean, SD Standard deviation, T0 Start of the program, T1 End of the program, T2 6-month follow-up, T3 18-months follow-up, PAHCO Physical Activity-Related Health Competence, CCPT Control Competence for Physical Training, PAAR Physical Activity-Specific Affect Regulation, PASR Physical Activity-Specific Self-Regulation, HRQOL Health-Related Quality of Life
## Analysing the changeability and temporal stability of PAHCO
A detailed overview of the results of the analysis with respect to PAHCO’s changeability can be found in Table 3. Temporal stability of PAHCO is shown in Table 4. The description of the LMM regressions and the model fit parameters are presented in Supplementary Table 1.Table 3Results of the LMM regressions for PAHCO score and PAHCO subscales over timeCriterionPAHCOCCPTPredictorSEβCI $95\%$SEβCI $95\%$Gender Male0.070.04−0.09 / 0.180.07−0.00− 0.14 / 0.13Age0.030.02−0.03 / 0.080.030.03−0.04 / 0.09Educational Level Secondary0.170.29−0.03 / 0.620.200.20−0.19 / 0.60 Tertiary0.170.29−0.04 / 0.610.200.30−0.10 / 0.70Relationship Status Current Relationship0.06−0.05−0.17 / 0.060.07−0.08− 0.20 / 0.05Leisure-Time PA0.020.250.15 / 0.360.030.05−0.01 / 0.10Time T10.050.440.33 / 0.540.060.560.44 / 0.68 T20.040.510.44 / 0.570.040.630.54 / 0.71 T30.040.460.39 / 0.530.040.590.50 / 0.67PAARPASRPredictorSEβCI $95\%$SEβCI $95\%$Gender Male0.110.10−0.12 / 0.320.100.06−0.14 / 0.25Age0.04−0.01− 0.08 / 0.080.040.02−0.07 / 0.10Educational Level Secondary0.280.53−0.03 / 0.910.250.23−0.25 / 0.72 Tertiary0.280.49−0.06 /0.870.250.16−0.32 / 0.65Relationship Status Current Relationship0.09−0.02−0.20 / 0.170.09−0.05− 0.23 / 0.13Leisure-Time PA0.060.06− 0.07 / 0.200.040.160.09 / 0.23Measurement Time T10.070.330.19 / 0.470.080.400.24 / 0.57 T20.050.360.27 / 0.450.060.530.42 / 0.63 T30.050.380.29 / 0.470.060.440.33 / 0.54PAHCO Physical Activity-Related Health Competence, CCPT Control Competence for Physical Training, PAAR Physical Activity-Specific Affect Regulation, PASR Physical Activity-Specific Self-Regulation, T0 Start of the intervention, T1 End of the intervention, T2 6-month follow-up, T3 18-month follow-up, Reference group for *Gender is* ‘Female’, Reference group for Educational *Level is* ‘Primary’, Reference group for Relationship *Status is* ‘No relationship’, Reference group for Measurement *Time is* ‘T0’, Results with a p-value <.01 are shown in bold, SE Standard error, β Standardised regression estimate, CI $95\%$ $95\%$ confidence intervalTable 4Temporal stability of PAHCO and the subscales after completion of the interventionCriterionPAHCOCCPTDifferenceSEβCI $95\%$SEβCI $95\%$T2 – T10.05−0.07− 0.16 / 0.020.06− 0.07− 0.18 / 0.05T3 – T10.05−0.03− 0.12 / 0.070.06− 0.03− 0.14 / 0.09T3 – T20.030.04−0.03 / 0.110.040.04−0.04 / -0.12PAARPASRPredictorSEβCI $95\%$SEβCI $95\%$T2 – T10.06−0.03− 0.15 / 0.090.07− 0.13− 0.26 / 0.03T3 – T10.07−0.05− 0.18 / 0.080.08−0.04− 0.17 / 0.13T3 – T20.05−0.02− 0.11 / 0.070.050.09−0.01 / 0.20PAHCO Physical Activity-Related Health Competence, CCPT Control Competence for Physical Training, PAAR Physical Activity-Specific Affect Regulation, PASR Physical Activity-Specific Self-Regulation, T1 End of the intervention, T2 6-month follow-up, T3 18-month follow-up, Results with a p-value <.01 are shown in bold, SE Standard error, β Standardised regression estimate, CI $95\%$ $95\%$ confidence interval The LMM regression to test the first research goal, which investigated the changeability and temporal stability of PAHCO and the subscales of this model, showed a substantial increase for the total PAHCO score after completion of the HEPA intervention (β = 0.44, CI $95\%$ [0.33, 0.54], $p \leq 0.001$) as well as after the first (β = 0.51, CI $95\%$ [0.44, 0.57], $p \leq 0.001$) and second follow-up measurement (β = 0.46, CI $95\%$ [0.39, 0.53], $p \leq 0.001$). In the post hoc analyses, PAHCO revealed neither a drop-off at the first follow-up measurement ($$p \leq .18$$) nor a decrease at the second follow-up measurement ($$p \leq .61$$) in comparison with the level after completion of the HEPA intervention.
On the subscale level, control competence for physical training (CCPT) displayed substantial gains in comparison with the baseline level after the HEPA intervention (β = 0.56, CI $95\%$ [0.44, 0.68], $p \leq 0.001$) at the first follow-up (β = 0.63, CI $95\%$ [0.54, 0.71], $p \leq 0.001$) and second follow-up measurement (β = 0.59, CI $95\%$ [0.50, 0.67], $p \leq 0.001$). CCPT showed no decrease in the post hoc analyses after completion of the HEPA intervention at the first follow-up measurement ($$p \leq 0.26$$) and second follow-up measurement ($$p \leq 0.70$$).
The LMM regression of the subscale of PA-specific affect regulation (PAAR) showed a moderate, positive increase after completion of the HEPA intervention (β = 0.33, CI $95\%$ [0.19, 0.47], $p \leq 0.001$), at the first follow-up measurement (β = 0.36, CI $95\%$ [0.27, 0.45], $p \leq 0.001$) and the second follow-up measurement (β = 0.38, CI $95\%$ [0.29, 0.47], $p \leq 0.001$) compared with the baseline. The post hoc analyses revealed no decrease in PAAR at the first ($$p \leq 0.64$$) and second follow-up measurement ($$p \leq 0.46$$) compared with the PAAR level after completion of the HEPA intervention.
The subscale of PA-specific self-regulation (PASR) displayed a positive, moderate increase after the HEPA intervention (β = 0.40, CI $95\%$ [0.24, 0.57], $p \leq 0.001$). This subscale showed a strong gain at the first follow-up measurement (β = 0.53, CI $95\%$ [0.42, 0.63], $p \leq 0.001$) and a moderate gain at the second follow-up measurement (β = 0.44, CI $95\%$ [0.33, 0.54], $p \leq 0.001$) in comparison with the baseline scores in the LMM. The post hoc analyses showed no decrease in this subscale at the first follow-up ($$p \leq 0.09$$) and second follow-up measurement ($$p \leq .65$$) in comparison with the level after completion of the HEPA intervention.
## Analysis of the longitudinal effect of the PAHCO subscales on leisure-time PA and HRQOL
The analysis of the second research goal, which investigated an effect of the PAHCO subscales on leisure-time PA and HRQOL, is summarised in Table 5. PASR displayed a small to moderate, positive influence of leisure-time PA (β = 0.18, CI $95\%$ [0.11, 0.25], $p \leq 0.001$). The subscales of CCPT ($$p \leq 0.85$$) and PAAR ($$p \leq 0.07$$) displayed no noteworthy effect on leisure-time PA. In addition, leisure-time PA did not display a significant effect 18 months after completing the intervention ($$p \leq 0.16$$).Table 5Results of the LMM regressions for leisure-time PA and HRQOL over timeCriterionLeisure-time PAHRQOLPredictorSEβCI $95\%$SEβCI $95\%$Gender Male0.090.00−0.07 / 0.070.12−0.02− 0.13 / 0.10Age0.04−0.03− 0.11 / 0.030.05−0.11− 0.23 / 0.00Educational Level Secondary0.250.39−0.12 / 0.280.30−0.52−0.82 / -0.01 Tertiary0.260.14−0.07 / 0.340.29−0.43− 0.74 / 0.00Relationship Status Current Relationship0.090.00−0.07 / 0.070.120.220.12 / 0.33Leisure-Time PA0.040.04−0.06 / 0.15HRQOL0.040.03−0.04 / 0.10CCPT0.04−0.01− 0.09 / 0.070.040.220.12 / 0.33PAAR0.03−0.02−0.09 / 0.050.040.06−0.04 / 0.16PASR0.040.180.11 / 0.250.040.260.16 / 0.36Time T10.090.720.65 / 0.800.120.400.28 / 0.52 T20.080.170.03 / 0.170.090.110.02 / 0.20 T30.080.060.00 / 0.130.080.110.02 / 0.20HRQOL Health-Related Quality of Life, CCPT Control Competence for Physical Training, PAAR Physical Activity-Specific Affect Regulation, PASR Physical Activity-Specific Self-Regulation, T0 Start of the intervention, T1 End of the intervention, T2 6-month follow-up, T3 18-month follow-up, Reference group for *Gender is* ‘Female’, Reference group for Educational *Level is* ‘Primary’, Reference group for Relationship *Status is* ‘No relationship’, Reference group for the Measurement *Time is* ‘T0’, Results with a p-value <.01 are shown in bold, SE Standard error, β Standardised regression estimate, CI $95\%$ $95\%$ confidence interval With respect to the effect of PAHCO on HRQOL, the subscales of CCPT (β = 0.22, CI $95\%$ [0.12, 0.33], $p \leq 0.001$) and PASR (β = 0.26, CI $95\%$ [0.16, 0.36], $p \leq 0.001$) had a small to moderate, positive effect on the HRQOL. PAAR showed no notable connection with HRQOL ($$p \leq 0.23$$). HRQOL showed no significant effect following the intervention after six ($$p \leq 0.02$$) and 18 months ($$p \leq 0.01$$).). HRQOL showed no significant effect following the intervention after six ($$p \leq 0.02$$) and 18 months ($$p \leq 0.01$$).
## Explaining the changeability and temporal stability of PAHCO
The results of the analysis confirm the first research goal, which assumed changeability and temporal stability of PAHCO, by displaying a moderate to strong increase over the course of the WHPP as well as no decrease over the follow-up period of a total of 18 months.
Our findings are in line with results on the changeability of PAHCO in a sample of secondary school students, which demonstrated a moderate effect for the increase of PAHCO following a physical education intervention [42]. Additionally, our results also align with reviews on health literacy [62] and physical literacy [63] interventions, which substantiate the changeability of these constructs in working aged adults. Yet Grüne et al. [ 43] found no effects on PAHCO following a PA intervention in vocational students. The authors of this study in the school setting, however, discussed their results against the backdrop of implementation obstacles and potential theoretical inconsistencies in intervention development. In contrast with these limitations, our study might have improved PAHCO as a result of the high amount of guided HEPA behaviour and the face-to-face design of the intervention, highlighted as beneficial intervention components by a review on the promotion of PA motivation [64], which is integrated in the basic elements of PAHCO.
The findings of our study on temporal stability of PAHCO agree with theoretical research on PAHCO [30] by displaying no changes of PAHCO level 18 months after the intervention. These results are partially in line with results from Rosenstiel et al. [ 41], who underlined a higher temporal stability of PAHCO after intervention in physical education classes with secondary school students in comparison with the control group. However, this study did not indicate universally stable improvement following a physical education intervention in PAHCO after 8–12 weeks, which partially contradicts our results. While this study by Rosenstiel [41] also supposed temporal stability in PAHCO after the physical education intervention, the authors connected the lacking sustainability of the intervention effects with a small total number of six intervention sessions lasting 90 minutes each. The total number of intervention sessions in our study differed largely from this intervention design, which might explain different findings with respect to the stability of intervention effects in PAHCO at the follow-up measurements. This claim is supported by a review on PA [65], which underlines the importance of aspects connected with the duration of PA interventions, such as total contact time with the exercise therapists. In addition, the differing results on PAHCO’s temporal stability in the study on secondary school students by Rosenstiel [41], in comparison with our findings in OWs, could be due to lower temporal stability of PAHCO in younger populations. This idea is underlined by a review on trait stability, which postulates a consolidation of personality traits in young adulthood [66].
With respect to the longer follow-up period in comparison with previous PAHCO interventions, our findings are supported by a meta-analysis of interventions addressing PA motivation, which showed stable intervention effects at follow-up time points beyond 6 months [64]. In addition, current reviews suggest an overall positive effect of interventions on PA self-efficacy [67], health literacy [62] and physical literacy [63], which share much common ground with the PAHCO model. Yet the temporal stability of these interventions is small to negligible for PA self-efficacy [67] and uncertain for physical and health literacy, as most studies – including on these constructs – lack follow-up periods beyond 6 months or examine inconsistent results at these later measurement points [68, 69].
In summary, the findings of our study on PAHCO’s changeability and temporal stability substantiate the theoretical assumptions of this model, largely in line with the existing literature and extend the current knowledge on PAHCO. These results might be of particular importance for future WHPPs, as interventions on PAHCO could lead to temporarily stable changes in PAHCO, which consequentially could promote HEPA and HRQOL in OWs. The potential of temporarily stable changes in PAHCO might present a specific benefit for OWs, as this target group is prone to insufficient levels of PA at low and moderate intensity and to longer periods of sedentary, work-related activities [18]. In addition, these changes in PAHCO could also promote leisure-time PA, which would allow OWs to compensate for and change these work-related activity patterns. However, as this is the first study in non-clinical adults to examine the changes of PAHCO following an intervention, future research is needed to corroborate our findings.
## Explaining the longitudinal effect of the PAHCO subscales on leisure-time PA and HRQOL
The second research goal, which postulated a positive effect of the PAHCO subscales on leisure-time PA and HRQOL in OWs can be confirmed for the PASR subscale, which displayed a positive, small to moderate effect on leisure-time PA and HRQOL. Alongside this, the subscale of CCPT showed a positive small to moderate effect on HRQOL, which also supports the second research goal. Yet the subscales of CCPT and PAAR did not indicate an effect on leisure-time PA and PAAR showed no noteworthy longitudinal effect on HRQOL.
*In* general, although the measures on PA and HRQOL were deviating from our study, these findings are in agreement with cross-sectional studies on PAHCO, PA and HRQOL in adult populations [31, 39] as well as the target group of OWs [20]. These studies underline the importance of the subscales of CCPT and PASR in connection with leisure-time PA and HRQOL. To the best of our knowledge, only one study has previously investigated longitudinal relationships of PAHCO with PA and HRQOL [39] in a non-clinical adult population. Although our results are generally in line with its findings, this study had a person-oriented approach and investigated PAHCO over a period of 4 months, which impedes the integration of our findings into the current literature.
Alongside this person-oriented study, our findings are partly in agreement with the results of a longitudinal study on PAHCO in chronic obstructive pulmonary patients, which found a positive, bivariate relationship between the subscales of PAAR and PASR after a rehabilitation program and the PA level 6 months after the program [40]. In addition, this study also found a positive, bivariate relationship between the subscales of PASR, PAAR and CCPT after the rehabilitation program and the patients’ quality of life after 6 months. However, structural equation model analysis of PAHCO in this sample of chronic obstructive pulmonary patients revealed a connection between patients’ movement competence after the program and the outcomes of PA and quality of life after 6 months. The subscales of CCPT, PAAR and PASR showed no connection with PA and quality of life after 6 months in the structural equation model [40]. Although movement competence was not examined in our study, the differing results compared with the research by Carl et al. [ 40] might also derive from varying populations and study designs. Movement competence might, for example, display largely differing connections with PA in patients with chronic obstructive pulmonary diseases compared with OWs, because of the burden of disease-limiting possibilities for PA participation [70].
In addition, our findings on PAAR and HRQOL differ from the results found by Sudeck et al. [ 34], which focused on PAAR and proved a moderating effect of PAAR on the relationship of PA and mental aspects of HRQOL over a period of 4 days. PAAR, which covers individuals’ ability to achieve mental health benefits from PA, showed no effect on leisure-time PA and HRQOL in our study. These contradictions could be due to various differences between our study and the research by Sudeck et al. [ 34]. While the later research comprised a period of 4 days with a high proportion of physically active adults and employed different measures of PA and HRQOL, our analysis focused on a period of more than 18 months and a sample of mostly inactive OWs and did not include PAAR as a moderator for the PA-HRQOL connection. Future studies, however, might focus on the relationships of this subscale with PA and HRQOL in a longitudinal moderation analysis in OWs.
In addition to these studies, which address PAHCO directly, the results on the subscale of PASR are in line with reviews on PA-self efficacy [71] and PA motivation [64] in healthy adults, which demonstrate that interventions targeting these concepts are associated with higher levels of PA. In addition, the review by Medrano-Ureña et al. [ 72] found that PA self-efficacy interventions can also produce higher levels of HRQOL. The subscale of PASR shows substantial overlap with PA-self efficacy and PA motivation and concerns the theoretical assumption of increasing regular HEPA participation, which might in return promote HRQOL [30]. The effect of CCPT on HRQOL is also indicated by the review from Nguyen et al. [ 11], which focuses on exercise interventions, also addressing PA-related knowledge in OWs to promote HRQOL in OWs. Within this review, a large proportion of the interventions addressed participants’ knowledge on physical load or execution of HEPA before or during the intervention programs. These PA-related knowledge aspects are a central component of CCPT, which might explain the positive effect of this subscale on HRQOL in our study, also investigating OWs.
With regard to the connections of the closely related constructs of health and physical literacy with leisure-time PA and HRQOL, a review on health literacy and PA found positive connections between these concepts but no intervention effects of health literacy on PA [73]. Furthermore, health literacy interventions displayed a weak positive effect on health outcomes, such as HRQOL, but were also limited by insufficient study designs and heterogeneous health literacy interventions [62]. A current review by Carl et al. [ 63] supports the effectiveness of physical literacy on PA but found no studies focusing on the effect of physical literacy interventions on health. The shortcomings of health literacy interventions to promote PA as well as the lack of current physical literacy studies addressing health outcomes along with PA might substantiate the value of PAHCO, which theoretically combines benefits of both constructs.
## Strengths and limitations
This is the first study to explore PAHCO, leisure-time PA and HRQOL in OWs in a longitudinal design. The focus on OWs might be particularly important, owing to the high risk of physical inactivity in this population, which is largely due to the work-related demands of this occupation [17]. In addition, this study tries to overcome the shortcoming of a large proportion of longitudinal studies in PA promotion [74] and previous longitudinal studies on PAHCO [39] by employing a follow-up period beyond 6 months to examine PAHCO temporal stability, leisure-time PA maintenance and long-term HRQOL promotion 18 months after completion of the WHPP. Moreover, our study extends the theoretical and empirical knowledge of PAHCO by exploring the changeability and temporal stability of PAHCO in a non-clinical adult population, as well as analysing the effect of this model on leisure-time PA and HRQOL. These extensions to previous studies into the characteristics and effect of PAHCO are crucial to illustrate the potential of interventions that specifically target PAHCO in these populations [37].
Despite these strengths, this study has some limitations. The sub-competence of movement competence was not assessed within the version of the PAHCO questionnaire used in our study, as movement competence was not part of this measure at the time of data collection [32]. The absence of movement competence from the PAHCO questionnaire is derived from the complexity of valid operationalisation methods across various populations [75]. This problem was resolved by Carl et al. [ 32] in a study incorporating movement competence and validating the refined PAHCO questionnaire across various adult samples. Movement competence, which covers locomotor abilities and skills for PA in exercise and daily living situations, might explain additional variance of HEPA and HRQOL in our study with OWs, as indicated in longitudinal findings by Carl et al. [ 40] on PAHCO, PA and quality of life in chronic obstructive pulmonary disease patients.
In addition, the LSI might not be a sufficient tool to comprehensively examine HEPA as light and very light, non-leisure PA and active commuting are not included in the LSI scoring. These PA forms display a positive connection with health in current meta-analyses [76, 77] and therefore might have been included to precisely explore the HEPA-health relationship. In this study, these forms of PA were not implemented as the main focus was on the promotion of PAHCO and its effect on facets of leisure-time PA, which served as a proxy parameter for HEPA, and health. With previous studies, also in the target group of OW, displaying no relationship between occupational PA and health [20, 21] and another study showing no direct connection between overall PA and health [38], this study only employed the GSLTPAQ to examine a substantial facet of HEPA.
Furthermore, future research on PAHCO, PA, and HRQOL in OWs might benefit from objective measures of PA and health. In this regard, the present study might have been affected by subjective bias in the primary outcomes [78, 79]. For example, the use of accelerometers in the examination of PA would allow a more precise measure of this health behaviour, whilst also opening up the possibility of incorporating the assessment of sedentariness [80]. The advantage, which could result from objective measures of PA, might be crucial, because these health behaviours could also play a vital role in the relationship of PAHCO and health in OWs [81].
Finally, our study employed no control group or randomisation for the HEPA intervention. Therefore, the findings of this study do not offer causal conclusions on the effect of the intervention on PAHCO, leisure-time PA and HRQOL. Even though research in occupational settings is affected by a multitude of organisational and logistical problems, at times impeding the implementation of a controlled design [82], randomised control trials are needed to quantify the effect of HEPA interventions in the workplace on PAHCO without the potential of observation bias [83].
## Conclusion
This study extends current research into PAHCO by investigating the changeability and temporal stability of PAHCO as well as examining the effects of PAHCO on leisure-time PA and HRQOL in a sample of German OWs. While this population might be particularly prone to impaired HRQOL, owing to work-related physical inactivity, our findings underline the changeability and temporal stability of PAHCO, which implies important characteristics for intervention development to address the health-related demands and resources of this occupational group. The potential of PAHCO for the development of interventions in OWs is additionally substantiated, as our results suggest that motivational and volitional aspects of PAHCO can promote leisure-time PA and HRQOL, while aspects focusing on knowledge regarding the adaption of physical load during PA improve HRQOL. Based on these insights, comprehensive WHPPs could incorporate PAHCO by implementing components of HEPA-related knowledge in existing programs to facilitate health promotion and additionally refine the communication strategies to contain timely motivational and volitional cues, which could facilitate behaviour change and maintenance in OWs. In summary, these findings and the potential implications should, on the one hand, encourage practitioners to incorporate PAHCO within WHPPs to increase HEPA in OWs. On the other hand, future, randomised control trials on PAHCO in OWs should expand the field of study by including objective measures on PA, which would also account for sedentariness and test our results in respect of the potential of PAHCO in WHPPs.
## Supplementary Information
Additional file 1: Supplementary Table 1. Model-fit parameters for null model and the final regression models. Supplementary Fig. 1. Physical activity-related health competence over time. Supplementary Fig. 2. Control competence for physical training over time. Supplementary Fig. 3. Physical acitivity-specific affect regulation over time. Supplementary Fig. 4. Physical acitivity-specific self-regulation over time.
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|
---
title: 'Implications of serum uric acid for female infertility: results from the national
health and nutrition examination survey, 2013–2020'
authors:
- Jiemei Liang
- Xiting Chen
- Jinfa Huang
- Weizhe Nie
- Qian Yang
- Qitao Huang
- Kaixian Deng
journal: BMC Women's Health
year: 2023
pmcid: PMC10007853
doi: 10.1186/s12905-023-02234-1
license: CC BY 4.0
---
# Implications of serum uric acid for female infertility: results from the national health and nutrition examination survey, 2013–2020
## Abstract
### Background
There is limited concrete evidence connecting serum uric acid levels to female infertility. Therefore, this study aimed to find out if serum uric acid levels are independently related to female infertility.
### Methods
From the National Health and Nutrition Examination Survey (NHANES) 2013–2020, a total sample of 5872 chosen female participants between the ages of 18 and 49 were identified for this cross-sectional study. The serum uric acid levels (mg/dL) of each participant were tested, and the reproductive health questionnaire was used to evaluate each subject's reproductive status. Both in the analyses of the full sample and each subgroup, logistic regression models were used to evaluate the relationship between the two variables. A stratified multivariate logistic regression model was used to perform the subgroup analysis based on serum uric acid levels.
### Results
Infertility was found in 649 ($11.1\%$) of the 5,872 female adults in this study, with greater mean serum uric acid levels (4.7 mg/dL vs. 4.5 mg/dL). Serum uric acid levels were associated with infertility in both the initial and adjusted models. According to multivariate logistic regression, the odds of female infertility were found to be significantly higher with rising serum uric acid levels (Q4 [≥ 5.2 mg/dL] vs. Q1 [≤ 3.6 mg/dL]), adjusted odds ratio [aOR] = 1.59, $$p \leq 0.002$$]. The data suggests that there is a dose–response relationship between the two.
### Conclusions
The results from this nationally representative sample from the United States confirmed the idea that there is a link between increased serum uric acid levels and female infertility. Future research is necessary to evaluate the relationship between serum uric acid levels and female infertility and explicate the underlying mechanisms of this relationship.
## Background
Infertility is defined as the inability to conceive after unprotected sexual activity or therapeutic donor insemination in women under the age of 35 or within six months in women over the age of 35. Infertility is estimated to affect $15\%$ of all couples worldwide [1, 2]. The World Health Organization has classified infertility as a social disease, and the Centres for Disease Control and Prevention (CDC) in the United States have named infertility a public health priority [3]. Infertility is more than just a quality-of-life concern and has significant public health repercussions, such as psychological discomfort, social stigmatisation, economic pressure, and marital discord [4, 5].
Serum uric acid (SUA) is a major by-product of purine metabolism catalysed by xanthine oxidoreductase (XOR). XOR is a source of reactive oxygen species, which can lead to oxidative stress and endothelial dysfunction. When SUA becomes an oxidant, it contributes to the development of various pathological processes in the body that are ruled by oxidative stress [6]. SUA might behave as an antioxidant, which might have some protective effects, or as a pro-oxidant, which might accelerate a chain reaction of free radicals and cause oxidative damage to cells [7]. In addition to inducing oxidative stress, SUA has a role in the metabolism of lipids, glucose, and inflammation [8, 9]. Hyperuricemia (HUA) is a chronic metabolic condition, defined by unusually elevated SUA levels, which has been identified to have effects on multiple body organs through its numerous effects and contribute to the emergence of several disease states [10]. In the female reproductive system, hyperuricemia with SUA deposition may cause female sexual dysfunction [11]. According to research on buffalo ovaries, disruption of the plasma-follicular barrier structure is linked to higher levels of SUA [12]. Oocyte meiosis can be inhibited by hypoxanthine, which is a precursor to SUA [13]. SUA has potential mechanisms such as oxidative stress, promotion of inflammation, endothelial damage and thrombosis, and therefore, high levels of SUA may be correlated with incread clinical severity of polycystic ovarian syndrome (PCOS), endometriosis, or adverse pregnancy outcomes [14–17].
Our hominoid ancestors had gene alterations that led to the lack of uricase. As a consequence, humans must adapt to SUA levels that are comparatively greater [18]. In addition to genes, the risk of hyperuricemia is associated with ethnicity, age, lifestyle, and dietary factors [19]. Unhealthy living and eating habits in modern society lead to an increased incidence of hyperuricemia. High SUA levels are involved in the development of several diseases, including obesity, diabetes, metabolic syndrome, kidney disease, cardiovascular disease, and female reproductive disorders [10, 17, 20–22]. At the same time, unhealthy lifestyle and dietary factors will increase the prevalence of female infertility [23, 24]. Hence, we hypothesise that increased SUA levels may lead to decreased female fertility. Ultimately, this leads to an increased incidence of female infertility.
There are no studies that we are aware of that used a nationally representative sample to study the link between SUA levels and female infertility. In this cross-sectional study, we identified and examined correlations between SUA levels and female infertility using the latest nationally representative data from the National Health and Nutrition Examination Survey (NHANES) 2013–2020.
## Data sources and study population
The National Centre for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC) collects data on nutritional status and health information for the NHANES which is a national population-based survey. All data for this study were provided in NHANES cycles 2013–2020. We used this data to describe the demographic characteristics of our population, obtain female self-reported infertility rates, and assess SUA levels in women 18 to 49 years old. This study was based on NHANES public data, and all information was collected from the official website [25]. The NHANES protocols are approved by the NCHS Research Ethics Review Board, and every respondent provided their signed informed consent [26].
The study enrolled women aged 18–49 years old who completed an interview using the reproductive health questionnaire and had a physical examination at the mobile examination centre (MEC). A multistage, stratified probability strategy was used to choose survey respondents [27]. Demographic and health history information was obtained through an extensive household interview. Physical assessments included the collection of blood samples at the MEC. Samples of serum were examined by the CDC Division of Laboratory Sciences. Analyses of the samples were performed in the United States.
## Fertility assessment
Responses from the reproductive health questionnaire were used to calculate the dependent variable of infertility (variable name in the questionnaire: RHQ074). Those who answered affirmatively to the survey question, "Have you ever attempted to become pregnant for at least a year without becoming pregnant?" were presumed to have infertility [28].
## Measurement and classification of SUA
The main independent variable was SUA measured in mg/dL. SUA levels were collected during subject enrolment in the NHANES using a colorimetric method in which uricase oxidises UA to allantoin and hydrogen peroxide (the Beckman Coulter UniCel DxC 800 Synchron chemistry analyzer between 2013 to 2014, the Beckman Coulter UniCel DxC 800 Synchron and the Beckman Coulter UniCel DxC 660i Synchron since 2015). Quality-control procedures' specifics have been disclosed elsewhere [29]. Values are reported in mg/dL and can be converted to μmol/L by multiplying by 59.48.
## Covariates
In the NHANES database, factors were classified as demographics or possible confounders that could influence SUA or fertility status [30, 31]. We considered demographic characteristics (age, sex, race or ethnicity, education, marital status, and ratio of family income to poverty); lifestyles (drinking and smoking); health insurance coverage; physical examinations; and laboratory tests (serum lipids, creatinine (Cr), blood urea nitrogen(BUN), and estimated glomerular filtration rate (eGFR)). In addition, we considered body mass index (BMI) and waist circumference (WC). Disease history was also taken into account and included the following diagnoses: hypertension [32] (characterised as being on anti-hypertensive medication and having a systolic blood pressure ≤ 140 mmHg or a diastolic blood pressure ≤ 90 mmHg); diabetes mellitus [32] (obtained through self-report and using diabetes medications); and metabolic syndrome (MetS). BMI was coded into three categories [33]: (underweight or normal weight (< 25 kg/m2), overweight (25–29.9 kg/m2), and obese (> 30 kg/m2). MetS was diagnosed in respondents when at least three of the following five symptoms were present [34]: WC ≥ 88 cm, triglycerides (TG) ≥ 150 mg/dL, high-density lipoprotein (HDL) < 50 mg/dL, systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg (averaged over three readings), or fasting plasma glucose (FPG) ≥ 100 mg/dL. CKD-EPI Creatinine Eq. [ 2021]: [35].\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$eGFR = {142} \times {\text{min}}\left({{\text{standardised}}\frac{{{\text{S}}_{{{\text{cr}}}} }}{{\text{k}}},{ }1} \right)^{ \propto } \times \max \left({{\text{standardised}}\frac{{{\text{S}}_{{{\text{cr}}}} }}{{\text{k}}},{ }1} \right)^{ - 1.200} \times 0.9938 ^{Age} \times 1,012\left({\text{if female}} \right)$$\end{document}eGFR=142×minstandardisedScrk,1∝×maxstandardisedScrk,1-1.200×0.9938Age×1,012if female
## Statistical analysis
Means and standard errors (SE) were used for continuous variables, as well as percentages and standard errors for categorical variables. The t-test (normal distribution) and Kruskal–Wallis test (skewed distribution) were used to assess continuous variables. The stratified multivariate logistic regression model was used to perform the subgroup analysis by SUA levels. The relationship between SUA levels and infertility was investigated by using SUA data as a continuous variable and in quartiles. The odds ratios (ORs) and their $95\%$ confidence intervals (CIs) were estimated. The following stratified multivariate logistic regression models were used to assess the effect of SUA levels on female infertility: Model 1: no adjustment; Model 2: adjusted for social demographic covariables (age, race/ethnicity, education, PIR) and health insurance coverage; Model 3: adjusted for the variables in Model 2 plus BUN, Cr, and eGFR; Model 4: adjusted for the variables in Model 3 plus BMI, DM, hypertension, and MetS.
All statistical analyses were carried out using the software tools R, version 4.1.1 (http://www.R-project.org, The R Foundation), and Free Statistics, version 1.5. In all tests, a statistically significant difference was defined as $P \leq 0.05$ (two-sided).
## Results
Four cycles of NHANES (2013–2014, 2015–2016, 2017–2018, and 2017–2020) were used in this study. There were 44,960 eligible participants, and of these, 22,673 adult females completed the interview and 15,689 participants completed the reproductive health questionnaire. Participants with missing data in SUA or answering the fertility information for RHQ074 variables ($$n = 9$$,817) were excluded. Our analyses included the remaining 5,872 participants aged 18–49. Figure 1 shows the flowchart of the exclusion criteria. Fig. 1Flow chart of sample selection Table 1 presents the descriptive characteristics of the study population according to their fertility status. Infertility was projected to affect $11.1\%$ of women between the ages of 18 and 49. Infertile women were older (36.2 years vs. 32.9 years), their SUA mean was more significant (4.7 mg/dL vs. 4.5 mg/dL), they had higher PIR levels (2.6 vs. 2.3), and they had lower eGFR levels (108.5 mL/min/1.73 m2 vs. 111.0 mL/min/1.73 m2) than women with non-infertility. Female participants with infertility were more likely to have a regular partner ($14.8\%$ vs. $8.1\%$), have been pregnant at least once ($13.3\%$ vs. $7.4\%$), have obesity ($14.3\%$ vs. $8.6\%$), have hyperuricemia ($13.8\%$ vs. $10.7\%$), diabetes mellitus ($8.8\%$ vs. $4.6\%$), MetS ($12.3\%$ vs. $9.2\%$), and have hypertension ($21.5\%$ vs. $15.5\%$). There were no statistical differences in ethnicity, education, health insurance coverage, BUN, or Cr. Table 1Baseline characteristics of participantsCovariatesTotal ($$n = 5$$,872)Infertile ($$n = 649$$)Fertile ($$n = 5$$,223)P-valueAge, years33.3 ± 9.436.2 ± 8.032.9 ± 9.5 < 0.001Age < 353,127 (53.3)259 (39.9)2,868 (54.9)Age ≥ 352,745 (46.7)390 (60.1)2,355 (45.1)Race/ethnicity0.058Mexican American972 (16.6)88 (13.6)884 (16.9)Other Hispanic602 (10.3)62 (9.6)540 (10.3)Non-Hispanic white1,922 (32.7)246 (37.9)1,676 (32.1)Non-Hispanic black1,350 (23.0)143 [22]1,207 (23.1)Non-Hispanic asian718 (12.2)77 (11.9)641 (12.3)Other race308 (5.2)33 (5.1)275 (5.3)Education0.695Less than high school1,875 (35.1)215 (33.8)1,660 (35.3)High school1,989 (37.3)239 (37.5)1,750 (37.2)More than high school1,474 (27.6)183 (28.7)1,291 (27.5)*Marital status* < 0.001Live alone1,465 (41.3)118 (27.6)1,347 (43.2)Married or cohabiting2,082 (58.7)309 (72.4)1,773 (56.8)PIR2.3 ± 1.62.6 ± 1.62.3 ± 1.6 < 0.001Health insurance coverage0.989Yes4,639 (79.1)512 [79]4,127 (79.1)No1,225 (20.9)136 [21]1,089 (20.9)Drinking0.003Yes1,700 (62.4)224 [70]1,476 (61.3)No1,026 (37.6)96 [30]930 (38.7)Smoking < 0.001Yes1,672 (28.5)238 (36.7)1,434 (27.5)No4,198 (71.5)411 (63.3)3,787 (72.5)SUA, mg/dL4.5 ± 1.14.7 ± 1.14.5 ± 1.1 < 0.001Hyperuricemia0.026Yes600 (10.2)83 (12.8)517 (9.9)No5,272 (89.8)566 (87.2)4,706 (90.1)BUN, mg/dL11.0 (9.0, 13.0)11.0 (9.0, 14.0)11.0 (9.0, 13.0)0.719Cr, mg/dL0.7 (0.6, 0.8)0.7 (0.6, 0.8)0.7 (0.6, 0.8)0.473eGFR, mL/min/1.73 m2110.7 ± 16.7108.5 ± 16.2111.0 ± 16.8 < 0.001BMI, kg/m229.8 ± 8.532.0 ± 9.029.5 ± 8.4 < 0.001Obesity < 0.001Yes2,465 (42.4)352 [55]2,113 (40.8)No3,352 (57.6)288 [45]3,064 (59.2)Diabetes Mellitus < 0.001Yes295 (5.0)57 (8.8)238 (4.6)No5,573 (95.0)592 (91.2)4,981 (95.4)Hypertension < 0.001Yes950 (16.2)140 (21.6)810 (15.5)No4,918 (83.8)509 (78.4)4,409 (84.5)MetS0.012Yes559 (9.5)80 (12.3)479 (9.2)No5,313 (90.5)569 (87.7)4,744 (90.8)Ever been pregnant < 0.001Ever4,063 (76.1)542 (85.2)3,521 (74.9)Never1,276 (23.9)94 (14.8)1,182 (25.1) Table 2 shows the outcomes of unweighted multivariable logistic regression studies examining the association between SUA levels and the likelihood of infertility. In the initial model, SUA levels were positively associated with female infertility (OR = 1.19; $95\%$ CI: 1.11–1.288). In adjusted models, the connection between SUA levels and the risk of female infertility in women was still positive (Model 2: OR = 1.19, $95\%$ CI: 1.1–1.28; Model 3: OR = 1.22, $95\%$ Cl 1.13–1.32; Model 4: OR = 1.16, $95\%$ Cl 1.06–1.27). Female infertility was $76\%$ more likely for women with SUA in the highest quartile [OR = 1.76, $P \leq 0.001$], compared to $59\%$ more likely in Model 4 [aOR = 1.59, $$P \leq 0.002$$].Table 2Relationship between SUA (mg/dL) and fertility or infertilityExposureOdds ratio ($95\%$ confidence interval)Model 1 ($$n = 5$$,872)Model 2 ($$n = 5$$,336)Model 3 ($$n = 5$$,335)Model 4 ($$n = 5$$,328)SUA (mg/dL)1.19 (1.11–1.28)1.19 (1.1–1.28)1.22 (1.13–1.32)1.16 (1.06–1.27) < 0.001 < 0.001 < 0.0010.001Q1 (≤ 3.6)1(Ref)1(Ref)1(Ref)1(Ref)Q2 (3.7–4.3)1.14 (0.88–1.48)1.15 (0.87–1.53)1.15 (0.87–1.53)1.12 (0.84–1.49)0.3240.3170.3180.454Q3 (4.4–5.1)1.42 (1.11–1.82)1.49 (1.14–1.94)1.52 (1.16–1.98)1.38 (1.04–1.82)0.0050.0030.0020.024Q4 (≥ 5.2)1.76 (1.38–2.24)1.8 (1.39–2.35)1.86 (1.42–2.44)1.59 (1.19–2.13)Model 1: adjusted for noneModel 2: adjusted for social demographic covariables (age, race/ethnicity, education, and PIR) and health insurance coverageModel 3: adjusted for: Model 2 + BUN + Cr + eGFRModel 4: adjusted for: Model 3 + BMI + DM + hypertension + MetS In Fig. 2, the outcomes of the subgroup analysis are displayed. Participants aged 18 to 35 (OR = 1.27, $95\%$ CI: 1.01–1.61), married or cohabiting (OR = 1.18, $95\%$ CI: 1.01–1.38), below the poverty line (OR = 1.23, $95\%$ CI: 1–1.51), and those without obesity (OR = 1.37, $95\%$ CI: 1.12–1.68) showed the connection between infertility and SUA levels. Participants between the ages of 35 and 49, those who were single, lived above the poverty line, had never given birth, or belonged to the obesity categories, did not show any correlation. Fig. 2Association between SUA and infertility. Each stratification was adjusted for age, sex, race and ethnicity, educational level, marital status, family income, health insurance, drinking, smoking, BUN, Cr, eGFR, BMI, DM, hypertension, and MetS except for the stratification factor itself
## Discussion
In this nationally representative cross-sectional study, infertility was prevalent among women between the ages of 18 and 49 at an estimated $11.1\%$, which was within the range of the reported national prevalence ($6.7\%$–$15.5\%$) [36, 37]. There is a positive correlation between SUA and infertility among US female adults. In sensitivity studies, the magnitude and direction of this connection remained constant. The strength of the dose-dependent relationship between the SUA quartiles and infertility increased. The largest connection between infertility and SUA was seen among participants in the highest quartile (Q4). One of the interesting findings in this research is that the strength of the connection between SUA values and infertility grew in a dose-dependent manner. This was more notable in secondary infertility than primary infertility.
To the best of our knowledge, this is the first investigation into the relationship between SUA and infertility among American women of reproductive age who took part in the NHANES between 2013 and 2020. Studies on the connection between SUA and female infertility status are sparse and inconsistent. High SUA levels are associated with MetS, diabetes mellitus, cardiovascular disease, kidney disease, and female reproductive disorders [21, 22, 31, 38].
Previous studies have linked decreased fertility to increasing age, obesity, diabetes, MetS, hypertension, and gout [39–42]. Because SUA levels and female fertility are both associated with a variety of disease states, we were curious to see if there was a link between the two. Subfertility and infertility are terms that can be used interchangeably, and infertility is a disease that causes disability by impairing function [43]. Numerous plausible processes may underlie the link between SUA and infertility, according to earlier investigations. Previous studies have demonstrated that the antioxidant effect of physiological levels of SUA serves as an in vivo protective function [7]. When antioxidants like ascorbic acid are scarce, SUA can become an oxidant and participate in a variety of pathological processes caused by oxidative stress [6]. An imbalance between pro-oxidants and antioxidants can contribute to female reproductive difficulties like endometriosis, PCOS, and unexplained infertility [44]. In the reproductive system, excessive SUA levels are also linked to PCOS, endometriosis, pregnancy difficulties, adverse fatal outcomes, and other diseases [14, 16, 17, 22, 45]. As a result, we believe there is a link between SUA levels and female infertility.
Multiple body organs and systems can become damaged by an excessive buildup of SUA [38]. There is substantial evidence that uric acid levels can directly cause inflammation and abnormal lipid metabolism [8, 9]. Inflammatory pathways and hormonal aberrations are shared by reproductive illnesses (adenomyosis, endometriosis, uterine fibroids, and PCOS) and unexplained infertility and may reduce pregnancy success through shared processes [46]. Such processes could also contribute to the decreased fecundity of patients with endometriosis or POCS [47, 48]. Some infertility reasons have been connected to ovarian inflammation. According to one study, SUA levels have an impact on the quality of semen and male infertility [49]. It seems biologically plausible to have excessive SUA levels as a risk factor for excessive SUA levels and more studies are needed to confirm our findings and investigate the underlying mechanisms.
It has been proposed that dietary treatments provide a secure, economical means of controlling hyperuricemia. The Dietary Approaches to Stop Hypertension (DASH) diet and the Mediterranean diet, are well-known to help obtain optimal SUA levels, have differing SUA-lowering effects, and have been mentioned in previous reports on dietary styles restricting the consumption of fats and meats [50, 51]. Insulin resistance impairs glycolysis and the kidneys' ability to remove SUA, causing the increased synthesis of SUA and decreased urine SUA clearance [52]. According to a review, women had improved fertility when they followed healthy diets that prioritised fish, poultry, whole grains, fruits, and vegetables [24]. Excluding drug control, therapeutic lifestyle changes, appropriate weight loss, and adequate physical activity are beneficial to overall health while also improving hyperuricemia and infertility [53, 54]. For women who would like to become pregnant, paying attention to their nutrition and lifestyle choices, as well as their SUA levels, will help improve fertility.
This study had several strengths and limitations. The use of NHANES data is advantageous because it offers a nationally representative data set, extensive and detailed information on nutritional, demographic, and lifestyle factors, objective cognitive performance tests, and biological samples to control for known major confounders. The current study contains several drawbacks. First, because this research was cross-sectional, we were unable to draw any conclusions about the cause of the link between SUA and infertility in women of reproductive age. Second, women might not be able to recall exactly how long they attempted to get pregnant because infertility was measured through self-reporting. Thirdly, we could have missed infertile women who haven't attempted to become pregnant yet. Lastly, the study did not include data on concomitant gynaecological diseases (such as endometriosis, PCOS, fibroids, polyps, etc.), and we did not treat serum lipids as a covariate [55]. However, we did explore the potential influence of obesity and MetS on the association between SUA and infertility.
## Conclusions
According to the results of this cross-sectional investigation, SUA is positively correlated with infertility in the US adult female population. This discovery assists clinicians in consciously controlling SUA levels to decrease the rate of infertility.
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|
---
title: Determinants of age-related decline in walking speed in older women
authors:
- Valéria Feijó Martins
- Luigi Tesio
- Anna Simone
- Andréa Kruger Gonçalves
- Leonardo A. Peyré-Tartaruga
journal: PeerJ
year: 2023
pmcid: PMC10007973
doi: 10.7717/peerj.14728
license: CC BY 4.0
---
# Determinants of age-related decline in walking speed in older women
## Abstract
### Background
Walking speed is reduced with aging. However, it is not certain whether the reduced walking speed is associated with physical and coordination fitness. This study explores the physical and coordination determinants of the walking speed decline in older women.
### Methods
One-hundred-eighty-seven active older women (72.2 ± 6.8 years) were asked to perform a 10-m walk test (self-selected and maximal walking speed) and a battery of the Senior fitness test: lower body strength, lower body flexibility, agility/dynamic balance, and aerobic endurance. Two parameters characterized the walking performance: closeness to the modeled speed minimizing the energetic cost per unit distance (locomotor rehabilitation index, LRI), and the ratio of step length to step cadence (walk ratio, WR). For dependent variables (self-selected and maximal walking speeds), a recursive partitioning algorithm (classification and regression tree) was adopted, highlighting interactions across all the independent variables.
### Results
Participants were aged from 60 to 88 years, and their self-selected and maximal speeds declined by $22\%$ and $26\%$ ($p \leq 0.05$), respectively. Similarly, all physical fitness variables worsened with aging (muscle strength: $33\%$; flexibility: 0 to −8 cm; balance: $22\%$; aerobic endurance: $12\%$; all $p \leq 0.050$). The predictors of maximal walking speed were only WR and balance. No meaningful predictions could be made using LRI and WR as dependent variables.
### Discussion
The results suggest that at self-selected speed, the decrease in speed itself is sufficient to compensate for the age-related decline in the motor functions tested; by contrast, lowering the WR is required at maximal speed, presumably to prevent imbalance. Therefore, any excessive lowering of LRI and WR indicates loss of homeostasis of walking mechanics and invites diagnostic investigation.
## Introduction
Walking speed is frequently investigated in the older adult population (Mian et al., 2006; Schoene et al., 2017; Frimenko, Goodyear & Bruening, 2015). Aging, even under healthy conditions (Michel & Sadana, 2017), is associated with visible stiffness in ambulation, more prudent walking, and quantitative changes in virtually all walking parameters. Such changes include shorter stride length and frequency (hence, lower speed), larger step width, reduced trunk mobility, and increased risk of falls (Mian et al., 2006; Aboutorabi et al., 2016; Herssens et al., 2018; Schoene et al., 2017). Spontaneous walking speeds below 1.0 m s−1 are associated with increased mortality (Cesari et al., 2005; Figgins et al., 2021). Reduced walking speed is also related to metabolic/cardiovascular, and mental/neurologic comorbidities (Mone & Pansini, 2020; Mone et al., 2022a, 2022b; Noh et al., 2020; Zanardi et al., 2021).
Declines in the most diverse body functions can contribute to changes in walking performance (Cruz-Jimenez, 2017; Miller, Bemben & Bemben, 2021; Pantoja et al., 2016). Cardiorespiratory endurance reaches its maximum capacity at about 20 years of age, after that, until 65 years of age, there is a 20–$30\%$ reduction in cardiac output (Erkkola, Vasankari & Erkkola, 2021). Maximal muscle strength reduces by 15–$30\%$ every 10 years after the fifth decade of life (Papadopoulou, 2020; Pantoja et al., 2016). Muscle power production may also decrease because of mitochondrial dysfunction (Conley et al., 2007). Changes in joint flexibility may explain the lower range of joint excursions, subtended mainly by loss of joint cartilage and a decrease in collagen concentration, entailing loss of compliance and elasticity of joint capsules, ligaments, and tendons (Kothari et al., 2016; Erkkola, Vasankari & Erkkola, 2021). Decreased balance seems to induce changes in walking speed (Cruz-Jimenez, 2017) as the decrease in body balance may stem from delayed muscle recruitment; impaired anticipatory and compensatory postural adjustments; loss of proprioceptive fibers (Sanders et al., 2019; Gerards et al., 2021; Martina et al., 1998); and decreased stiffness of calf tendons, leading to delayed elongation of muscle spindles (Onambele, Narici & Maganaris, 2006). The age-associated decline in static and dynamic balance variables related to postural sway has been estimated at $1\%$ per year (Takeshima et al., 2014).
Changes in neural control also play an important role in age-related changes in walking mechanics (Mian et al., 2006; Ortega & Farley, 2007). Furthermore, neural control can provide functional compensation for metabolic and dynamic losses due to, e.g., muscle overactivation and co-activation (Ortega & Farley, 2007; Miller, Bemben & Bemben, 2021; Delabastita et al., 2021). A simple, more general form of adaptation is lowering the walking speed. However, this adaptation is not without disadvantages. The muscular work during walking is minimized by an inverted pendulum (Alexander, 2005). Maximizing the effectiveness of this mechanism requires a given speed (Cavagna, Thys & Zamboni, 1976) and, for any given speed, a given step length and, therefore, a given cadence (Cavagna & Franzetti, 1986). The optimal step length, at optimal speed, is very close to those spontaneously adopted by young adults (Cavagna, Thys & Zamboni, 1976; Peyré-Tartaruga & Monteiro, 2016). Lower or higher speeds imply higher external work and cost metabolic (Tesio, Roi & Möller, 1991). For any given speed, increasing cadence implies a higher muscular work to reset, at each step, the limbs with respect to the body center of mass (Willems, Cavagna & Heglund, 1995).
Studies assessing spontaneous walking speed in older adults have obtained contradictory results that seem highly sample-dependent (Herssens et al., 2018; Fukuchi, Fukuchi & Duarte, 2019; Boulifard, Ayers & Verghese, 2019). Speed measures range from 0.79 (Boulifard, Ayers & Verghese, 2019) to 1.34 m s−1 (Fukuchi, Fukuchi & Duarte, 2019). Naturally, speed needs to be normalized by body height (or lower limb length), e.g., through the dimensionless Froude number (Cavagna & Franzetti, 1986). Another possible determinant of reduced preferred speed is dynapenia (reduced muscle strength). This reduction would imply an impairment in the forward propulsive function of the gastrocnemius muscle (Conway et al., 2021).
The comparison of possible mechanisms of reduced speed between older women and men is lacking. Walking habits (Leung et al., 2009) and body size can interact with age in determining walking speed and cadence. Even though the reduction in walking speed is similar between the sexes, women reduce stride length proportionally more than men, reducing stride frequency less than men (Frimenko, Goodyear & Bruening, 2015).
Whether walking speed depends on physical fitness and why healthy older adults tend to adopt slower speeds even over short distances is still an open question. A previous study has revealed that step time (inverse of step frequency) had the greatest influence on the reduction of walking speed in senior women (Fien et al., 2019). Thus, some coordination parameters, such as walk ratio index (WR) and balance, seem to be related to reduced speed due to the task of swinging limbs during walking (Gomeñuka et al., 2020). Also, a recent retrospective cohort study has reported that long-term participation in a community-based exercise program delays age-related declines in walking speed and lower extremity muscle strength (Hayashi et al., 2021); however, other physical fitness parameters were not evaluated.
This study aims to investigate the motor skills that determine the walking speed in older women. Factors that affect step length and frequency, such as muscle strength and balance, are candidates to explain the reduced functional mobility of older women.
## Materials and Methods
This is an open, cross-sectional study carried out in a university extension program in southern Brazil. The study was approved by the local ethics committee (Universidade Federal do Rio Grande do Sul, project number: 17243819.0.0000.5347 and clinical trials ID:NCT04348539). All individuals who agreed to participate signed an informed consent form. For the Istituto Auxologico Italiano, this study fell within the RESET research program, Ricerca Corrente IRCCS, Italian Ministry of Health.
## Participants
One hundred eighty-seven untrained older women were recruited through the media (including social media). All were non-frailty individuals (Fried frailty index). The recruitment was on the School of Physical Education, Physiotherapy and Dance website of the Federal University of Rio Grande do Sul (https://www.ufrgs.br/esefid/site/). The site has a space for disseminating studies to the community. Inclusion criteria for sample selection were age between 60 and 90 years, community-dwelling status, regular physical training program in the last 3 months at least two sessions per week, and verbal understanding instructions for testing and demonstrating independent ambulation. Exclusion criteria included the use of assistive mobility devices or any walking limitation.
## Assessments instruments
For the measurement of self-selected walking speed (SSWS), the 10-m walk test was used. And the participants were asked to walk three attempts at their preferred usual speed in a 14-m straight line, measured at 10-m and discarded the first and last 2 m, which correspond to the period of acceleration and deceleration of the walking (Novaes, Miranda & Dourado, 2011). The same procedure was applied to determine the maximal walking speed. Here, the participants were instructed “to walk as fast as possible without running”. The time, in seconds, was measured using a digital stopwatch, and the mean of three repetitions was used for further analysis. Speeds are presented in meters per second.
The locomotor rehabilitation index (LRI) was calculated as the ratio of the observed walking speed to the predicted optimal (lowest cost) walking speed (Peyré-Tartaruga & Monteiro, 2016; Gomeñuka et al., 2019). Subject’s optimal walking speed was estimated using the dimensionless Froude number (Fr), as shown in Eq. [ 1]: [1] \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$\rm Fr = v^2 {/} (g \times L)$$\end{document}Fr=v2/(g×L)where v is the speed, g is the gravity acceleration, and L is the lower limb length (measured from the anterior-inferior iliac spine to the ground through the lateral malleolus) (Vaughan & O’Malley, 2005). The dimensionless optimal walking speed (OWS, Eq. [ 2]) in humans corresponds to Fr = 0.25 (Eq. [ 1]). So that, [2] \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$${\rm OWS} = \sqrt {0.25\; \times {\rm \; g\; } \times {\rm L}} \;$$\end{document}OWS=0.25×g×L Thus, the LRI is as follows (Eq. [ 3]):
[3] \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$\rm LRI = 100 \times SSWS / OWS$$\end{document}LRI=100×SSWS/OWS The LRI has been applied to assess different populations, including patients with heart failure (Figueiredo et al., 2013), Parkinson’s disease (Monteiro et al., 2017), and older adults trained in Nordic walking (Gomeñuka et al., 2019).
The WR was calculated as the ratio of step length to cadence (Sekiya & Nagasaki, 1998; Rota et al., 2011; Bogen et al., 2018; Kalron et al., 2020), with step length expressed in mm and cadence in steps min−1. The WR serves as a sensitive indicator of neural and cognitive walking impairments: it significantly decreases in multiple sclerosis (Rota et al., 2011; Kalron et al., 2020) and Parkinson’s disease (Zanardi et al., 2021) as well as in healthy subjects under high attentional demands (Almarwani et al., 2019).
Four tests were used to assess motor parameters that potentially influence walking mechanics. Tests are from the Senior Fitness Test battery (Rikli & Jones, 1999) (see legend of Table 1 for short descriptions): (i) 8-foot up and go (agility/dynamic balance test, ABa), (ii) 30-s chair stand (lower body strength, LBS), (iii) 2-min step (aerobic endurance, AE), and (iv) chair sit and reach (lower body flexibility, LBF). These tests have been extensively validated, do not require any special equipment, and can be easily applied in any clinical or exercise environment (Rikli & Jones, 2013; Gonçalves et al., 2021).
**Table 1**
| Unnamed: 0 | Mean (SD) | Median (IQR) | Range |
| --- | --- | --- | --- |
| Age (years) | 72.22 (6.8) | 72 (67–77) | 60–88 |
| Height (m) | 1.56 (0.06) | 1.56 (1.53–1.61) | 1.39–1.73 |
| BMI | 28.37 (4.67) | 27.95 (25.22–30.88) | 19.85–49.07 |
| LRI (%) | 90.0 (13.83) | 90.5 (80.6–100.4) | 60.1–120.7 |
| WR | – | 0.56 (0.52–0.63) | 0.35–1.02 |
| LBS (no. full stands) | – | 16 (13–19) | 6–30 |
| LBF (cm)* | −3.44 (10.70) | −2 (−9 to 3) | −29 to 25 |
| ABa (seconds)* | – | 5.1 (4.52–5.65) | 3.37–8.85 |
| AE | 87.50 (15.89) | 87 (79–97) | 53–128 |
| SSWS (m s−1) | 1.30 (0.22) | 1.31 (1.16–1.42) | 0.77–1.87 |
| MWS (m s−1) | 1.74 (0.30) | 1.74 (1.57–1.90) | 0.94–2.74 |
## Statistical analysis
The predictive models for either SSWS or maximal walking speed and either LRI or WR were applied. Given that multicollinearity is expected across variables describing a subject’s motor performance (mostly between maximal walking speed and SSWS but also between speed and LRI), a decision-tree model rather than a conventional multiple regression model was used.
SSWS, maximal walking speed, LRI, and WR data were tested for normality of distribution based on skewness and kurtosis and then summarized as mean (standard deviation, SD) and median (interquartile range, IQR) or median (IQR) when appropriate. Significance was set at $p \leq 0.05$, and p-values were Bonferroni-adjusted for multiple comparisons. A predictive regression model was applied using a recursive partitioning algorithm, i.e., a classification and regression tree (CART) model. This analysis is distribution-free and transforms continuous levels into ordinal grades. The algorithm builds a decision tree based on binary splits on variables (either continuous, ordinal, or categorical). At each split, nodes are generated, and these nodes can be further split. The algorithm automatically detects interactions (i.e., the tree/node structure) between independent variables, providing the highest explanation of variance for the dependent variable (either categorical or continuous; here, continuous). The final result (terminal nodes) comprises a series of classes with the lowest possible within-class variance and the highest possible between-class variance. Unlike conventional linear regression modeling, in which the analyst must specify the expected interactions, CART itself discovers interactions, even high-order ones that are very difficult to hypothesize (Breiman et al., 1984). The algorithm is more sensitive to interactions than to main effects. The model’s variance explanation is much less vulnerable to multicollinearity issues. Each split is performed on a single variable. The latter is ignored if no further information is added by further splitting on a covariate. Software packages typically allow the analyst to control the procedure by imposing a minimum number of observations on each node or by setting stopping rules for tree branching (for a simple clinical example, see D’Alisa et al., 2006). A priori knowledge or requirements can thus complement the purely algebraic search for the maximum amount of variance explained. The stability of the predicted model can be inferred either by imposing the model splits (from the building sample) to an independent (validation) sample or by simulating several independent samples (boot-strapping) originating from the available sample. This procedure is typically done through random extraction of subsamples and substituting their values by random replication of observations from the remaining sample or the original total sample (resampling). In any case, the amount of variance explained for the validated tree unavoidably declines (shrinks) concerning the variance explained for the original sample. It is left to the analyst to decide whether the model is satisfactorily stable or not (Breiman et al., 1984). There is no rule of thumb for accepting a given amount of variance explained. A reasonable empirical threshold for the validation tree is $30\%$, as suggested by the results for trees effectively predicting the length of stay, care costs, and functional outcomes of rehabilitation inpatients in the USA (Stineman, 1995).
In the present study, CART analysis is initiated from unsplit dependent variables (SSWS, maximal walking speed, LRI, WR) (root nodes). Each variable was split into nodes according to optimal cut-off points for the remaining variables to maximize the variance explained. Splitting continued until terminal nodes were defined, building the final classification model. The limitations imposed on each tree were as follows: maximum splitting levels, 10; splitting algorithm, least squares; minimum size node to split, 10; minimum rows allowed in a node, 5; tree pruning and validation method, cross-validation; the number of cross-validation folds, 10; and tree pruning criterion, within one standard error of minimum cost complexity.
Descriptive statistics and regression modeling were done using IBM SPSS® version 21.0 (IBM Corporation, Armonk, NY, USA), and STATA® software (version 16.0; Stata Corp. LLC, College Station, TX, USA). CART analysis was done through DTREG® software (DTREG, Brentwood, TN, USA, 2021).
## Results
Table 1 provides descriptive statistics and a short definition of all variables assessed in this study.
Age, SSWS, maximal walking speed, LRI, WR, and the four independent variables (ABa, LBS, AE, and LBF) were tested for normality based on skewness and kurtosis (Bonferroni-adjusted $p \leq 0.006$). Only ABa, LBS, and WR were significantly nonnormal (data not shown); thus, the assumption of linear regression was violated. For each of these variables, observations smaller than or greater than three SDs beyond the mean were trimmed (for linear regression only, not for further analyses). The WR ratio remained nonnormal ($p \leq 0.005$ for skewness and kurtosis), as it had a uniform distribution. Linear regression was applied despite this limitation.
Table 2 revealed that all variables worsened with age (with confidence limits never including zero). The change was not significant for AE. In any case, the worsening was moderate. From 60 to 88 years of age, SSWS and maximal walking speed worsened (i.e., declined) by $22\%$ and $26\%$, respectively. For the other variables, worsening ranged from $14\%$ to $33\%$. For LBF, a percentage change would be misleading: finger–toe distance increased from 0 to 8 cm. The variance explained by age was low for all variables, exceeding $10\%$ for WR.
**Table 2**
| Unnamed: 0 | n | β (95% CI) | Const (95% CI) | R2 | p # | Change§ |
| --- | --- | --- | --- | --- | --- | --- |
| LRI | 185* | −0.419 [−0.711 to −0.128] | 120.2 [99.08–141.3] | 0.04 | 0.0051 | −14% |
| WR | 187 | −0.003 [−0.005 to −0.001] | 0.803 [0.641–0.966] | 0.04 | 0.0087 | −15% |
| LBS | 185^ | −0.158 [−0.245 to −0.070] | 27.30 [20.95–33.66] | 0.06 | 0.0005 | −33% |
| LBF | 187 | −0.331 [−0.555 to −0.108] | 20.49 [4.282–36.70] | 0.04 | 0.0039 | 107%& |
| ABa | 183^ | 0.048 [0.030–0.065] | 1.718 [0.461–2.976] | 0.14 | 0.0 | 22% |
| AE | 187 | −0.364 [−0.700 to −0.029] | 113.8 [89.50–138.1] | 0.02 | 0.0333 | −12% |
| SSWS | 187 | −0.009 [−0.133 to −0.004] | 1.932 [1.600–2.266] | 0.07 | 0.0002 | −22% |
| MWS | 187 | −0.014 [−0.020 to −0.008] | 2.726 [2.283–3.168] | 0.1 | 0.0 | −26% |
The correlation matrix of the nine variables (Fig. 1) gives an overview of bivariate associations.
**Figure 1:** *The scatterplot provides the correlation half-matrix of the parameters. Pearson’s correlation coefficients are given in the corresponding boxes.Parameters: age, locomotor rehabilitation index (LRI), walk ratio (WR), lower body strength (LBS), lower body flexibility (LBF), agility/dynamic balance test (ABa), aerobic endurance (AE), self-selected walking speed (SSWS), and maximal walking speed (MSW).*
Low values of AE (cardiorespiratory fitness index) and LBF (joint flexibility index) indicate better performance. Fig. 1 shows that most of Pearson’s correlation coefficients were very low. Only the correlation coefficients between LRI and SSWS (0.96), LRI and maximal walking speed (0.51), and SSWS and maximal walking speed (0.53) were higher than the arbitrary threshold of |0.5|. These findings were expected (see Eqs. [ 1]–[3]), given that these variables are either derived from each other (LRI and maximal walking speed or SSWS) or strictly dependent on the subject’s height (maximal walking speed and SSWS).
Interactions between multiple variables were explored through CART analysis. Fig. 2 depicts the decision trees used to predict SSWS (left panel) and maximal walking speed (right panel).
**Figure 2:** *The scatterplot provides the correlation half-matrix of the parameters. Pearson’s correlation coefficients are given in the corresponding boxes.Parameters: age, locomotor rehabilitation index (LRI), walk ratio (WR), lower body strength (LBS), lower body flexibility (LBF), agility/dynamic balance test (ABa), aerobic endurance (AE), self-selected walking speed (SSWS), and maximal walking speed (MSW).*
Figure 3 shows the trees developed to predict LRI (left panel) and WR (right panel).
**Figure 3:** *Final classification and regression tree (CART) prediction models of locomotor rehabilitation index (LRI) and walk ratio (WR).Parameters: age, locomotor rehabilitation index (LRI), walk ratio (WR), lower body strength (LBS), lower body flexibility (LBF), agility/dynamic balance test (ABa), aerobic endurance (AE), self-selected walking speed (SSWS), and maximal walking speed (MSW).*
Table 3 summarizes the variance explained (for training/building and validation data) for each of the four trees shown in Figs. 2 and 3.
**Table 3**
| Target variable | Variance explanation % | Variance explanation %.1 |
| --- | --- | --- |
| Target variable | Training data | Validation data |
| LRI | 95% | 93% |
| WR | 33% | 21% |
| SSWS | 84% | 80% |
| MWS | 50% | 36% |
As demonstrated in Table 3, the variance explained by validation trees was satisfactory for SSWS, maximal walking speed, and LRI (ranging from $36\%$ to $93\%$) but barely acceptable for WR ($21\%$). The results suggest that most independent variables, including age, were not predictive of SSWS. In the corresponding tree, only LRI was retained, a circular finding (see above). By contrast, maximal walking speed was explained by SSWS (another expected finding) and, notably, by WR for speeds below 1.23 m s−1 as well as by ABa for WR values of less than or equal to 0.7 (most of the cases).
## Discussion
The expected associations between SWSS and maximal walking speed did not convey meaningful information. Although expected, the association between SWSS and LRI indicates that $7\%$ of the variance in SWSS is related to size effects. Therefore, LRI seems to be an improved marker of functional mobility due to size-dependent variation in walking speed. Other points deserve consideration. Neither age nor any of the four motor indices selected (LBS, LBF, Aba, and AE, see legend of Table 1) nor WR explained SSWS. Maximal walking speed was partially explained by the interaction between WR and ABa (Fig. 2). The WR tree (Fig. 3) confirms the relationship of WR with speed.
## An algebraic explanation
It must be said that the explanation of variance requires some variance to be explained and a covariance. Along a 28-year gradient, the spontaneous and maximal speeds of older women undergo small changes, with a high interindividual variation. On the other hand, WR is largely invariant with speed and age. Not surprisingly, the weak relationship between speed and age (Table 1) is lost if a bivariate association is abandoned in favor of an interactive model (Figs. 2 and 3). Of course, a greater sample size might have allowed obtaining a more branched and explanatory decision tree. This algebraic interpretation, however, does not seem to be entirely satisfactory. An interpretation based on physiology from outside the data should be considered based on numerical assumptions.
## Looking for an explanation in physiology
The results suggest that healthy aging implies a mild tendency for a decrease in SSWS, unexpectedly unrelated to the various physical performance parameters analyzed and the step length/cadence ratio (WR). The question then arises: given that these women were capable, at various ages, of increasing their speed (on average, SSWS was 1.30 m s−1, whereas maximal walking speed was 1.74 m s−1), why did they not retain the same SSWS at all ages? The second unanswered question is, how could WR remain unrelated to age and the various motor performance parameters? After all, step length and cadence should reflect lower limb joint power, mobility, and balance.
One reason may be that human walking has very wide margins of safety. In symmetric gaits, overall energy expenditure is minimal, given the refined pendulum-like exchange of mechanical energy of the center of mass. This characteristic makes humans the most efficient walkers in the animal realm (Sockol, Raichlen & Pontzer, 2007; Henn, Cavalli-Sforza & Feldman, 2012). The cardiorespiratory power and the power required to drive muscles (mainly the plantar flexors) remain much below the ceiling level (Tesio et al., 2017). Lower limb joint excursions retain wide mobility margins despite having a more overall flexed posture. In focal strength deficits, compensation may occur between limbs and, within the same limb, between joints (Tesio, Roi & Möller, 1991; Tesio & Rota, 2019). Once the speed needs to be decreased (see below for further explanation), there seems to be no need for taking longer and more frequent steps than that already foreseen for the new speed. In case of need, however, a wide margin of safety remains for decreasing WR. In fact, at any given speed, a decrease in step length has a minimal influence on the effectiveness of the pendulum mechanism until a $50\%$ decrease is reached (Cavagna & Franzetti, 1986; Tesio et al., 2017).
Therefore, as a form of speculation, it can be hypothesized that cardiac–energetic or musculoskeletal constraints do not determine the age-related decline in speed. Rather, as suggested by several authors (Ortega & Farley, 2007; Miller, Bemben & Bemben, 2021; Delabastita et al., 2021), balance control may represent a hidden, relevant determinant of the mild age-related decrease in SSWS.
## The role of balance compared to other walking constraints
Over short distances, one can well afford a mildly higher metabolic cost unless this is prevented by severe cardiac or respiratory deficits (Tesio, Roi & Möller, 1991; Willems, Cavagna & Heglund, 1995). However, because of its pendulum-like mechanics, the body center of mass must be accelerated forward, upward (Cavagna, Thys & Zamboni, 1976), and laterally (Tesio & Rota, 2019) at each step to overcoming ground friction and gravity acceleration; the greater the ground friction, the longer the step, and the faster the movement, the shorter the step duration. These mechanical demands decrease by reducing walking speed. In particular, such a decrease in speed leaves more time for the amazingly fast U-turn from one side to the opposite at each step, as demonstrated by the analysis of the 3D trajectory of the body center of mass during a single stance (Malloggi et al., 2021).
Once the speed is conveniently lowered, therefore, a further decrease in step length (as evidenced by a lower WR) would unnecessarily entail a higher “internal” work per unit distance, i.e., the muscular work needed to reset the limbs at each step (Willems, Cavagna & Heglund, 1995). Not surprisingly, WR remained nearly invariant with age and SSWS in the present sample of women, confirming literature data on a wide range of velocities and adult ages (Rota et al., 2011; Bogen et al., 2018). This invariance, however, does not hold for the maximal walking speed showing that the spatiotemporal coordination pattern represented by WR in the present study is altered in aged women at high walking speeds.
Consistently with its explanatory role, balance is known to decrease in healthy aging. In the present study, ABa was the variable that most depended on age (Table 2). It entered the prediction algorithm of maximal walking speed together with WR only. These results point toward a pivotal role of balance in determining the decline of speed in aging, at least at higher speeds. It should be noted that WR is diminished whenever the balance is primarily affected (see Introduction). At any speed, WR decreases when walking on slippery surfaces (Cappellini et al., 2010), and, as a rule, in the case of neural impairments. Furthermore, the higher co-activation of lower limb muscles (Mian et al., 2006; Gomeñuka et al., 2020) may help to understand balance’s role in reducing walking speed in older women. The typical WR for adults and older adults up to 85 years is in the order of 5.5–6.5 mm step−1 min−1, across a wide range of walking speeds and body heights (Sekiya & Nagasaki, 1998; Rota et al., 2011), and in line with our findings. Of note, this parameter is consistently lower by about $5\%$ in women than men (Bogen et al., 2018).
## Aging and walking, and what LRI and WR tell us
To sum up, in healthy aging, the decrease in speed (either self-selected or maximal) is modest (Sanders et al., 2019; Gerards et al., 2021; Martina et al., 1998). LRI and WR, which are related to each other, are virtually stable and seem unrelated to cardiorespiratory and musculoskeletal performance. This result is no surprise, given the high effectiveness of human bipedalism. LRI and WR indices provide complementary information. They both seem to reflect a homeostatic control of walking, so alterations might represent alarming early predictors of latent cardiorespiratory or joint power limitations (LRI) and/or latent balance deficit (LRI and WR). In particular, a decrease in LRI indicates a reduced pendulum-like mechanism resulting in a higher energy cost of walking (reduced economy, Gomeñuka et al., 2014, 2016; Peyré-Tartaruga & Monteiro, 2016).
Further, a reduced WR may indicate balance deficits insufficiently compensated for by a reduction of speed. In support of this speculation, one should consider that human bipedalism is unique among bipedal vertebrates in many respects. For instance, the role of plantar flexion is critical as the main “engine” of walking (Usherwood et al., 2012). Another unique feature of particular interest is the need for a refined balance control on the frontal plane (Malloggi et al., 2021; Cassidy et al., 2014). This need can represent a weakness in the case of balance deficits and many other neural impairments, leading to a reduction in speed and, in the most severe cases, further reduction of step length.
Some limitations of the present study cannot be overlooked. First, the results refer only to women. Second, only short distances were tested; speed and LRI and WR indices might have differed at longer distances. Third, the sample size did not validate the predictive model in an independent sample, representing a complementary and perhaps a more robust mode of validation than cross-validation. Finally, questions regarding the chosen statistical methods may have some implications for the results. Future studies in this field are advised including and controlling factors as comorbities and including groups more advanced with symptoms of frailty as in institutionalized individuals (Mone & Pansini, 2020; Mone et al., 2022a, 2022b).
## Conclusions
The maximal walking speed was partially explained by an impaired agility/dynamic balance, and a reduction in muscle strength, flexibility, and balance across age groups was observed. Whereas LRI seems to denote physical capabilities, WR represents a key coordination aspect of functional mobility, particularly related to balance in older women. The results suggest that both LRI and WR are helpful as a short screening battery for walking performance in aging and, potentially, in disability. These indices, however, only measure the presence of complex, tenacious, adaptive, and homeostatic mechanisms so that any alterations should entail a deeper, causal diagnostic inquiry.
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|
---
title: Transcriptome analysis and exploration of genes involved in the biosynthesis
of secoiridoids in Gentiana rhodantha
authors:
- Ting Zhang
- Miaomiao Wang
- Zhaoju Li
- Xien Wu
- Xiaoli Liu
journal: PeerJ
year: 2023
pmcid: PMC10007974
doi: 10.7717/peerj.14968
license: CC BY 4.0
---
# Transcriptome analysis and exploration of genes involved in the biosynthesis of secoiridoids in Gentiana rhodantha
## Abstract
Gentiana rhodantha is a medicinally important perennial herb used as traditional Chinese and ethnic medicines. Secoiridoids are one of the major bioactive compounds in G. rhodantha. To better understand the secoiridoid biosynthesis pathway, we generated transcriptome sequences from four organs (root, leaf, stem and flower), followed by the de novo sequence assembly. We verified 8-HGO (8-hydroxygeraniol oxidoreductase), which may encode key enzymes of the secoiridoid biosynthesis by qRT-PCR. The mangiferin, swertiamarin and loganic acid contents in root, stem, leaf, and flower were determined by HPLC. The results showed that there were 47,871 unigenes with an average length of 1,107.38 bp. Among them, 1,422 unigenes were involved in 25 standard secondary metabolism-related pathways in the KEGG database. Furthermore, we found that 1,005 unigenes can be divided into 66 transcription factor (TF) families, with no family members exhibiting significant organ-specificity. There were 54 unigenes in G. rhodantha that encoded 17 key enzymes of the secoiridoid biosynthetic pathway. The qRT-PCR of the 8-HGO and HPLC results showed that the relative expression and the mangiferin, swertiamarin, and loganic acid contents of the aerial parts were higher than in the root. Six types of SSR were identified by SSR analysis of unigenes: mono-nucleoside repeat SSR, di-nucleoside repeat SSR, tri-nucleoside repeat SSR, tetra-nucleoside repeat SSR, penta-nucleoside repeat SSR, and hexa-nucleoside repeat SSR. This report not only enriches the Gentiana transcriptome database but helps further study the function and regulation of active component biosynthesis of G. rhodantha.
## Introduction
The herbaceous *Gentiana genus* (family Gentianaceae) comprises about 500 species worldwide, and is widely distributed in the temperate and tropical alpine regions of the northern hemisphere including Europe, Asia, northern Australia, New Zealand, North America, reaches Cape Horn along the Andes and northern Africa. There are approximately 247 species in China, which are mainly distributed in the southwest mountainous area. Gentiana has multiple pharmacological effects, including hepatoprotective, anti-inflammatory, antipyretic, etc (Editorial Committee of Chinese Flora, 1988; Dong et al., 2017). Many Gentiana species, including G. scabra, G. rigescens, G. macrophylla, and G. rhodantha have been recorded in the Chinese Pharmacopoeia. They are all perennially erect herbs with bluish-purple flowers. G. scabra and G. rigescens are officially listed in the Chinese Pharmacopoeia under the name Gentianae radix et rhizoma (Longdan and Jianlongdan, respectively, in Chinese). They have been used for jaundice, eczema, and acute conjunctivitis. Moreover, G. macrophylla is perennial and officially listed as *Gentianae macrophyllae* radix (Qinjiao in Chinese) for rheumatic arthralgia, poplexy and hemiplegia. The dried whole herbs of G. rhodantha are officially listed in the Chinese Pharmacopoeia as *Gentianae rhodanthae* herb (Honghualongdan in Chinese) for jaundice, detoxification, and relieving cough (Chinese Pharmacopoeia Commission, 2020). It is not only used as traditional Chinese medicine but also as ethno-medicine in China. It is widely used in ethnic minorities, including Miao, Buyi, Bai, Yao, and Tujia (Luo, 1990; Mo et al., 2003; Zhao, 2005; Jiang, 2015). To date, many Chinese medicines related to G. rhodantha have been developed. Among them, the Feilike mixture treats coughing up sputum, poor breathing, acute and chronic bronchitis, emphysema, and other symptoms (Jin, 2012). The Kangfuling tablet is used for treating gynecological diseases, including cervicitis, vaginitis, menstrual irregularities, red vaginal discharge, dysmenorrhea, adnexitis, etc (Li, 2013). Furthermore, the Lianlong capsule helps in reducing swelling and loosening of knots, along with treatment of thyroid tumors, liver cancer, and other malignant tumors (Yang et al., 2016). In recent years, G. rhodantha wild resources have been declining due to low seed germination rates, poor reproductive ability, and overexploitation (Sun et al., 2016; Shen et al., 2017). Fortunately, tissue culture and a rapid propagation system had been established, making it possible to artificially produce G. rhodantha and regulate its quality (Zhong et al., 2021).
RNA sequencing (RNA-seq) is a powerful technology for genome-wide analysis of RNA transcripts (Grabherr et al., 2011). High-quality transcriptome data not only mine genomic resources, but also facilitate genetic and molecular breeding approaches for metabolic regulation in medicinal plants (Qi, Liu & Rong, 2011). Flavonoids, secoiridoids, and phenolic acids were the main active components in G. rhodantha (Xu et al., 2011; Ma, Fuzzati & Wolfender, 1996; Xu et al., 2008; Yao, Wu & Chou, 2015). Mangiferin is a carbon glycoside of tetrahydroxypyrone, which belongs to the diphenylpyrone group of compounds. It has special properties and has attracted extensive interest as a therapeutic metabolite (Fan et al., 2017; Wang et al., 2022). Secoiridoids, like swertiamarin, are also the main active components of G. rhodantha, which have rich pharmacological effects, including analgesia, hypotension, and osteoblasts proliferation (Chen, 2009; Xu et al., 2008; Sun et al., 2008).
The transcriptomic studies for determining the genes involved in the secoiridoid biosynthetic pathway were performed for multiple Gentiana species, including G. rigescens, G.crassicaulis, and G. waltonii (Kang et al., 2021; Zhang et al., 2015; Ni et al., 2019). However, only the chloroplast genome G. rhodantha has been reported (Hu & Zhang, 2021; Ling, 2020). The mangiferin and secoiridoid biosynthetic pathway is still unknown in G. rhodantha. We first sequenced the transcripts of the root, stem, leaf, and flower collected at the full bloom stage for G. rhodantha using the IIIumina Hiseq 6000 high-throughput platform, and then deciphered all the candidate genes and putative transcription factors involved in the mangiferin and secoiridoid biosynthetic pathway. Although mangiferin is one of the main active components of G. rhodantha, the mangiferin biosynthetic pathway is unavailable in the KEGG official website to date. Therefore, this study focused on the analysis and identification of the putative genes involved in secoiridoid biosynthesis, aiming to provide useful insights into their further quality regulation. Additionally, numerous simple sequence repeats (SSR) markers were found, which will facilitate the marker-assisted breeding of G. rhodantha.
## Materials and Methods
G. rhodantha used in this experiment, was collected from the Yiduoyun village, Kunming, Yunnan, China (25°00′5.62″N, 102°58′46.35″E, 1,068 m). The taxonomic identities of the voucher specimens were identified by the corresponding author. Natural wild G. rhodantha was sampled at the full bloom stage. Fresh root, stem, leaf, and flower were collected from three plants having relatively consistent growth and synchronized development (Figs. 1A, 1B). One half was quickly frozen in liquid nitrogen and stored at −80 °C, while the other half was dried to constant weight at 55 °C and used for determining the mangiferin, swertiamarin, and loganic acid contents.
**Figure 1:** *Representative images of G.rhodantha used for the RNA sequencing and summary of transcriptome annotations.(A) Whole plant including root, stem, leaf, flower; wild state of G. rhodantha. (B) BUSCO completeness assessments of the G. rhodantha transcriptome. Dark blue bars represent complete and duplicated BUSCOs.*
## RNA extraction and cDNA library preparation and RNA-Seq analysis
RNA was extracted from the root, leaf, stem, and flower. First, 1 µg RNA per sample was used for the RNA sample preparations. Sequencing libraries were generated using the NEBNext®Ultra™ RNA Library Prep Kit for Illumina® (New England Biolabs, Ipswich, MA, USA) following the manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Twelve libraries were obtained from three biological replicates per organ. Illumina Novaseq 6,000 sequencing was performed after the library was qualified. The average sequencing depth (count* 150/gene_len) was approximately 456.
A power analysis was performed by RNASeqpower in the R package (Hart et al., 2013). The statistical power of this experimental design, calculated in RNASeqpower, was 0.9347033 (depth = 456, $$n = 3$$, CV = 0.24, effect = 2, alpha = 0.05).
## De novo assembly and sequence processing
First, the raw data (raw reads) of the fastq format were processed through the in-house Perl scripts. Filter joint, low quality, sequences with N bases, low quality bases (Q <20) were removed to get high-quality clean data (Bolger, Marc & Bjoern, 2014). Firstly, Breaking sequencing reads into short segments (K-mer) via TRINITY (https://github.com/trinityrnaseq/trinityrnaseq/wiki) under the parameter of (–min_contig_length 200, –group_pairs_distance 500). These small fragments were then extended into longer segments (contigs). Overlaps between these fragments were used to get a collection of fragments (component). Finally, the De Brujin graph method and sequencing read information were used to identify transcript sequences in each fragment collection. The RSeQC (RNA-seq data QC) software was used to remove the redundant sequences in the transcript to obtain the unigene (Haas, Papanicolaou & Yassour, 2013). Bowtie (v1.0.0)-v 0 was used to compare the sequenced reads with the unigene library. Based on the comparison results, the expression level was evaluated in combination with RSEM (v1.2.19) using the parameters (-a -m 200). The expression abundance of the corresponding unigene was expressed by the FPKM (fragments per kilobase of transcript per million mapped reads) value. FPKM (Trapnell et al., 2010) is a commonly used gene expression level estimation method in transcriptome sequencing data analysis. It can eliminate the effect of differences in gene length and sequencing amount on the computational expression. The FPKM calculation method is as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{eqnarray*}FPKM= \frac{\text{cDNA Fragments}}{\text{Mapped Fragments (Millions)}\ast \text{Transcript Length (kb)}} \end{eqnarray*}\end{document}FPKM=cDNA FragmentsMapped Fragments (Millions)∗Transcript Length (kb) Note: In the formula, cDNA fragments indicates the number of fragments as compared to a certain transcript, the number of double-ended reads; mapped fragments (millions) indicates the total number of fragments as compared to a transcript (in 106 units); transcript length (kb): transcript length (in 103 bases).
The transcriptome assembly was assessed in terms of their completeness and the percentage of complete, fragmented, and missing fragments by using BUSCO (v5.3.2). Parameter: -c 64 -m tran –offline -f -l embryophyta_odb10. ( https://busco.ezlab.org) (Simão et al., 2015).
## Function annotation
The DIAMOND software (v2.0.4) (https://github.com/bbuchfink/diamond) (Buchfink, Xie & Huson, 2015) was used to compare the unigene sequence with the following databases: Nr (https://ftp.ncbi.nlm.nih.gov/blast/) (Deng, Li & Wu, 2006), Swiss-prot (http://www.uniprot.org/) (Apweiler et al., 2004), Clusters of Orthologous Genes (COG) (https://www.ncbi.nlm.nih.gov/COG/) (Tatusov et al., 2000), euKaryotic Orthologous Groups (KOG) (https://ftp.ncbi.nih.gov/pub/COG/KOG/) (Koonin et al., 2004), eggNOG (http://eggnogdb.embl.de/) (Huerta-Cepas et al., 2015) and KEGG (http://www.genome.jp/kegg/) (Kanehisa et al., 2004). KOBAS 2.0 (http://kobas.cbi.pku.edu.cn/) (Xie et al., 2011) was used to get the KEGG Origin result of the unigene in KEGG. After predicting the amino acid sequence of the unigene, the Hammer (v3.1b2) (http://hmmer.org/) (Eddy, 1998) software was used to compare with the Pfam (https://www.ebi.ac.uk/interpro/entry/pfam/#table) (Finn et al., 2013) database to obtain the annotation information of unigenes. The whole transcript data set can be found in the National Center for Biotechnology Information (NCBI) database (BioProject ID: PRJNA816320).
## Screening for secoiridoid biosynthesis genes
By referring the relevant secoiridoid biosynthesis metabolism pathways (Wu & Liu, 2017; Yang, Fang & Li, 2018), the results of nine database annotations were combined. The secoiridoid biosynthesis-related unigenes in the G. rhodantha transcription data were uncovered. The direct embodiment of a gene expression level is the abundance of its transcript. The higher the transcript abundance, the higher was the gene expression level. After referring the secoiridoid biosynthesis pathway in G. scabra, G. rigescens, and G. macrophylla, a possible biosynthetic pathway of G. rhodantha was speculated. Combined with the annotation results in the Nr and KEGG databases, the secoiridoid biosynthesis-related unigene in the transcriptome data was mined, and the expression amount was calculated using the TPM (transcripts per million) value.
## Identification of SSRs
The MISA (v1.0) (http://pgrc.ipk-Gatersleben.de/misa/misa.html) software was used to identify the SSR motif. The input file is a unigene sequence, and six types of SSRs were identified: [1] mono-nucleotide repeating SSR, [2] di-nucleotide repeating SSR, [3] tri-nucleotide repeating SSR, [4] tetra-nucleotide repeating SSR, [5] penta-nucleotide repeating SSR, and [6] hexa-nucleotide repeating SSR.
## Differential expression analysis
DESeq2 (v1.6.3) (Love, Huber & Anders, 2014) was used for differential expression analysis between samples to obtain the differential gene expression set of the two conditions. In the process of differential expression analysis, the Benjamini–Hochberg method was used to correct the significance (p-value) obtained by the original hypothesis test. Finally, the corrected p-value and False Discovery Rate (FDR) were adopted as the key indicator of differential expression gene screening. During the screening process, FDR <0.01 and FC —(fold change)— ≥2 were used as the screening criteria.
## qRT-PCR analysis
The 8-HGO was screened from the secoiridoid biosynthesis pathway using the screening criteria of — log2 (FC) —≥ 2 and FDR <0.01. The 18S rRNA is present in the ribosomal subunit, and its encoding gene rDNA (18S rRNA/rDNA) is evolutionarily conserved. The relative expression of 8-HGO from four organs was verified with 18S rRNA as the internal reference gene. qRT-PCR was performed using the Analytik Jena-qTOWER2.2 (Analytik Jena, Jena, Germany) with TUREscript 1st Stand cDNA SYNTHESIS Kit (Aidlab, Hong Kong). Gene-specific primers were designed using Primer Premier 5.0, and the primer sequences are listed in the (Table 1). The relative gene expression was calculated by the 2−ΔΔCt method (Pfaffl, 2001).
## Measuring the mangiferin, swertiamarin, and loganic acid contents
The mangiferin, swertiamarin, and loganic acid contents were estimated using the 1,260 high-performance liquid chromatography (HPLC) (Agilent, Santa Clara, CA, USA). The extraction and measurement of these three components in G. rhodantha were conducted as per our method for G. rigescens and Liu et al. ( 2022a). First, 2.5 g of dried powder was extracted under ultrasonication in 25 ml $80\%$ methanol for 40 min (power 150 W, working frequency 55 kHz), followed by centrifugation. Chromatographic conditions were: [1] chromatographic column: Agilent Intersil-C18 column (4.6 mm ×150 mm, 5 µm), [2] mobile phase: $0.1\%$ formic acid aqueous solution (A) and acetonitrile (B), flow rate: one mL/ min, gradient elution: (0-−2.5 min, 7–$10\%$ B; 2.5–20 min, 10–$26\%$ B; 20–29.02 min, 26–$58.3\%$ B; 29.02–30 min, 58.3–$90\%$ B; 30–34 min, $90\%$ B), [3] column temperature: 30 °C, [4] sample size: 5 µL, and [5] detection wavelength: 241 nm. The mangiferin, loganic acid, and swertiamarin contents in the different organs were determined by comparing their peak times and retention times with that of the standard. Finally, the data was processed using Excel 2016 and plotted using Graphpad Prism 6.01.
**Table 1**
| Gene name | Primers sequences(5 ′ → 3′) | Annealing temperature (TM) |
| --- | --- | --- |
| 18S-F | CAACCATAAACGATGCCGA | 60 ° C |
| 18S-R | AGCCTTGCGACCATACTCC | 60 ° C |
| 8-HGO-F | GAAGAAGTGAAGGACCTCAAG | 60 ° C |
| 8-HGO-R | CGGGAGCATAAATTCGTCTT | 60 ° C |
## Illumina sequencing and read assembly
We obtained a total of 12 libraries from the four organs, including high-quality reads fragments from 20,423,964, 21,194,626 and 21,917,414 of root, 21,475,005, 20, 386,021, and 19,271,209 of stem, 20,873,444, 21,275,398, and 20,960,228 of leaf and 21,230,658, 21,811,288, and 22,615,547 of flower. After sequence assembly, a total of 47,871 unigenes were obtained. The length of N50 was 1,826 bp, with an average length of 1,107.38 bp (Table 2). Pearson’s correlation coefficient (r) was used as an indicator of studying inter-sample correlation (Schulze, Kanwar & Gölzenleuchter, 2012). The closer r2 is to 1, the stronger was the correlation between the two samples (Fig. 2B). The figure shows that the correlation of three repeats within both the stem and root was >0.8, which indicated high reproducibility. However, the correlation of three repeats within both leaf and flower was not good. PCA (Fig. 2A) analysis showed similar results as the heat maps. A BUSCO analysis was performed to evaluate the completeness, and we recovered 1,292 of the 1,614 conserved eukaryotic genes ($80\%$) (Fig. 1C).
## Functional annotation
After comparing and annotating the unigenes in the nine databases, (including GO, KEGG, and NR), we annotated 31,516 ($65.8\%$) unigenes in at least one database. The specific results were GO annotation 24,320 ($50.8\%$), KEGG annotation 19,547 ($40.8\%$), and NR annotation 30,452 ($63.6\%$) (Table 3).
**Table 3**
| Databases | Number | 300 ≤ length | Length ≥1,000 |
| --- | --- | --- | --- |
| COG | 9846 | 4572 | 5274 |
| GO | 24320 | 11417 | 10735 |
| KEGG | 19547 | 8812 | 9162 |
| KOG | 16889 | 7727 | 9162 |
| Pfam | 22635 | 10143 | 12492 |
| Swissprot | 18472 | 7525 | 10947 |
| TrEMBL | 28198 | 13142 | 15056 |
| eggNOG | 23134 | 10017 | 13117 |
| Nr | 30452 | 15261 | 15191 |
| ALL | 31516 | 16144 | 15371 |
When GO was used to classify gene functions, we assigned 24,320 unigenes to three categories: biological process, cellular component, and molecular function, with a total of 43 branches. Within the ‘biological process’ category, the most enriched categories were the cellular process (13,249, $54.5\%$), metabolic process (12,440, $51.2\%$), and biological regulation (3,798, $11.5\%$). Within the cell components, the most enriched categories were cellular analytical entity (13,854, $57.0\%$), intracellular (8,597, $35.3\%$), and protein-containing complex (2,568, $10.6\%$). Furthermore, in the molecular function category, the main branches were binding (12,033, $49.5\%$), catalytic activity (11,087, $45.6\%$), and structural molecular activity (1,544, $6.3\%$) (Fig. 3).
Comparing the unigenes of G. rhodantha with the NR database, we annotated 30,452 unigenes, which showed the highest similarity with *Coffea arabica* (4,900, $16.09\%$), C. eugenioides (2,557, $8.40\%$), and C. canephora (1,767, $5.80\%$) (Fig. 4).
TFs can activate the expression of multiple genes in an specific metabolic pathway, which consequently regulate the production of target metabolites (Sun et al., 2018). In the G. rhodantha transcriptome, we divided 1,005 unigenes into 66 TFs families. Previous studies showed that plants mainly have six terpenoid metabolism-related TFs families (C2H2, AP2/ERF, bHLH, MYB, NAC, and bZIP) (Xu et al., 2019). Here, C2H2 transcription factor family members were the most abundant (84, $8.9\%$), followed by AP2/ERF-ERF (65, $6.5\%$), bHLH (57, $5.6\%$), bZIP (54, $5.4\%$), NAC (53, $5.3\%$), and GRAS (51, $5.1\%$) (Fig. 5A).
There were significant differences in TFs in different organs. We divided the 634 root-specific unigenes into 65 TF families. AP2/ERF-ERF TF family members were the most abundant (47, $7.41\%$), followed by C3H (40, $6.31\%$), C2H2 (40, $6.31\%$), MYB-related (37, $5.84\%$), and bHLH (36, $5.68\%$). Furthermore, we divided the 675 stem-specific unigenes into 65 TFs families. Among them, AP2/ERF-ERF TFs family members were the most abundant (52, $7.70\%$), followed by C2H2 (41, $6.07\%$), MYB-related (39, $5.78\%$), C3H (38, $5.63\%$), and WRKY (37, $5.48\%$). In the leaves, we divided 602 unigenes into 64 TF families. The bHLH transcription factor family was the most abundant (41, $6.81\%$), followed by MYB-related (38, $6.31\%$), C3H (37, $6.15\%$), AP2/ERF-ERF (35, $5.81\%$), and C2H2 (34, $5.65\%$). Finally, we categorized 648 flower-specific unigenes into 65 TF families. Here, AP2/ERF-ERF TFs family members were the most abundant (44, $6.79\%$), followed by C2H2 (42, $6.48\%$), bHLH (41, $6.33\%$), C3H (39, $6.02\%$), and MYB-related (37, $5.25\%$) (Fig. 5B).
We further enriched these TF families into KEGG metabolic pathways. Two unigenes encoding the C2H2 family of TFs were enriched in tropane, piperidine, and pyridine alkaloid biosynthesis, whereas six unigenes encoding the zf-HD TF family members were enriched in betalain biosynthesis. Furthermore, three unigenes encoding the trihelix TF family members were enriched in ubiquinone and other terpenoid quinone biosynthesis. Finally, three unigenes encoding C3H TFs family and one encoding bHLH TFs family were enriched in the phylalanine, tyrosine, and tryptophan biosynthetic pathways (Table 4).
**Figure 3:** *GO classification map 24,320 unigenes were classified into three main categories: biological process, cellular component, and molecular function with a total of 43 branches.* **Figure 4:** *Unigenes from G. rhodantha distributed in Nr database.* **Figure 5:** *Distribution of transcription factor families in general and in different organs in G. rhodantha.(A) Over distribution of TFs; (B) Distribution of TFs in different organs.*
## Analysis of KEGG pathways
The KEGG pathways in G. rhodantha can be divided into five categories: cellular processes, environmental information processing, genetic information processing, metabolism, and organic systems, which we mapped to 136 KEGG pathways. The top three metabolic pathways were carbon (763, $6.95\%$), amino acids (510, $4.65\%$), and glycolysis/gluconeogenesis (432, $3.94\%$), with the rest being mainly enriched in pentose and gluconate interconversion along with tarch and sucrose metabolism.
The secondary metabolites contained in higher plants are closely related to their medicinal ingredients. In this regard, we assigned 1,422 unigenes to the 25 secondary metabolic pathways in G. rhodantha. Among them, 172 unigenes encoded key enzymes involved in the terpenoid biosynthesis pathway, including terpenoid backbone (73 unigenes), monoterpenoids (21 unigenes), diterpenoids (38 unigenes), and sesquiterpenoid and triterpenoid (40 unigenes). There were 356 flavonoid biosynthesis-related unigenes, including the phenylpropanoid (253 unigenes), flavonoid (76 unigenes), flavone and flavonol biosynthesis pathways (15 unigenes), and isoflavonoid (12 unigenes). However, only 86 were alkaloid biosynthesis-associated unigenes (Table 5).
## Simple sequence repeat (SSR) analysis
SSR is one of the effective molecular markers for detecting genetic diversity and constructing a genetic map (Liu et al., 2022a; Liu et al., 2022b; Liu et al., 2022c). Six types of SSR were identified via by SSR analysis of unigenes with over 1 KB screened by MISA software: [1] mono-nucleoside repeat SSR, [2] di-nucleoside repeat SSR, [3] tri-nucleoside repeat SSR, [4] tetra-nucleoside repeat SSR, [5] penta-nucleoside repeat SSR, and [6] hexa-nucleoside repeat SSR. Thereafter, we identified a total of 7,388 unigenes, of which the mono-nucleoside and tri-nucleoside repeat SSRs received the most comments, i.e., 4,573 ($61.9\%$) and 1,385 ($18.7\%$), respectively. They were followed by di-nucleoside, tetra-nucleoside, hexa-nucleoside, and penta nucleoside, with 982 ($13.3\%$), 54 ($7\%$), 24 ($3\%$), and 14 ($2\%$), respectively (Fig. 6A).
The identified SSRs were dominated by the A/T single-nucleotide repeats representing ∼$64.29\%$. Furthermore, the AT/TA di-nucleotide repeat type accounted for $9.45\%$ of the total SSR. The tri-nucleotides repeat types, i.e., GGT/GAT and GAA/ATA accounted for $1.62\%$ and $1.47\%$, respectively. However, the penta-nucleotide and hexa-nucleotide repeats had the lowest proportion of $0.02\%$ (Fig. 6B).
## Differential gene expression analysis
*Since* gene expression is spatiotemporal specific, we pairwise compared the transcriptome data from four different organs. In the process of DEGs analysis, we used the Benjamin- Hochberg method to correct the significant row p- value obtained from the original hypothesis test. In the screening process, FDR <0.01 and FC (fold change) ≥ 2 were used as the screening criteria. G. rhodantha is a perennial herbaceous grass used as medicines. Considering their roots are perennial and its aerial parts (stem, leaf, and flower) are annual, we chose root as the control group. When we compared the control group (root) with the experimental group (stem, leaf, and flower), we found significant transcript differences. The number of differential expressions was the highest between the root and flower, whereas it was the lowest between the root and leaves (Fig. 7A). A total of 4,606 transcripts showed organ-specific expression, of which 966, 2,191, 274, and 1,175 transcripts were from the root, stem, leaf, and flower, respectively (Fig. 7B).
The annotation and analysis of metabolic pathways of differentially expressed genes (DEGs) is helpful for further understanding the function of genes. When regarding root as the control group and the stem as the experimental group, 6,156 DEGs were enriched in 129 metabolic pathways, including benzoxazinoid biosynthesis [13], flavoid biosynthesis [39], stilbenoid, dialylheptanoid and ginger biosynthesis [19], circadian rhythm-plant [41], and photosynthesis-antenna proteins [24]. When regarding root as the control group and leaf as the experimental group, 4,534 DEGs were enriched in 129 metabolic pathways, which were significantly enriched in flavone and flavonol biosynthesis [8], porphyrin and chlorophyll metabolism [32], glycosphingolipid biosynthesis-ganglio series [15], glycosaminoglycan degradation [17], and flavor biosynthesis [22]. Finally, when regarding root as the control group and the flower as the experimental group, 6,649 DEGs were enriched in 131 metabolic pathways, which were significantly enriched in cutin, suberine, and wax biosynthesis [22], zeatin biosynthesis [20], cyanoamino acid metabolism [33], flavor biosynthesis [27], and plant hormone signal transduction [165] (Table 6). These DEGs were significantly enriched in flavonoid biosynthesis, which showed that the flavonoid accumulation was more abundant.
**Figure 6:** *Simple sequence repeats (SSRs) in G. rhodantha.(A) Distribution of different types of SSR. (B) Frequency of most abundant SSR motifs. C: Composite repetitive SSR, C*: There are overlapping composite type SSRs, P1: mono-nucleoside repeat SSR, P2: di-nucleoside repeat SSR, P3: tri-nucleoside repeat SSR, P4: tetra-nucleoside repeat SSR, P5: Penta-nucleoside repeat SSR, P6: Hexa-nucleoside repeat SSR.* **Figure 7:** *Differential expression analysis of four organs in G. rhodantha.(A) Number of genes of up-regulated and down-regulate while root compared with other three organs respectively. (B) Venn diagram representing the number of DEGs among all organs. R: root; S: stem; L: leaf; F: flower.*
## Gene expression analysis of unigenes associated with mangiferin, swertiamarin, and loganic acid biosynthetic pathway
Although mangiferin is one of the main active components of G. rhodantha, its biosynthesis has not been included in the KEGG database to date. Therefore, we focused on analysis and identification of the putative genes in the secoiridoid biosynthesis pathway.
The secoiridoid biosynthesis is probably completed using the following steps: intermediate generation, terpene skeleton synthesis, and post-modification (Wu & Liu, 2017; Kang et al., 2021). The analysis results showed that 54 unigenes in the G. rhodantha transcriptome encoding 17 key enzymes in different organs (Table 7) and related genes, which is represented by the heat map.
A wide variety of terpenoids with diverse structures are synthesized from common precursors isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). These compounds can be derived from both the mevalonic acid (MVA) pathway in the cytoplasm and the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway in plastids (Singh, Gahlan & Kumar, 2012). IPP and DMAPP are catalyzed by geranyl pyrophosphate synthase (GPPS) to geranyl diphosphate (GPP), which is an important cut-off point. The GPP flow through different metabolic directions to monoterpenes, diterpenes, triterpenes, etc. For the synthesis of secoiridoid, geraniol is the starting compound. There may be three pathways from GPP to geraniol (Fig. 8A). The pathway involving the transformation of GPP into geraniol and pyrophosphate by the action of geraniol synthase (GES) was fully annotated, while the other two pathways were not fully annotated. Geraniol was converted into iridodial, which was the skeleton of secoiridoids under the excessive step reaction (Miettinen et al., 2014; Lichman et al., 2019). Iridodial is converted into secoiridoid glycoside compounds under a series of modification processes involving the addition of sugar groups, deoxygenation, ring opening, etc (Liu et al., 2017; Rain & Takahashi, 2016).
Based on the statistics of TPM value, we searched for the TPM values of the genes in different organs and then used TBtools(v1.098) to plot the heatmap. For MVA pathway, the AACT2 expression is relatively high in the root, stem, and flower, whereas that of HMGS1 is relatively high in root and flower (Fig. 8B). For the MEP pathway, the relative expression of DXR was higher in the leaf and flower, while that of HDR was higher in the aerial parts (Fig. 8C). In secoiridoid pathway, the relative expression of 8-HGO was also higher in the aerial part (Fig. 8D).
In the gentiopicroside biosynthesis pathway, 8-HGO is particularly important as its structural gene (Wang, 2020). 8-HGO is the key enzyme behind the secoiridoid skeleton construction. Additionally, we verified the relative expression of 8-HGO from the four organs using qRT-PCR. These results showed a similar expression pattern with that of the transcriptome. The relative expression of the 8-HGO gene was higher in the aerial parts than in the root (Fig. 9). Therefore, this suggested that the secoiridoid component was mainly synthesized in aerial part, especially in the leaves.
## Contents of mangiferin, swertiamarin, and loganic acid
To examine the possible relationship between the gene expression and their corresponding metabolites, we determined the content of three bioactive compounds including two secoiridoid pathway metabolites (swertiamarin and loganic acid and mangiferin. After comparing their contents, all three compounds showed similar accumulation patterns in the different organs, with the lowest levels being in the root. Furthermore, swertiamarin was the least abundant in the root and the most abundant in flower (Fig. 10). Therefore, we found that the contents of the three components in the aerial parts were significantly higher than in the root.
**Figure 10:** *The content of mangiferin, swertiamarin, and loganic acid in diûerent tissues.R, root; S, stem; L, leaf; F, flower.*
## Discussion
Secoiridoid is one of the important medicinal components for G. rhodantha (Li et al., 2006; Inao et al., 2004). The analysis of the secoiridoid biosynthesis pathway is very important for quality improvement and breeding for G. rhodantha. First, we sequenced the transcriptome of different organs of G. rhodantha. When comparing with other plants of the same genus, the results showed that sequence splicing quality of G. rhodantha was relatively high (47,871 unigenes obtained, average length 1,107.38 bp), as compared with G. rigescens (76,717 unigenes obtained, average length 753 bp) (Zhang et al., 2015), G. crassicaulis (159,534 unigenes obtained, average length 679 bp) (Kang et al., 2021), G. waltonii (79,455 unigenes obtained, average length 834 bp) (Ni et al., 2019).
There were 30,452 unigenes annotated in the Nr database for G. rhodantha, with the results showing the highest similarity to C. arabica from the Rubiaceae family. This may be due to the limited data about Gentianaceae. Based on the results of the DEGs of G. rhodantha, we found more metabolic pathways in the root with respect to flower than in the other contrast pairs (root with respect to stem, root with respect to leaf). The DEGs were mainly enriched in phytohormone signaling, flavonoid biosynthetic pathways, and cyanogenic amino acid metabolism. Higher plants contain diverse secondary metabolites, which are closely related to their medicinal effects. Additionally, mangiferin, swertiamarin, and loganic acid showed similar accumulation patterns in the different organs, with the lowest levels seen in the root. This may be because the accumulation of the extremely bitter tasting swertiamarin in the aerial parts, especially in the flower, could effectively defend against predator aggression. Mangiferin showed the highest accumulation in all the organs, especially in the leaf, which was consistent with the previous studies (Zhang et al., 2007).
The synthesis of secondary metabolites is a complex multi-step process (Wu et al., 2022). KEGG analysis showed that 1,422 unigenes were involved in 25 secondary metabolic pathways, including isoquinoline alkaloid biosynthesis (ko00905), dieterpenoid biosynthesis (ko00904), and sesquiterpenoid and triterpenoid biosynthesis (ko00909). In this study, we selected 8-HGO from the secoiridoid biosynthesis pathway for qRT-PCR validation experiments, and the results were consistent with the expected results. The low content of these three components in the root showed a correlation with the relatively low expression of 8-HGO, thereby confirming the presence of 8-HGO in the secoiridoid biosynthetic pathway. This was consistent with previous reports about G. rhodantha and those of other species of Gentianaceae, like *Swertia mussotii* (Shen et al., 2016; Liu et al., 2017). Based on above research, it can be inferred that this active composition is mainly synthesized in the aerial part, with the medicinal value of the aerial part being higher than the root. Moveover, the biomass of the roots is very small. When used as a medicinal material, we recommend that the medicinal part should be changed to the aerial part for better protection of the resources and the maintenance of sustainable use.
TFs can control gene expression by specifically binding with the cis-regulatory elements in the promoter region of target genes, and play a key regulatory role in the plant growth and development (Latchman, 1997). Currently, hundreds of TF families have been isolated and identified from higher plants, which are closely related to plant stress resistance, and can regulate the expression of genes related to different plant stressors, like drought, high salt, low temperature, and pathogens (Gibbs et al., 2015; Verma, Ravindran & Kumar, 2016). The number of genes encoding different TFs families varies in different plant species, and they often have species-tissue-specific or developmental stage-specific function(s) (Singh, 1998). A total of 1,005 unigenes were involved in encoding 66 TF families for G. rhodantha. The members of the TF families did not exhibit any significant organ-specificity. Among all the TFs, the C2H2 transcription factor family was the most abundant (89, $8.9\%$). C2H2 zinc finger proteins play roles in the plant response to a diverse stresses, including low temperatures, salt, drought, oxidative stress, excessive light, and silique shattering (Kim et al., 2013; Yue et al., 2016; Jiang et al., 2022). C2H2 may also be important in the synthesis of secondary metabolites involved in the stress resistance of G. rhodantha. It can be considered for the study of transcription factors regulating the quality of G. rhodantha. Furthermore, 141 unigenes were annotated as WRKY family transcription factors, of which 17 showed higher expressions in the leaf than in the root. WRKY may be a good candidate for studying the secoiridoid biosynthesis regulation in G. rigescens (Zhang et al., 2015). The identification of these transcription factors will be helpful to further analyze the molecular mechanism of secoiridoid biosynthesis and lay a foundation for regulating the secoiridoid metabolite accumulation. Therefore, these findings are highly significant as they provide a reference for mining the key genes of the biosynthesis pathway of secondary metabolites.
As far as the reported transcriptome analysis of G. rhodantha is concerned, it is only the initial stage, and more extensive research is necessary. Furthermore, the expression of genes related to secondary metabolites is also affected by many factors, including plant growth and developmental stage and the ecological environment. Since we had only studied the flowering period, therefore, we could not capture all the gene expression-related information.
Simple repeats, also known as short tandem repeats, are 1–6 nt long DNA sequences widely distributed in the eukaryotic genomes. Six nucleotides form repetitive motifs in different orders. Since the motifs are repeated several times, so they have repeatability, polymorphism, richness, and co-dominance (Chen et al., 2012; Shi et al., 2016). We found a total of 7388 SSR loci in the transcriptome of G. rhodantha, including various nucleotide types, thereby indicating that the SSR loci of G. rhodantha was rich and abundant. The number of single-nucleotides repeats of A/T is the largest [4,573], which was consistent with the previous results of Gardenia jasminoide (Liu et al., 2022a; Liu et al., 2022b; Liu et al., 2022c). Unfortunately, the SSR data for other species of the *Gentiana genus* have not been reported yet. Therefore, our study presents information for the further development of SSR molecular markers and the DNA ID code construction of G. rhodantha.
## Conclusion
In this study, we obtained the transcriptome data of G. rhodantha by high-throughput sequencing for the first time, and then determined the genes and DEGs involved in the secoiridoid biosynthesis pathway. Using qRT-PCR, we further verified the RNA-seq analysis results for one key enzyme gene related to secoiridiod biosynthesis. Therefore, our findings provide timely clues for a better understanding of the molecular mechanism of secoiridoid biosynthesis in G. rhodantha.
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|
---
title: 'Oral care considerations for people with cystic fibrosis: a cross-sectional
qualitative study'
authors:
- Niamh Coffey
- Fiona O’ Leary
- Francis Burke
- Barry Plant
- Anthony Roberts
- Martina Hayes
journal: BDJ Open
year: 2023
pmcid: PMC10008013
doi: 10.1038/s41405-023-00136-w
license: CC BY 4.0
---
# Oral care considerations for people with cystic fibrosis: a cross-sectional qualitative study
## Abstract
### Objectives
To investigate the attitudes of adults with Cystic Fibrosis (CF) towards dental attendance and any perceived barriers to treatment.
### Methods
A cross sectional survey in the form of a structured, anonymous questionnaire was used to obtain information regarding adults with CF’s feelings towards dentists and dental treatment. The final version of the questionnaire was based on a collaborative effort between researchers at Cork University Dental School and Hospital and Cystic Fibrosis (CF) patient advocates from CF Ireland. Participants were recruited via CF Ireland’s mailing list and social media channels. The responses underwent descriptive statistical analysis and inductive thematic analysis.
### Results
A total of 71 people (33 Male: 38 Female) over the age of 18 living with CF in the Republic of Ireland responded to the survey. $54.9\%$ of respondents were unhappy with their teeth. $63.4\%$ felt that CF had an impact on oral health. $33.8\%$ were anxious about attending their dentist. Respondents believed that CF has impacted on their oral health due to the medications and dietary requirements involved, as well as tiredness and other side effects of CF. Reasons for being anxious about attending the dentist included cross infection concerns, issues with the dentist, with tolerating treatment, and with the teeth themselves. Respondents wanted dentists to be aware of the practicalities of dental treatment for people with CF, especially their discomfort with lying back. They also want the dentist to be aware of the impact that their medication, treatment and diet has on their oral health.
### Conclusions
Over one third of adults with CF reported anxiety about attending the dentist. Reasons for this included fear, embarrassment, cross infection concerns and problems with treatment, especially being in the supine position. Adults with CF want dentists to be aware of the impact that CF can have upon dental treatment and oral health care.
## Introduction
Cystic Fibrosis (CF) is an autosomal recessive genetic condition, resulting from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene on chromosome 7 [1] Its dysfunction results in abnormal transport of Chloride and Bicarbonate ions, leading to thick viscous secretions in multiple organ systems. This can result in diabetes, osteoporosis, liver disease, chronic kidney disease, and gastro-intestinal issues, as well as serious respiratory problems, with progressive bronchiectasis and recurrent infective pulmonary exacerbations being the hallmark of CF lung disease [2]. Lung transplantation is recommended for individuals with advanced lung disease [3]. Due to earlier diagnosis and improved therapies, the life expectancy of people with CF (PWCF) has greatly improved, from a median survival age of 11 years in 1978 to between 44 and 53 years in 2019 [4]. Due to the fact that CF has changed from an almost exclusively paediatric disease to a disease of adulthood, the transition to adult care is now an important aspect of CF patient management [5]. The medical care of PWCF is complex, with treatment aiming to control the symptoms, reduce complications and reduce the severity of different manifestations of CF. Oral care considerations include the long-term use of antibiotics (which may be inhaled or nebulised), bisphosphonates for osteoporosis, presence of CF related diabetes, gastro-oesophageal reflux disorder, and malnutrition which may be treated with Oral Nutritional Supplements with a high sugar content [6–10].
The oral health status of individuals with CF is not yet fully understood. A 2020 systematic review showed that the majority of studies show better oral hygiene, with lower levels of gingivitis and plaque among PWCF than controls [11], with fewer studies showing increased gingivitis and higher levels of plaque and calculus. It is thought that the long-term use of broad-spectrum antibiotics may have a preventative effect against the development of periodontal disease [12].
A number of studies have investigated the link between CF and Developmental Defects of Enamel (DDEs) and the majority of these report an increased level of DDEs in the CF population [13–18]. It is hypothesised that this may be due to metabolic or nutritional disturbances, long term antibiotic use and pancreatic enzyme use [14, 18]. Interestingly, Abu-Zahra et al. also found that the severity of enamel defects increased with the number of surgeries the patient had undergone [15].
There has been significant research into the relationship between CF and dental caries experience, however, no clear consensus can be formed. Despite the fact that people with CF have been hypothesised to be more at risk of caries due to a high-calorie diet, sugar containing oral nutritional supplements, gastro-oesophageal reflux disorder (GORD), increased levels of streptococcus mutans and DDEs [14, 19], the majority of studies found that there was a lower caries experience in people with CF [12, 15–18, 20–23]. However, some studies found that caries experience in people with CF increased with age [24, 25], and, furthermore, studies that included adults, or were limited to adult participants found a higher caries experience in study groups [13, 26, 27].
Studies have shown that people with special medical care needs may be less likely to access dental care [28] and a recent report has shown that people with Cystic Fibrosis may attend dental practices less frequently than is recommended [29].
There has been no prior research into the attitudes of adults with CF regarding dental attendance. This study was undertaken to investigate concerns adults with CF may have regarding dental attendance and dental treatment, and to identify methods to improve service provision for these individuals.
## Aims
This study seeks to investigate the attitudes of adults with CF towards dental attendance and any perceived barriers to treatment.
## Study design
A cross sectional survey was carried out in accordance with the World Medical Association Declaration of Helsinki and received Ethical Approval from the Clinical Research Ethics Committee of the Cork Teaching Hospitals (ECM $\frac{03}{2022}$ PUB).
The inclusion criteria were people over the age of 18 years with a diagnosis of CF. The exclusion criteria were people without Cystic Fibrosis, or people with Cystic Fibrosis under the age of 18. All respondents gave written consent to take part, declaring that they understood they were under no obligation to complete the questionnaire and that they consented to data collection. Participants were recruited via Cystic Fibrosis Ireland’s mailing list and social media channels.
## Questionnaire
A structured, anonymous online questionnaire, with open-ended questions, was used to obtain information regarding their feelings towards dentists and dental appointments. Patient involvement was sought from patient advocates from CF Ireland, with whom the questionnaire was trialed, and amendments made based on their feedback. The final version of the questionnaire was based on a collaborative effort between researchers at Cork University Dental School and Hospital and CF patient advocates from CF Ireland. Data was collected from May to August 2020. A copy of the questionnaire is included in Appendix I.
## Data analysis
Descriptive statistical analysis of the quantitative questions was completed using IBM SPSS (v26; SPSS Inc., Chicago, IL, USA). The qualitative portion underwent thematic analysis as described by Braun and Clarke [30] [2006]. The analysis was inductive in that there is no previous study in this area and, therefore, the data collected determined the themes generated. Selected quotations and related themes can be seen in Appendices II-IV.
## Results
A total of 71 adults with CF responded to the survey.
The quantitative responses and descriptive statistical analysis are summarised in Table 1.Table 1Respondent Profile. VariableCategoryN%GenderMale3346.5Female3853.5Level of education completedPrimary Level34.2Secondary Level1723.9Third Level4867.6EmploymentFull-/part- time employment/self-employed4360.6Unemployed2535.2Prefer not to say34.2Natural teeth20 or more6490.110-1945.6Don’t know34.2DentureWears denture00No Denture71100Are you happy regarding the appearance of your teeth?Yes3041.3No3954.9Declined to answer22.8Dental attendanceRegular attender4259.1Irregular attender2940.8Reason for last dental visitRoutine check-up/consultation3954.9Pain/trouble with teeth gums or mouth2129.6Treatment811.3Do you think that CF has impacted your oral health?Yes4563.4No1318.3Don’t know/Declined to answer1318.3Anxious/worried about attending dentist?Yes2433.8No4563.4Don’t Know22.8Do you make your dental practice aware of your CF status?Yes2535.2No1926.8Dental practice already aware2738 The majority of respondents had a high level of education ($67.6\%$ having 3rd level education) and were employed or self-employed ($60.6\%$). They retained the majority of their teeth and no respondent had a denture. Despite this, the majority of patients ($54.9\%$) were unhappy with the appearance of their teeth. $40.8\%$ of people were irregular attenders and nearly $30\%$ said the reason for their last dental visit was “pain or trouble with teeth, gums or mouth”. The majority ($63.4\%$) of patients felt that CF had an impact on their oral health. $33.8\%$ were anxious about attending the dentist. $25.2\%$ of respondents made the dental practice aware of their CF status before attending, with a further $38\%$ reporting that the dental practice was already aware.
When asked if they think dentists should be part of the multidisciplinary team, $54.9\%$ said “Yes”, $32.4\%$ said “Maybe” and only $12.7\%$ said “No”.
## Qualitative data
The following questions underwent thematic analysis:Do you believe that CF has impacted on your oral health in any way?Are you anxious/worried about going to the dentist? If yes, why?What do you think is important for the dentist to know about CF?
## Question 1:
Do you believe that CF has impacted on your oral health in any way?
The majority of respondents ($63.4\%$) believed that CF did have an impact on their oral health. The major themes that emerged from this question, in decreasing frequency was:Impact of medications (especially antibiotics)TirednessDietSide effects of CF The main CF-related factor that respondents mentioned was the impact of medications- concerns ranged from nausea/vomiting to discolouration, to dry mouth. Many blamed antibiotics in particular as the cause for “staining”, “lost enamel” and “weakened teeth”.
Tiredness/lack of energy also had an impact on their oral health. Many mentioned being “too tired to brush”, “no energy to brush”, with one respondent noting “When I’ve been very sick it’s hard to get the energy to brush your teeth, I remember having to ask my mum to brush them for me”.
Another area where CF impacted oral health was diet. Many respondents noted an increase in amount of sugar needed: “to get calories in”, “just over eating sweet things when sick and no real appetite for large meals”, and being “addicted to sugar”. One respondent noted that they had an increased need for sugary food since developing CF related diabetes (CFRD) “Since developing CFRD, I eat a lot more jellies and drink orange juice now to treat lows [hypoglycaemic episodes].
Other systemic effects of CF also had an impact on their dental health, such as “tummy issues causing bad breath and erosion” and missing dental appointments due to being unwell.
## Question 2:
Why are you anxious/worried about going to the dentist?
Five major themes emerged from this question, in descending order of frequency, these were:Issues with the dentistCross-infection concernsProblems with treatmentProblems with teethCF related problems.
Dentist-related problems included fear of dentists themselves, fear of being judged by the dentist, with some respondents noting “I have previously been treated with lack of understanding from dentists/hygienists” and ““I am always in trouble when I attend”.
Another aspect of dental visits that concerned adults with CF was cross-infection control, with many respondents mentioned increased anxiety since the Covid-19 pandemic “I did not feel this way before but I do now in light of Covid-19”. Some respondents also mentioned concern at being so “close to the sink at dentist chair”. This is due to the perception that there may be stagnant water or saliva in the sink [spittoon] which could pose a cross-infection risk to the individual.
A practical issue that caused anxiety in adults with CF was dental treatment, with many expressing difficulty in lying in the supine position that is usually employed during a dental examination- “I find it difficult to be in a flat position as it affects my breathing. It’s also very difficult not to cough”. Some respondents reported dental problems causing anxiety surrounding dental appointments: “My teeth are all wearing away at the bottom the past few years and I feel my teeth are going to break soon”, “I just feel embarrassed about my teeth”.
CF itself had an impact on people’s anxiety regarding dental attendance, with one individual noting that they “Feel sick enough without there being another thing wrong with my teeth”.
## Question 3:
What do you think is important for the dentist to know about CF?
Four major themes emerged from this question. In descending order of frequency, they were:Practicalities of dental treatmentImpact of medication/treatment/dietImpact of CF itselfCleanliness of surgery/cross-infection control.
The main concerns adults with CF had were the practicalities of dental treatment, namely keeping the patient upright and allowing them to have breaks during treatment. The vast majority of respondents said some variant of this, e.g., “it is often difficult for people with CF to lie down and not cough for long periods of time”, “would have liked option to sit more upright during prolonged treatment, felt this was where I was exposed to risk of aspiration”, “I can’t lie flat or sometimes breathe through my nose quick enough”. On a positive note, some respondents noted that their dentist/hygienist already accommodates this: “My dentist is very good and tilts chair upright and also pauses when I indicate I need to cough. It slows process but he is very understanding and allows time”, “Extra breaks may be needed, my dentist/hygienist are good when it come to that!”.
Another issue that adults with CF wanted dentists to be aware of is the impact that medication, treatment and diet can have on their oral health. A concern is that dentists are not aware or not understanding of the effects that CF-related medication and diet has on their oral health. They want dentists to be aware of “the full extent of the treatment required that may impact the teeth, gums and tongue”, “the effect different meds have on oral hygiene”, “before lecture on oral hygiene, sweets etc.- CF patients need [them] to maintain weight”. Another overlapping theme that emerged was for the dentists to be aware of CF itself and the impact that has on the individual: “the impact it [CF] has on our health and mental health and how that could relate to our dental hygiene/care/condition”, “About CF in general and how CF has impacted our oral health”.
The final theme that emerged and can be linked to dental anxiety as outlined in the previous section, is for dentists to be aware that adults with CF may be anxious about cross-infection risks. Respondents mentioned aspects such as “cross-infection risks, especially infection risk from pseudomonas”, “clean lines”, “make sure all equipment is sterile for treatment”.
## Discussion
Adults with CF can feel anxious regarding dental care which may result in irregular attendance and higher risk of dental disease. The most common concerns described by respondents of this study include dental anxiety, cross-infection concerns, and difficulty with the practicalities of treatment, for example, laying back in the dental chair.
$33.8\%$ of respondents said they were anxious regarding attending the dentist. This is a much higher proportion than the estimated $4\%$ to $20\%$ of people in the general population in developed countries who suffer from dental anxiety [31, 32]. It is also a higher proportion than a 1998 sample of people with special health care needs where $27.9\%$ reported “fear/anxiety about dental visits” Ideally, levels of dental anxiety amongst the population would be decreasing year on year due to new therapies etc, therefore, this is a major concern. It is well established that dental anxiety is one of the most important barriers to dental care [33, 34]. Therefore, this study is important because it enables us to gain better understanding of what is causing the high levels of anxiety in adults with CF.
Examining the major themes regarding this anxiety, it can be seen there are dental-related concerns (the dentist themselves and treatment concerns) and patient-related concerns (problems with teeth and CF as a condition). Clinicians should seek to rectify the concerns over which they have control, i.e. being judgmental regarding patient’s teeth or need to stop for breaks. These authors recommend allocating extra time to patients with CF to enable clinicians to have an in-depth discussion regarding the patients’ previous medical and dental history, and concerns they may have regarding their teeth or regarding dental treatment. Good communication skills, and the feeling that patients are being listened to can result in a marked improvement in patient satisfaction and healthcare outcomes [35], therefore spending extra time to fully understand the CF patient’s medical background should help to improve their perception of dentists and dental treatment, and to alleviate their anxiety.
This study shows that adults with CF can themselves be concerned regarding the appearance of their teeth, and many believe the medication and diet related to CF has had a negative effect on their oral health. They have raised concerns about dentists’ lack of understanding on the condition and its management, and therefore, may be anxious about attending a dentist due to fear of being adversely judged. Adults with CF want dentists to understand that due to increased dietary needs, periods of ill-health and polypharmacy, they may have increased risk of oral disease. Therefore, dentists practising in areas with a relatively high proportion of PWCF, such as Ireland and the U.K., should educate themselves regarding the comorbidities and treatment modalities involved in this condition. More recommendations are given below.
One of the major points repeated was the need to sit the patient in an upright position due to a frequent need to cough, this is due to mucus build up or reflux, and is reported by almost every person with CF [36, 37]. Therefore, sitting the patient in an upright position during treatment should be accommodated if possible.
Dentists should also alleviate the patient’s concern regarding cross-infection control, by reassuring adults with CF that the strictest standards of cross-infection control are being adhered to.
A large number of respondents mentioned Covid-19 specifically as an area of concern, this is in-keeping with recent studies which showed that even in people without CF, “those with high Covid-19 fear were at least six times more likely to not visit the dentist” [38]. Similar to the previous comments about cross-infection control, it may be beneficial to allay PWCF’s fears regarding this by, for example, carrying out a Covid-19 test on any staff that may be in contact with the individual, wearing of FFP2 masks or similar.
This study is important as it provides the first insight into the attitudes of adults with CF towards dental treatment and gives recommendations for improving provision of care to these patients. There is a lot of overlap between what adults with CF want dentists to know, and what causes dental anxiety in them. Therefore, if dental practitioners are educated about CF and its repercussions and make small amendments to their practice based on these, it should make the dental experience less anxiety-inducing for adults with CF.
## Strengths and limitations
A limitation is the relatively small sample size. It should be noted there are 746 adults with CF in Ireland as of 2019 [39]; therefore, a response from approximately $9.5\%$ of that population was obtained. As with any survey, there is a risk of sample selection bias, in that people who are anxious about attending the dentist may be more likely to self-select for the survey. Another limitation is the high level of education ($67.6\%$ completing third level) and of employment ($60.6\%$) reported by the respondents. This is higher than the level of education ($49\%$ completing third level) and of employment ($54\%$) seen in other Irish PWCF, as outlined in CF Ireland’s Independent Living report 2017 [40], and so may not be fully representative of the CF population as a whole. In future studies, it may be beneficial to collect data regarding the age of respondents and investigate if there is any age-related difference in dental concerns.
A major strength of this study is that it is the first study into the attitudes of adults with CF regarding dental treatment and should serve to guide dentists and other dental care providers in their management and treatment of this vulnerable cohort. This is the first opportunity adults with CF were given in order to voice their concerns or anxieties regarding dental treatment and it would be beneficial to compare it to other (non-Irish) CF populations to see if they have similar concerns.
Another strength is that the questionnaire was anonymous, therefore respondents were more likely to be honest with how they perceived the dentist and dental appointments. The fact that we employed the use of PPI (Patient and Public involvement) was a major benefit as a 2014 systematic review shows PPI has a a positive impact on all research/stages [41].
## Conclusions
A third of adults with CF reported anxiety about attending the dentist. Reasons for this included fear, embarrassment, cross-infection concerns and problems with treatment, especially being in the supine position. Dentists and dental care professionals should be aware of the impact that CF can have upon dental treatment and oral health care.
## Recommendations
Medical complexities faced by people with CF such as frequent and long-term antibiotic use, specific dietary requirements and periods of illness may lead to difficulty in maintaining meticulous oral hygiene. Dentists should be understanding of this and not chastise patients regarding their dental condition but instead give recommendations regarding how to improve this.
A thorough medical and social history should be carried out in order to improve patient care. Be aware that people with CF may not think to disclose the consumption of Oral Nutritional Supplements (ONS) in their medical history, and so should be asked about this separately. Preventative advice such as fluoride varnish application, fissure sealants or a fluoride mouth rinse may be advised if a patient is deemed at high risk of caries development, due to sugar-containing medicines, ONS, high calorie diets, presence of DDEs etc. Patients with CF-related diabetes should be warned re the potential risk of periodontal disease. The majority of PWCF take some form of inhaled medication and they should be advised to rinse out after use.
It goes without saying that the dental surgery and all instruments used must undergo the strictest levels of cross-infection control, however the patient with CF should be reassured that all standards are being met.
Dentists should endeavour to make the CF patient’s experience more comfortable by sitting the patient upright if possible, to facilitate mucus clearance. It may also be necessary to take a number of breaks in order to allow the patient to expectorate.
## Supplementary information
Appendices 1-4 The online version contains supplementary material available at 10.1038/s41405-023-00136-w.
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|
---
title: 'Eating disorders during lockdown: the transcultural influence on eating and
mood disturbances in Ibero-Brazilian population'
authors:
- Isabel Baenas
- Carmem Beatriz Neufeld
- Rita Ramos
- Lucero Munguía
- Rosane P. Pessa
- Tânia Rodrigues
- Susana Jiménez-Murcia
- Sónia Gonçalves
- Marília C. Teodoro
- Ana Pinto-Bastos
- Nazaré O. Almeida
- Roser Granero
- Mikel Etxandi
- Shauana R. S. Soares
- Fernando Fernández-Aranda
- Paulo P. P. Machado
journal: Journal of Eating Disorders
year: 2023
pmcid: PMC10008014
doi: 10.1186/s40337-023-00762-7
license: CC BY 4.0
---
# Eating disorders during lockdown: the transcultural influence on eating and mood disturbances in Ibero-Brazilian population
## Abstract
Adverse consequences on mental health derived from COVID-19 pandemic and lockdown particularly affected people with mental illness, including eating disorders (EDs), being the involvement of socio-cultural features poorly studied. We aimed to assess eating and mood changes in EDs during lockdown regarding ED subtypes, age, provenance, and considering socio-cultural aspects. 264 females with EDs linked to specialized ED units in Brazil, Portugal, and Spain were evaluated using the COVID-19 Isolation Eating Scale (CIES). A global impairment in mood symptoms and emotion regulation was reported regardless ED subtype, age, and country. Spanish and Portuguese individuals seemed more resilient than Brazilian ones, who reported a more adverse socio-cultural context. A global trend to eating symptoms worsening was observed, regardless of the ED subtype, age, and country, but without statistical significance. Patients with anorexia nervosa and binge eating disorder (BED) described eating style worsening. Moreover, the BED group significantly increased weight and body mass index, similarly to bulimia nervosa, and in contrast to the other subtypes. In sum, a psychopathological impairment was observed in EDs during lockdown, suggesting that socio-cultural aspects could be considered as potential modulatory factors. Nevertheless, these are preliminary results, being longitudinal studies and long-term follow-ups still needed. This work also highlights the importance of more personalized therapeutic approaches.
### Background
COVID-19 pandemic has implied exceptional restrictive measures to contain its widespread, with adverse consequences on mental health, especially for those people with a background of mental illness, such as eating disorders (EDs). In this population, the influence of socio-cultural aspects on mental health has been still underexplored. Then, the main aim of this study was to assess changes in eating and general psychopathology in people with EDs during lockdown regarding the ED subtype, age, and provenance, and considering socio-cultural aspects (e.g., socioeconomical factors such as work and financial losses, social support, restrictive measures, or health accessibility, among others).
### Methods
The clinical sample was composed of 264 female participants with EDs (74 anorexia nervosa (AN), 44 bulimia nervosa (BN), 81 binge eating disorder (BED), and 65 other specified feeding and eating disorder (OSFED)), with a mean age of 33.49 years old (SD = 12.54), from specialized ED units in Brazil, Portugal, and Spain. The participants were evaluated using the COVID-19 Isolation Eating Scale (CIES).
### Results
A global impairment in mood symptoms and emotion regulation was reported in all the ED subtypes, groups of age, and countries. Spanish and Portuguese individuals seemed more resilient than Brazilian ones ($p \leq .05$), who reported a more adverse socio-cultural context (i.e., physical health, socio-familial, occupational, and economic status) ($p \leq .001$). A global trend to eating symptoms worsening during lockdown was observed, regardless of the ED subtype, group of age, and country, but without reaching statistical significance. However, the AN and BED groups described the highest worsening of the eating habits during lockdown. Moreover, individuals with BED significantly increased their weight and body mass index, similarly to BN, and in contrast to the AN and OSFED groups. Finally, we failed to find significant differences between groups of age although the younger group described a significant worsening of the eating symptoms during lockdown.
### Conclusions
This study reports a psychopathological impairment in patients with EDs during lockdown, being socio-cultural aspects potential modulatory factors. Individualized approaches to detect special vulnerable groups and long-term follow-ups are still needed.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40337-023-00762-7.
The online version contains supplementary material available at 10.1186/s40337-023-00762-7.
## Introduction
From the end of 2019, the widespread of COVID-19 infection has categorized it as a pandemic [1]. The implementation of different restrictive measures has limited mobility and favored social isolation at home, with a global impact on individuals’ lifestyle on both physical and mental health [2]. In this line, different authors have reported a predominant negative impact of confinement on eating patterns, physical activity, and emotional well-being in general population, including countries from the same geographic area (i.e., Spain and Portugal) or with shared socio-cultural aspects, such as speaking language and historical background (i.e., Portugal and Brazil) [2–4].
Lockdown in Portugal was instituted between 19th March and 3rd May 2020 [5], similarly to Spain, where the state of alarm was decreed from 14th March to 21th June (https://www.boe.es/eli/es/rd/$\frac{2020}{03}$/$\frac{14}{463}$). Brazil did not establish neither an official lockdown nor national restrictive measures, which were unequally applied by local governors. As a result, Brazil was considered in the top five countries with a high number of contagious people (https://www.worldometers.info/coronavirus/). Even to date, high differences are found between the three countries. Brazil has 34,470,776 of total cases, and 684,369 deaths, while Spain has a total of 13,352,019 cases and 112,804 deaths, and Portugal 5,429,340 cases and 24,886 deaths (https://covid19.who.int/). Then, this differential lockdown context may influence the impact of the pandemic on the population in each country, together with other idiosyncratic aspects such as the amount and structure of the population, the economic situation of each country, the type of health system, and accessibility to assistance, among others, contributing to perceived emotional distress [2].
Worldwide, people with a background of mental illness were considered as an especially vulnerable group during the pandemic and lockdown [6], including individuals with eating disorders (EDs) [7]. Particularly among them, changes in eating psychopathology and psychological state have been reported [7, 8], being greater than in healthy controls [9], with a tendency to worsen eating symptoms [10].
In Portuguese clinical population, Machado et al. [ 11], evaluated the impact of the lockdown in 43 adult individuals ($95.3\%$ females) with EDs through a self-reported survey: 20 with anorexia nervosa (AN), 14 with bulimia nervosa (BN), two with binge eating disorder (BED), and seven with other specified feeding and eating disorders (OSFED). Notably, $31\%$ of the sample reported an increased weight during lockdown. Besides, a significant increase of body mass index (BMI) in the total sample was stated after lockdown. Most participants, whether in treatment or not during lockdown, described significant changes in their lifestyle, including physical exercise and eating habits, as well as stress linked to the pandemic situation. A higher impact of lockdown was significantly associated with the presence of eating and general psychopathology, but also with impulsivity and difficulties in emotion regulation (ER). Indeed, the ER difficulties mediated the impact of lockdown on the global clinical impairment among these patients. In this vein, emotion dysregulation and a lack of adaptive coping strategies, together with some personality traits (e.g., low self-directedness) have been considered as vulnerability factors leading to psychological distress during lockdown, which might also be associated with disturbed eating patterns not only in the general population [3], but also in individuals with EDs [12, 13].
Previous literature in Spanish clinical population has suggested that the effects of lockdown were different depending on the ED subtypes [14], being individuals with OSFED those who reported the highest global impairment in eating psychopathology [14]. There are few Brazilian studies that evaluated eating patterns during lockdown, without referring to the clinical population with EDs. In the non-clinical population, we found some interesting results. For instance, an increase of the consumption of bakery and processed foods was also accompanied by an inverted eating pattern, with a decrease in food consumption in the morning and an increase at night [15]. Another study that considered adults with Diabetes Type I and II reported that $75.8\%$ of the individuals had eating psychopathology in the context of lockdown [16].
Overall, these results were consistent with research hypotheses that pointed out that the special circumstances of the lockdown may have favored not only an eating style worsening, but also have contributed to exacerbate unhealthy eating patterns among patients with EDs, such as overeating, or even precipitate an ED onset [17]. In this regard, younger age has been highlighted as a possible vulnerability factor related to the negative impact on mental health due to pandemic and lockdown [18] although this aspect was underexplored among people who already had an ED [19]. Moreover, the lockdown and restrictive measures have involved changes in treatment approaches, including the closure of day hospitals and outpatient facilities, or the adaptation to telehealth, which may also have contributed to a higher emotional distress (e.g., anxiety, mood disturbances) [7] and, therefore, to a negative effect in eating symptoms [11].
Early research in individuals with EDs has shown a worsening of their psychological state with anxiety, stress, and increased worries about the risk of being infected with COVID-19 and other negative consequences of the lockdown (e.g., relatives’ infection, employment) [7, 12]. Curiously, mixed differences were found according to the ED subtype. Some studies stated that patients with AN had experienced the highest psychological distress [20]. Other works found that again individuals with OSFED reported the highest rates of anxiety and depressive symptoms after lockdown [14], reinforcing the idea that a worse psychological state could influence eating symptoms [14].
Considering that the heterogeneity of previous results may be influenced by the lack of homogenized psychometric instruments to evaluate changes in eating and mood psychopathology in the context of pandemic and lockdown, the COVID Isolation Eating Scale (CIES) was developed by the Psychoneurobiology of Eating and Addictive Disorders Spanish research group [14]. The scale has been validated and translated into nineteen languages [14]. An international and multicentric group of experts from different ED units used it to measure eating and general psychopathological changes during confinement in individuals with EDs [21]. The authors pointed out that while individuals with OSFED indeed were those who reported a worse psychological state during lockdown, the highest impact on weight and eating symptoms was associated with BED, in comparison with other EDs. Interestingly, differences according to cultural context and age were also reported, concluding that Asian and younger individuals appeared to be more resilient than European and adults with EDs, respectively [21]. However, no clinical groups from South America were included in the study.
To the best of our knowledge, this is the first work aimed to explore eating and mood state during lockdown in a clinical sample with EDs from the Ibero-Brazilian community regarding ED subtype, age, provenance, and the socio-cultural context. On the one hand, we analyzed whether there were intra-group pre-post changes in eating and general psychopathology within each ED subtype, group of age, and country. On the other hand, we performed between-group comparisons, according to ED subtypes, groups of age, and provenance. In this study, we used a validated instrument such as CIES, which allowed us to assess eating symptoms and style, anxiety, and depressive symptoms, as well as ER strategies. Moreover, we explored the socio-cultural context (e.g., work status and financial losses, social support, restrictive measures, health accessibility, telehealth implementation, among others), as some of these features have been recognized as potential contributing factors of emotional distress and a worse psychological state [22–24]. Bearing in mind results from previous literature, we hypothesized a global intra-group trend to worsening of eating and mood symptoms in the context of lockdown. Going one step further, we suggested the presence of significant differences in eating and mood changes in between-groups comparisons, with a particular influence of the socio-cultural features in the contextualization of the differences between countries.
## Participants
This cross-sectional study was composed by a sample of $$n = 264$$ participants with a mean age of 33.49 years old (SD = 12.54) from private and public ED units in Brazil ($$n = 101$$), Portugal ($$n = 28$$) and Spain ($$n = 135$$). Age range in the study was between 14 and 70 years old. In the study, two groups of age were considered: adolescents and young adulthood (younger than 25 years-old) versus adulthood (25 years old and older). The choice of this cut off point is based in most psychological studies that consider that adolescence now runs up until the age of 25 for the aims of analyzing and treating young people behaviors [25]. As inclusion criteria, all the participants were females diagnosed with an ED, according to Diagnostic and Statistical Manual of Mental Disorders, fifth edition, (DSM-5) criteria [26], by expert clinical psychologists and psychiatrists. All the participants included in this study fully completed the assessments.
## Contextual information
The Iberian countries are integrated by Spain and Portugal. Lockdown in Portugal was instituted between 19th March and 3rd May 2020 [5] and between 14th March and 11th May 2020 in Spain (although the state of alarm was extended until 21th June). In Brazil, around the 27th March 2020 some of the local state governors imposed quarantine although more precise data on the duration of this period is not available.
## Assessment
The COVID Isolation Eating Scale (CIES) is a self-report questionnaire that evaluates the impact of confinement on patients with EDs [14]. It is composed by four subscales: I, referred to COVID-19 pandemic personal circumstances (8 items); II, related to eating psychopathology during confinement (13 items), together with the presence of other psychiatric comorbidities and diabetes mellitus; III (34 items), regarding eating style, general psychopathology, and ER; and IV (13 items), associated with the evaluation of telemedicine. The last three subscales are answered in a five-point Likert scale, and subscales II and III are referred to two moments, before and after lockdown [14]. After a factorial analysis (CFA), five factors were identified [14]. Factor 1 (F1) was defined by the items measuring eating-related symptoms (subscale II); Factor 2 (F2), by the items measuring the effects of lockdown on the eating-related style (subscale III); Factor 3 (F3), by the items assessing anxiety and depressive symptoms (subscale III); Factor 4 (F4) was defined by the items related to ER (subscale III); and Factor 5 (F5), by those that evaluate telemedicine (subscale IV). In this study, F5 (subscale IV) was not evaluated.
Other socio-cultural and contextual information was also collected (e.g., age, work status, economic problems, social support, and health state) (see Additional File 1).
## Procedure
All the participants were already involved in outpatient treatment modality in specialized units of the different countries. Data collection took place retrospectively between June 2020 and March 2021. The subjects were asked by therapists from each centre to voluntarily participate, completing once the required information in reference to the first/early lockdown: some subscales within the CIES Scale, as well as additional data regarding socio-cultural and contextual lockdown were asked regarding two moments, before and after lockdown.
## Statistical analysis
Stata17 for Windows was used for the statistical analysis [27]. The post–pre differences/changes were generated for the weight (kg), the body mass index (BMI) (kg/m2), and the CIES factor scores (the absence of changes comparing the post- versus the pre-measures provided a difference equal to zero, positive differences indicated a decreasing trend, and negative differences indicated an increasing trend). Repeated measures analysis of variance (repeated-ANOVA) tested the significance relevance for the differences, and it was implemented through the manova command in Stata, which allows fitting mixed designs including controlled variables. The diagnostic subtype, age and, country were included as covariates in the study. Standardized Cohen’s-d coefficients measured the effect size for the differences between the means (null effect size was considered for |d|< 0.20, low-poor for |d|> 0.20, moderate-medium for |d|> 0.50 and large-high for |d|> 0.80) [28, 29]. The Finner’s method (family-wise error rate -FWER- algorithm more powerful than the classical Bonferroni’s correction) was employed for controlling the increase in the Type-I error due the use of multiple significance tests [30].
## Characteristics of the participants
Most participants in the study lived with other people during the lockdown (only 29 individuals reported living alone, $11.0\%$), were not infected by COVID-19 ($86\%$), did not have infected relatives or other close people ($57.2\%$), did not have the responsibility of caring for infected relatives ($64.8\%$), were active at work ($52.7\%$), and did not report economic difficulties in the context of the confinement ($59.5\%$). Additional file 1 displays the distribution of the age and the contextual variables registered during the lockdown (see Additional file 1).
## Intra- and between-group comparisons regarding diagnostic subtypes
Table 1 contains the post–pre changes in the weight (kg), the BMI (kg/m2) and CIES subscales scores within each diagnostic subtype. The repeated measures ANOVA adjusted by age and country showed increase for the CIES F3 anxiety/depression and F4 emotion dysregulation among all the ED subtypes. Additionally, patients with BN also increased the CIES F1 eating-related symptoms and F2 eating-related style; BED patients increased weight and BMI mean values. Table 1Assessment of the post–pre changes stratified by ED-subtypeAnorexia Nervosa ($$n = 74$$)Bulimia ($$n = 44$$)PrePostPrePostMeanSDMeanSDp|d|MeanSDMeanSDp|d|Weight (kg)48.859.7547.088.82.0610.1962.9612.1063.9413.03.2440.08BMI (kg/m2)18.843.2618.283.99.1410.1523.815.3224.175.66.2520.07CIES-F1 ED symptoms15.076.0615.815.90.1950.1218.555.5220.466.65.041*0.31CIES-F2 Eating style13.088.8612.588.32.3980.0620.649.6723.6811.19.012*0.29CIES-F3 Anxiety-depress19.668.9524.779.13 <.001*0.56†19.3910.0724.6411.63 <.001*0.51†CIES-F4 Emotion dysreg9.164.3510.574.86 <.001*0.309.524.9511.025.57.001*0.28BED ($$n = 81$$)OSFED ($$n = 65$$)PrePostPrePostMeanSDMeanSDp|d|MeanSDMeanSDp|d|Weight (kg)85.5919.5889.2921.36 <.001*0.1872.1519.4771.9722.01.8680.01BMI (kg/m2)32.306.6633.707.37 <.001*0.2027.047.4026.958.17.8210.01CIES-F1 ED symptoms16.464.0317.074.19.2090.1515.665.4015.895.39.7350.04CIES-F2 Eating style26.708.7626.659.19.9600.0117.629.0019.069.63.1510.16CIES-F3 Anxiety-depress18.837.7424.828.54 <.001*0.73†18.228.0023.988.47 <.001*0.70†CIES-F4 Emotional dysreg8.444.309.674.41 <.001*0.287.863.919.263.91.008*0.36ED: eating disorder. BMI: body mass index. BED: binge eating disorder. OSFED: other specificized feeding and eating disorders. Emotion dysreg.: emotional dysregulation. SD: standard deviation. * Bold: significant comparison. †Bold: Effect size into the ranges moderate to large. Results adjusted by age and country Figure 1 shows the mean scores for the post–pre changes between groups (defined as the difference between the measures at the end of the lockdown versus the measures prior to the lockdown) (see Additional file 2). After the adjustment by age and country, the mean changes for weight and BMI were statistically equal comparing AN versus OSFED (both groups decreased) and comparing BN versus BED (both groups increased). For the CIES F2 eating-related style, BN and OSFED achieved similar mean increase, while a brief decrease was reported for AN and BED.Fig. 1Mean post–pre changes by country. Note. BMI: body mass index. AN: anorexia nervosa. BN: bulimia nervosa. BED: binge eating disorder. OSFED: other specified feeding eating disorder
## Intra- and between-group comparisons regarding groups of age
The results of the repeated ANOVA exploring the changes between the post- and pre-lockdown within each age group (young/adolescents and adults) are displayed in Table 2. After the adjustment by the ED subtype and country, it was observed a significant increase in the CIES F1 eating-related symptoms and CIES F3 anxiety/depression scales among the young/adolescents subsample. For the adult subsample, a significant increase was observed in the weight, the BMI, the CIES F3 anxiety/depression, and the CIES F4 emotional dysregulation. Table 2Assessment of the post–pre changes by groups of ageYoung/adolescents ($$n = 78$$)Adults ($$n = 186$$)PrePostPrePostMeanSDMeanSDp|d|MeanSDMeanSDp|d|Weight (kg)63.4015.1362.6416.21.4510.0567.7222.2069.0824.39.044*0.06BMI (kg/m2)24.396.0224.156.63.5130.0425.717.9526.278.81.035*0.07CIES-F1 ED symptoms15.605.5117.456.46.030*0.3116.985.2017.445.29.2280.09CIES-F2 Eating style17.409.4717.799.91.6790.0420.5910.1721.5110.67.1410.09CIES-F3 Anxiety-depress18.289.1224.189.44.001*0.64†19.728.2824.529.15.001*0.55†CIES-F4 Emotional dysreg9.194.6810.264.90.0650.228.734.2110.004.55.001*0.29ED: eating disorder. BMI: body mass index. Emotion dysreg.: emotional dysregulation. SD: standard deviation. * Bold: significant comparison. †Bold: Effect size into the ranges moderate to large. Results adjusted by ED-subtype and country The results of the ANOVA procedures comparing the post–pre changes during the lockdown between the groups of age (also adjusted by the ED subtype and the country) showed no differences (see Fig. 2 and Additional file 3).Fig. 2Mean post–pre changes by age. Note: BMI, body mass index
## Intra- and between-group comparisons regarding country
The results of the repeated-ANOVA stratified by country and adjusted by the ED-subtype and age are shown in Table 3. For the three countries, a significant increase was identified in the CIES F3 anxiety-depression scale. In addition, an increase in the CIES F4 emotion dysregulation scale was observed for patients living in Brazil and Spain. Table 3Assessment of the post–pre changes stratified by countryPortugal ($$n = 28$$)PrePostMeanSDMeanSDp|d|Weight (kg)58.7517.5659.5820.23.4100.04BMI (kg/m2)22.677.3823.008.36.3980.04CIES-F1 ED symptoms16.695.3717.417.91.4990.11CIES-F2 Eating style18.489.5418.579.20.9390.01CIES-F3 Anxiety-depression symptoms21.847.7524.018.32.043*0.27CIES-F4 Emotional dysregulation9.214.359.454.32.5270.06Brazil ($$n = 101$$)PrePostMeanSDMeanSDp|d|Weight (kg)67.4217.7568.7520.70.3160.07BMI (kg/m2)25.586.3726.127.51.2730.08CIES-F1 ED symptoms17.404.5918.544.77.1130.24CIES-F2 Eating style24.427.6325.898.69.2260.18CIES-F3 Anxiety-depression symptoms19.277.2927.088.26 <.001*1.00†CIES-F4 Emotional dysregulation8.763.8010.644.19 <.001*0.47Spain ($$n = 135$$)PrePostMeanSDMeanSDp|d|Weight (kg)70.3923.3470.2924.88.8790.00BMI (kg/m2)26.508.4426.479.12.9210.00CIES-F1 ED symptoms15.935.8316.615.72.1530.12CIES-F2 Eating style16.5310.3516.7610.24.7010.02CIES-F3 Anxiety-depression symptoms18.959.5023.8810.01 <.001*0.50†CIES-F4 Emotional dysregulation8.884.7510.205.05 <.001*0.27ED: eating disorder. BMI: body mass index. SD: standard deviation. * Bold: significant comparison. †Bold: Effect size into the ranges moderate to large. Results adjusted by ED-subtype and age.
The comparison of the post–pre changes between the countries (adjusted by the ED-subtype and age) is contained in Fig. 3 and Additional file 4. Patients living in Brazil reported higher positive changes in the CIES scales (except for the F1 eating-related symptom levels).Fig. 3Mean post–pre changes by country. Note: BMI, body mass index
## Discussion
The main aim of this work was to evaluate the impact of COVID-19 lockdown in eating symptomatology and general psychopathology in patients with EDs from Ibero-Brazilian countries, considering the CIES Scale, as well as other socio-cultural and contextual factors. We also performed between-groups comparisons regarding ED subtypes, age, and provenance.
A global trend towards the impairment of eating symptoms pre to post lockdown was described in all ED subtypes [7, 11], reaching statistical significance in the BN group. The last result contrasted with previous studies that described an absence of pre-post changes [14], or even a decrease in eating symptoms, among individuals with BN during lockdown [21]. In our study, surprisingly, these patients also reported a significant improvement in their eating style, similarly to the OSFED group. Likewise, changes in eating style were more alike between individuals with AN and BED, who reported eating style worsening. This result partially agreed with previous literature which referred to BED and BN as the ED subtypes with the highest negative impact on eating style [21]. In our case, a higher trend to control food intake and restriction in patients with AN [8, 9] whereas disordered eating such as grazing eating behavior among individuals with BED may support these findings [7].
Remarkably, the AN and OSFED group had significant differences in weight and BMI changes in comparison with the BED and BN groups, which also appeared to be more similar to each other. While a trend towards weight loss, with a consequent reduction in BMI, was observed in individuals with AN and OSFED, the opposite occurred in subjects with BED and BN [21, 31]. In fact, intra-group comparisons showed that the BED group reported a significant increase in weight and BMI after lockdown [21]. Restrictive measures, sedentarism, and food insecurity may be contributing factors [32]. However, our results revealed that neither of the two weight approaches were significantly superior to the other. Thus, a balanced weight change between ED subtypes may be deduced in contrast with previous literature [21].
Despite both groups of age (i.e., adolescent/young and adults) reported worse eating symptoms after lockdown, in the case of younger patients this change was statistically significant. Works related to young people with a diagnostic of ED showed a psychopathological impairment in the context of pandemic characterized by a higher difficulty in achieving goal weight, as well as a higher hospitalization rate [33]. Lower food security, changes in academic routines, and stress due to pandemic with affective implications have been pointed as some of the factors potentially involved [34]. However, we failed to find significant pre-post differences between adolescents/young individuals with EDs and adult patients neither in weight/BMI changes, eating symptoms, nor in psychological state. In the general population, younger age has been proposed as a contributing factor for suffering from a more negative impact on mental health during pandemic [18], including disordered eating [33, 34] and the development of an ED [35]. On the other hand, our results also contrast with the hypothesis of a higher resilience among younger patients in comparison with adults regarding people with EDs [21]. The global tendency to an impairment of the eating and mood symptoms in both groups during lockdown could partially contribute to explain our findings, in line with Monteleone et al. [ 19]. Indeed, an increased need for ED assistance has been described in the context of the pandemic in both adolescents/young and adults individuals with EDs [36].
While the younger group described a mild non-significant loss of weight, the adult group experienced a significant increase of weight and BMI during lockdown. Although our results have been adjusted by ED subtype, patients with younger age would presumably have a higher prevalence of AN and OSFED diagnosis than BED, more frequent among adult individuals [37]. Moreover, both groups showed an eating style worsening. In this regard, we hypothesize that the impaired eating pattern in younger patients could be more linked to restrictive behaviors and exercise practice, in line with the observed trend to control weight. On the other hand, changes in eating style among adults might be closely associated with increased food consumption (e.g., picking, binging), which, consequently, could be more probably linked with a weight increase.
To the best of our knowledge, this is the first study which includes European countries (Iberian countries, Spain and Portugal) with South American ones (i.e., Brazil). Previous studies analyzing eating and mood psychopathology between different continents are scarce without grouping European and South American countries [21]. Curiously, in the later work, authors reported that Asian patients seemed more resilient than European individuals with EDs, who reported worse eating symptomatology during the lockdown [21]. Using the same instrument (i.e., CIES), the present study did not find significant differences related to eating psychopathology, nor weight changes between European and South American individuals, regardless of the ED subtype and age. Indeed, we observed that the three groups of provenance reported a trend towards worsening of their eating symptoms. Despite these results would be in line with the global impairment of eating symptoms described among patients with EDs in the context of the lockdown [7, 8, 10, 11], it also highlights the need to design future studies that include large international samples to contrast whether the impact of the pandemic and lockdown on eating symptoms in individuals with EDs would be more similar between some continents, as well as which kind of socio-cultural and contextual features could be modulating this fact.
Interestingly, after lockdown, the self-reported anxiety and depressive symptoms evaluated with the CIES scale were higher and statistically significant than in the pre-lockdown in all the ED subtypes, age groups, and countries, and generally accompanied by an impaired ER [11, 19]. Even that a concern for patients with EDs was expressed from the beginning of the COVID-19 pandemic [7], current studies have supported that this population has been highly impacted by this health crisis [38, 39]. Going one step further, as a result of the between-groups comparisons, only the comparison between countries showed significant differences. Curiously, the Brazilian group described a worse psychological state in the context of the lockdown when compared with Iberian countries. In this line, a previous study found higher anxiety in the Brazilian population than in the Portuguese population [4], describing socio-cultural factors such as concern for health and finances as potential risk factors. Then, our results might also be considered in light of the existence of contextual differences during lockdown and socio-cultural aspects. As mood disturbances have been associated with a negative impact on eating symptoms among patients with ED [33], this study suggests whether those individuals with more adverse contextual conditions and a worse psychological state in the face of future similar adverse circumstances might be at greater risk of eating symptoms worsening in the middle and long term.
The scenario related to early pandemic and the restrictive measures adopted by governments differed between countries and could have contributed to the perceived emotional distress [2]. According to our results, the Brazilian individuals were those who had significantly higher percentages of people in charge, infection by COVID-19 and close people infected, in contrast with the other countries. This could be related to the fact that Brazilian patients were those who mostly kept working during the lockdown, with a presumably higher exposure to the infection added to the fact that social measures in the face of the pandemic appeared to be laxer. In this line, concerns related to both their own or their relatives’ health have been reported as potential stressors with a negative impact on mental health during the COVID-19 pandemic [22], as well as in previous health crises [23]. On the other hand, they also experienced higher financial losses during lockdown, which might be associated with a higher emotional distress.
In Brazil, as in other South American countries, aspects such as the lower social income of the population and a lack of infrastructures related to the public health system resulted in higher difficulties in access to treatment for mental health, including EDs. Despite all the participants of the study were already linked to specific treatment and aspects related to the evaluation of treatment during lockdown were not reported in this study, we hypothesized whether being subject to different socio-cultural and contextual conditions may have had an influenced in the adaptation and therapeutic adherence during this period and, therefore, in the perceived emotional distress. In this line, during the pandemic, most of the studies performed that reported the rapid implementation of telehealth care for EDs and other psychiatric conditions have been carried out in European, Australian, Asiatic, and North American countries so far [21, 40]. However, there is still a lack of information on the health policies of South American or African countries in this regard [24]. Then, both a regulation of the incomes and health care policies may be considered and improved in order to ensure that health care will be provided in those vulnerable populations despite the country of residence [24]. This fact becomes especially relevant when considering that poorer adaptive coping strategies to deal with emotional distress related to the pandemic and lockdown has been described as a factor of higher psychological vulnerability in patients with EDs [11–13]. Precisely, Brazilian participants showed lower resilience, which could also mediate the significant differences observed regarding greater emotional distress in this group.
This study has some limitations, such as a small simple size, an observational cross-sectional design, the lack of a control group, and the focus on the female clinical population already linked to a specific treatment unit, which could limit the generalization of the results. Besides, a voluntary participation, the retrospective collection of the data through a self-report way, and differences in the recruitment period between units are other limiting aspects, which could be associated with recall biases. On the other hand, some strengths should also be highlighted. For instance, the study contemplated potential co-founder factors in the analysis and the CIES is considered a validated and homogeneous psychometric instrument. However, future research is still needed to further investigate the clinical implications of mood disturbances related to pandemic situations on eating symptoms in the middle and long term, as well as potential meditational variables, such as sociodemographic and cultural factors.
In conclusion, the present study supports previous literature regarding the negative impact of the COVID-19 pandemic and lockdown on patients with EDs, adding a transcultural perspective with the inclusion of European and South American countries, and paying attention to the crucial role of mood disturbances and the sociodemographic context of the participants. Hence, more adverse contextual conditions, a worse psychological state, and poorer coping strategies may be potential contributing factors to the worsening of the eating symptoms in similar adverse situations.
## Supplementary Information
Additional file 1: Table S1. Descriptive for the age and the confinement contextAdditional file 2: Table S2. Comparison of the post-pre differences by the ED-subtypesAdditional file 3: Table S3. Comparison of the post-pre differences by groups of age (adjusted by ED-subtype and country)Additional file 4: Table S4. Comparison of the post-pre differences by country (adjusted by ED-subtype and age)
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|
---
title: Unique regulation of TiO2 nanoporous topography on macrophage polarization
via MSC-derived exosomes
authors:
- Jinjin Wang
- Yazheng Wang
- Yi Li
- Yide He
- Wen Song
- Qintao Wang
- Yumei Zhang
- Chenyang He
journal: Regenerative Biomaterials
year: 2023
pmcid: PMC10008081
doi: 10.1093/rb/rbad012
license: CC BY 4.0
---
# Unique regulation of TiO2 nanoporous topography on macrophage polarization via MSC-derived exosomes
## Abstract
The comprehensive recognition of communications between bone marrow mesenchymal stem cells (bm-MSCs) and macrophages in the peri-implant microenvironment is crucial for implantation prognosis. Our previous studies have clarified the indirect influence of Ti surface topography in the osteogenic differentiation of bm-MSCs through modulating macrophage polarization. However, cell communication is commutative and multi-directional. As the immune regulatory properties of MSCs have become increasingly prominent, whether bm-MSCs could also play an immunomodulatory role on macrophages under the influence of Ti surface topography is unclear. To further illuminate the communications between bm-MSCs and macrophages, the bm-MSCs inoculated on Ti with nanoporous topography were indirectly co-cultured with macrophages, and by blocking exosome secretion or extracting the purified exosomes to induce independently, we bidirectionally confirmed that under the influence of TiO2 nanoporous topography with 80–100 nm tube diameters, bm-MSCs can exert immunomodulatory effects through exosome-mediated paracrine actions and induce M1 polarization of macrophages, adversely affecting the osteogenic microenvironment around the implant. This finding provides a reference for the optimal design of the implant surface topography for inducing better bone regeneration.
## Graphical Abstract
## Introduction
Dental implants have been widely used in clinics, which has solved the problem of dentition defects to a certain extent. However, given adverse reactions such as insufficient initial stability and the risk of peri-implantitis, there are significant challenges in achieving fast and stable osseointegration of the implants [1]. A comprehensive understanding of the cellular response in the peri-implant microenvironment is essential to improve prognosis.
After implantation, bone marrow mesenchymal stem cells (bm-MSCs) around the implant bed and in the distal bone marrow migrate to the implant through a migration and homing mechanism, which is an important cellular basis for early osseointegration [2]. Many studies have indicated that the physical topography modification of titanium (Ti) with various dimensions can significantly promote the osteogenic differentiation of bm-MSCs in vitro [3, 4]. Additionally, our previous research confirmed that the micro-nano topography of 80–100 nm diameter TiO2 nanotubes has a significant promoting effect on the osteogenic differentiation of bm-MSCs [5]. However, in vivo application of a variety of bone implantation materials fails to achieve ideal osseointegration [6], including TiO2 with a nanoporous topography, which may not achieve as excellent as in vitro osteogenic effect when applied in vivo [5]. The reason may be that the innate immune regulation of the implanted host to the materials plays different regulatory roles in the process of osseointegration [5–7].
As the key member of innate immune response, macrophage is significantly involved in the regulation process of the local tissue inflammatory response, and its different polarization directions determine the corresponding functional states. M1 macrophages secrete pro-inflammatory cytokines, such as interleukin (IL)-6, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) and inducer nitric oxide synthase (iNOS), whereas M2 macrophages attenuate inflammatory responses and promote wound healing process by secreting multitudinous anti-inflammatory cytokines, such as IL-4, IL-10 and arginase-1 (Arg-1) [8]. Over the past 10 years, many researchers have paid attention to the direct effect of biomaterials on macrophage polarization [7, 9, 10]. Moreover, in-depth studies have been performed on the effects of biomaterials on the osteogenic differentiation of bm-MSCs by regulating macrophage polarization [5]. In the microenvironment around the implant, however, cells can receive all types of biological signals through multiple cues [11], including cell-to-cell communications. In addition to be directly regulated by implant properties, macrophage polarization states could also be influenced by the signal exchanges between macrophages and other cells, such as the large number of bm-MSCs that are recruited [12, 13].
Apart from excellent self-renewal and multi-directional differentiation potential ability, MSCs have powerful immunomodulatory properties as well [14]. The immunomodulatory response of MSCs is plastic and can play completely different roles according to the local environment [15]. As the key MSCs in peri-implant microenvironment, we speculate that after implantation, bm-MSCs not only achieve osteogenic differentiation but also have significant immunomodulatory effects under the influence of the implant. Under this circumstance, what happens to the immune function of macrophages is unclear.
Extracellular vesicles (EVs) are crucial transmitters in the involvement of bm-MSCs in the innate and adaptive immune responses. Numerous studies have indicated that MSC-EV mediated intercellular communications could regulate immune cells, including T/B lymphocytes, natural killer cells, dendritic cells and macrophages [16]. In various EVs, exosome-mediated intercellular signaling has attracted considerable attention. Exosomes are small vesicles secreted by cells with diameters of 30–150 nm, which contain important molecular information such as mRNA, miRNA and protein, and can participate in the regulation of the immune response, inflammation, angiogenesis and other physiological processes [17, 18]. Exosome is considered as a new route of intercellular communication and contributes to maintaining tissue homeostasis. Studies have confirmed that MSCs-derived exosomes play a dominant role in regulating the physiological functions of macrophages and are important for the process of MSCs regulating tissue homeostasis [19–21]. Thus, bm-MSCs are likely to regulate macrophage functions mediated by exosomes under the influence of the surface topography of bone-implant materials.
To obtain a comprehensive understanding of the immunomodulatory effects of bm-MSCs on macrophages, and to clarify the interactions among bone-implant materials, bm-MSCs and macrophages in the peri-implant microenvironment, on the basis of our previous studies about the immediate effects of implant surface topography on physiological functions of bm-MSCs and macrophages, this study further examines the immunomodulatory effects of bm-MSCs mediated by exosomes on macrophage polarization under the influence of implant surface topography, to provide guidance for optimal surface design of bone-implant materials for better osseointegration.
## Preparation and characterization of Ti specimens
Commercially pure Ti plates (circular, 15 mm in diameter and 1 mm thick; column-shaped, 1 mm in diameter and 2 mm long) were provided by the Northwest Institute for Nonferrous Metal Research, Xi’an, China and were sequentially wet-polished with SiC sandpaper (400–7000 mesh) to obtain polished Ti specimens (PT). Subsequently, Ti specimens with nano-topography (NT) were fabricated via anodization in accordance with our previous reports [5]. The anodizing electrolyte comprising 372 ml of deionized water, 23 ml of $85\%$ phosphate and 5 ml of hydrofluoric acid. The oxidation condition is a voltage of 20 V for 1 h. The fabricated topography was examined using field-emission scanning electron microscopy (FE-SEM, Hitachi, Japan), and the roughness was examined using atomic force microscopy (AFM, Shimadzu, Japan), and the hydrophilicity was assessed using a DSA1 System (Kruss, Germany). The fabricated specimens were sequentially cleaned in acetone, absolute ethanol and deionized water using ultrasonic cleaner. Then the cleaned specimens were immersed in $75\%$ ethanol for 6–8 h and ultraviolet radiated for at least 30 min for sterilization.
## Cell culture
The whole bone marrow culture of long bone was used to isolate bm-MSCs. The femurs and tibias of 6–8 weeks old C57BL/6J mice were selected. The typical procedure is the same as the previous study [5]. None-adherent cells were removed by frequent medium change during 72 h. After 9–12 days, the clustered adherent cells were considered as bm-MSCs, and they were passaged and inoculated onto blank plates (negative control, NC) and Ti specimens with different surface topographies. After 3 days of incubation, the culture supernatant was collected and centrifuged at 12 000 rpm for 10 min. Subsequently, the supernatant of each group was diluted in a ratio of 1:1 in the complete culture medium of macrophages (Roswell Park Memorial Institute, RPMI-1640, Hyclone, USA), and the resulting media were denoted as bm-MSCs conditioned medium (CM) of NC, PT and NT.
The RAW264.7 cell line was purchased from ATCC and cultured under conventional culture conditions. The purity of cells was detected by flow cytometry using F$\frac{4}{80}$ labeling. After 24 h of adherent culturing, the macrophage culture medium RPMI-1640 was replaced with the CM of bm-MSCs in each group (the preparation of CM is described in the previous ‘Cell culture’ section), and then continued to be cultured for another 3 days.
## Osteogenic differentiation of bm-MSCs on Ti specimens with nanoporous topography
bm-MSCs were passaged and inoculated onto blank plates and Ti specimens with different surface topographies in 24-well plates (the inoculation density: 1.5 × 105 cells/well) under the condition of osteoinductive medium (Cyagen Biosciences, Santa Clara, CA, USA). After 3 days of incubation, the total RNA of bm-MSCs was extracted using a Trizol reagent (Takara, Japan) and quantified using a Nanodrop 2000 (Thermo Fisher Scientific, USA). After reverse transcription, qRT-PCR was conducted to detect the mRNA expression of osteogenic-related genes [alkaline phosphatase (ALP), runt-related transcription factor 2 (Runx2), osteocalcin (OCN) and osteopontin (OPN)] according to the manufacturer’s protocols (Takara). The primers used in this study are presented in Table 1. After 7 and 21 days of incubation, an ALP activity assay and quantification of mineralization via alizarin red staining were conducted, as previously reported [5].
**Table 1.**
| Gene | Forward primer sequence (5′–3′) | Reverse primer sequence (3′–5′) |
| --- | --- | --- |
| ALP | GACTGGTACTCGGATAACGA | TGCGGTTCCAGACATAGTGG |
| Runx2 | ATGCTTCATTCGCCTCACAAA | GCACTCACTGACTCGGTTGG |
| OCN | TGCTTGTGACGAGCTATCAG | GAGGACAGGGAGGATCAAGT |
| OPN | AGCAAGAAACTCTTCCAAGCAA | GTGAGATTCGTCAGATTCATTCCG |
| iNOS | GGACCCAGTGCCCTGCTTT | CACCAAGCTCATGCGGCCT |
| IL-6 | TAGTCCTTCCTACCCCAATTTCC | TTGGTCCTTAGCCACTCCTTC |
| TNF-α | CTTCTCCTTCCTGATCGTGG | GCTGGTTATCTCTCAG |
| Arg-1 | CTCCAAGCCAAAGTCCTTAGAG | AGGAGCTGTCATTAGGGACATC |
| IL-4 | ACAGGAGAAGGGACGCCAT | GAAGCCCTACAGACGAGCTCA |
| IL-10 | GCTCTTACTGACTGGCATGAG | CGCAGCTCTAGGAGCATGTG |
| GAPDH | TGTGTCCGTCGTGGATCTGA | TTGCTGTTGAAGTCGCAGGAG |
## Osseointegration of Ti implant with nanoporous topography in mice
Sixteen C57BL/6J mice (male, 6–8 weeks, 20 g) were included and randomly distributed into two groups (PT and NT). Femur implantation was achieved using previously reported procedures [5]. The animal procedures were approved by the university research ethics committee of the Fourth Military Medical University and in accordance with the guidelines for the management and use of laboratory animals. The laboratory mice were sacrificed at 3 weeks, and the femur specimens were obtained. The femur specimens were fixed in $4\%$ paraformaldehyde, then the osseointegration of implants was analyzed using a micro-computed tomography (micro-CT) scanner (YXLON International GmbH, Germany). Furthermore, the femur specimens were decalcified using ethylenediaminetetraacetic acid, and paraffin tissue sections were fabricated for hematoxylin & eosin (HE) and immunohistochemical (IHC) staining.
After the 3D scan reconstruction of femur specimens was completed, an area of 2 mm around the implant was selected as the region of interest (ROI), and the images were analyzed via VGStudio Max 2.2 (Volume Graphic, Germany). The new bone volume ratio (bone volume to total volume, BV/TV), trabecular thickness (TbTh), trabecular numbers (TbN) and trabecular separation (TbSp) of each specimen were determined and statistically analyzed. The infiltration of inflammatory cells and the expression level of M1 macrophage markers-iNOS (Abcam, UK, the dilution was 1:100) around the implant were detected via HE staining and IHC staining, respectively. The streptavidin-peroxidase method was used for IHC staining, in accordance with the manufacturer’s protocols (ZSGB-BIO, Beijing, China). Histological images of femur specimens were obtained using an optical microscope (Olympus, Japan) and analyzed via NIH Image J software.
## mRNA level of M1 macrophage markers
After 3 days of incubation, the total RNA of RAW264.7 was extracted, quantified and reversely transcribed (Takara) following the same procedures as previously described [5]. qRT-PCR was performed according to the manufacturer’s protocols (Takara). Primers used in the study are displayed in Table 1.
## Protein level of M1 macrophage markers
The surface markers CCR7 (M1) and CD206 (M2) were examined via flow cytometry for evaluating different macrophage phenotypes. The specific procedure is the same as described in previous studies [5]. Anti-mouse CD$\frac{16}{32}$ (Biolegend, USA) was used to block the non-specific antigens. The antibodies for flow cytometry in this study included PE-conjugated CCR7 and PerCP-conjugated CD206 (Biolegend, USA), and the isotype controls were PE-conjugated Rat IgG2a, ĸ and PerCP-conjugated Rat IgG2a, ĸ (Biolegend, USA). The expression level of M1/M2 macrophage markers of all samples was analyzed using a flow cytometer (NovoCyte, ACEA Biosciences, USA) in triplicate. In addition, the surface markers IL-1β (M1) and CD163 (M2) were detected via western blot for further evaluating different macrophage phenotypes. The first antibodies for western blot were anti-IL-1β (ABclonal, Wuhan, China, the dilution was 1:1000), anti-CD163 (ABclonal, the dilution was 1:1000) and anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (ABclonal, the dilution was 1:4000).
## Isolation, extraction, identification and inhibition of MSC-derived exosomes
bm-MSCs were inoculated on blank plates and Ti specimens with different surface topographies in 24-well plates (the inoculation density: 1.5 × 105 cells/well). After 24 h of incubation, the serum without exosomes replaced the normal serum in the culture medium, and then bm-MSCs continued to be cultured for another 48 h. The cultured supernatant of bm-MSCs was collected from each group, and exosomes were extracted via differential centrifugation at 4°C. *In* general, the supernatant was centrifuged at 300×g/min for 10 min, 2000×g/min for 10 min and 16000×g/min for 30 min. Then, the supernatant was dumped, and the sediment was centrifuged in an overspeed centrifuge at 150 000×g/min for 70 min. The supernatant was dumped and then re-suspended in PBS and centrifuged at 150 000×g/min for 70 min. The groups were denoted as NC-Exo, PT-Exo and NT-Exo.
The morphology and particle-size distribution of the exosomes was characterized via transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The exosome surface proteins were lysed using radioimmunoprecipitation assay lysis buffer and quantified with a bicinchonininc acid assay kit (Beyotime, Shanghai, China). Proteins with different molecules were separated via $10\%$ sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto polyvinylidene difluoride (PVDF) membranes. Following the block with $5\%$ bovine serum albumin in Tris–HCl balanced salt solution with Tween-20 (TBST, 10 mM Tris–HCl pH 7.5, 150 mM NaCl, $0.1\%$ Tween-20) for 1 h, the membranes were incubated in primary antibody diluent at 4°C overnight, and incubated with horseradish peroxidase-conjugated secondary antibodies (Cell Signaling Technology, USA) at the ambient temperature for 2 h. The first antibodies were anti-HSP90B1 (Cell Signaling Technology, the dilution was 1:1000), anti-TSG101 (Abcam, UK, the dilution was 1:1000), anti-CD9 (Abcam, the dilution was 1:1000) and anti-CD63 (Abcam, the dilution was 1:2000). The bands were visualized using an enhanced chemiluminescence reagent (Thermo Fisher Scientific). Subsequently, images were obtained via Tanon5500 (Shanghai, China) and analyzed using Image J software.
The bm-MSC-derived exosomes were labeled with PKH26 and added into the culture medium of macrophages for 3 h. Subsequently, the macrophage membrane was labeled with AF488, and the nucleus was labeled with Hochest 33342. The uptake of bm-MSC-derived exosomes by macrophages was detected under confocal laser microscopy.
The secretion of exosomes was inhibited by GW4869. As described previously, bm-MSCs were inoculated on the blank culture plates in 24-well plates (1.5 × 105 cells/well). When the cells adhered to the plate, and the cells were fused to $90\%$ after 24 h of incubation, GW4869 (5 μM) was added into the culture medium, and bm-MSCs were continued to be cultured for another 48 h. Then the GW4869-pretreated bm-MSCs were harvested and inoculated on the blank plates and Ti specimens with different surface topographies in 24-well plates (the inoculation density: 1.5 × 105 cells/well) under the condition of complete medium culture. After 3 days of incubation, the culture supernatant of each group was collected and centrifuged as before. The conditioned media without exosomes were denoted as NC-G, PT-G and NT-G.
## mRNA and protein level of M1 macrophage markers
The bm-MSC-derived exosomes from each group were added into the macrophage culture system at the final concentration of 20 μg/ml with PBS. Macrophages were stimulated with PBS only as a solvent control group. After 3 days of incubation, the expression of macrophage polarization markers was detected via qRT-PCR, flow cytometry and western blot (protocols similar to those described previously).
## Statistical analysis
Each experiment was independently performed three times. The GraphPad Prism 9.0 software was used for statistical analysis. Experimental data were presented as the mean ± standard deviation of samples (x ± S). Student’s t test was used for comparison between the two groups, and a one-way analysis of variance was used for three or more groups. The set test level α = 0.05 and bilateral $P \leq 0.05$ were considered to be statistically significant.
## Surface characteristics of TiO2 nanoporous topography
The surface topographies of both Ti specimens were characterized via FE-SEM. As shown in Fig. 1, the surface of PT was uniformly polished, whereas the tubular structure with a diameter of 80–100 nm was observed on NT surface (Fig. 1A). The roughness of both Ti specimens was evaluated via AFM. The surface roughness of NT was ∼10 times that of PT (Fig. 1C and E). The surface hydrophilicity of Ti specimens was reflected by the size of contact angle, which was ∼66° ± 1.75° for PT and 23° ± 3.12° for NT, indicating that the hydrophilicity of NT was better than that of PT (Fig. 1B and D).
**Figure 1.:** *Characterization of Ti specimens. (A) Surface topographies were examined using FE-SEM. (B, D) The hydrophilicity of Ti specimens was evaluated using the water contact angles. (C, E) The surface roughness of Ti specimens was examined using AFM. PT, polished Ti surface; NT, nanoporous Ti surface. ***P < 0.001.*
## Osteogenic induction of nanoporous topography in vitro and in vivo
The inductive effects of TiO2 nanoporous topography to the osteogenic differentiation of bm-MSCs was determined via qRT-PCR analysis, ALP staining and alizarin red staining. The mRNA expression of genes associated with osteogenesis (ALP, Runx2, OCN and OPN) was significantly enhanced in NT group compared with PT and NC in 3, 7 and 14 days (Fig. 2A). Additionally, the degree of ALP synthesis by bm-MSCs of NT was increased significantly compared with those of NC and PT, while there was no significant difference between NC and PT (Fig. 2B and D). Consistently, after incubation for 21 days, the most mineralized nodules were observed in the NT group. Furthermore, the enhanced formation of mineralized nodules in PT compared with NC was observed (Fig. 2C and E).
**Figure 2.:** *Osteogenic differentiation of mice bm-MSCs under the effects of different topographies in vitro. (A) qRT-PCR analysis of mRNA expression of ALP, Runx2, OCN and OPN in bm-MSCs after incubation for 3, 7 and 14 days. (B, D) ALP staining and ALP activity measurement after incubation for 7 days. (C, E) Alizarin red staining and semi-quantitative analysis after incubation for 21 days. NC, negative control; PT, polished Ti; NT, nanoporous Ti. *P < 0.01 vs NC, #P < 0.01 vs PT. Scale bar = 100 μm.*
However, the micro-CT results indicated that the bone around the implant was sparse, and no uniform and compact trabecular structure was observed in the PT or NT group. The results of 3D scan reconstruction analysis revealed that, except for TbTh and TbSp, other indicators of osteogenesis in the ROI in NT group was not significantly different from that in PT group (Fig. 3A and B). In the tissue sections of peri-implant femurs, a large number of infiltrating inflammatory cells was found in the target area for both PT and NT groups (Fig. 3C). Moreover, among the infiltrating inflammatory cells, numerous cells positively expressed iNOS, suggesting that macrophages in the peri-implant microenvironment tended to be M1-polarized (Fig. 3D). In addition, the number of M1 macrophages around the implant in NT group was even higher than that in PT group (Fig. 3E).
**Figure 3.:** *Osteogenesis of implants with different topographies in mice. (A) Micro-CT analysis of peri-implant osteogenesis, with a 2-mm area around the implant defined as the ROI. Scale bar = 500 μm. (B) Quantitative analysis of the bone volume fraction (BV/TV), trabecular separation (TbSp), trabecular number (TbN) and trabecular thickness (TbTh); (C) histological analysis of peri-implant inflammation via HE staining. The space in the upper left corner indicates the location of the implant. The green arrows indicate the infiltrating inflammatory cells. Scale bar = 200 μm. (D) Analysis of macrophage polarization around the implant via IHC staining. The red arrows indicate the iNOS-positive cells (M1 macrophages). scale bar = 100 μm. (E) The semi-quantitative analysis of IHC staining. *P < 0.05, **P < 0.01; ns, no significance.*
## Influence of bm-MSCs regulated by TiO2 nanoporous topography on macrophage polarization
To detect the effects of bm-MSCs cultured on either PT or NT on the polarization of macrophages, the latter were co-cultured indirectly using a CM of bm-MSCs on Ti surfaces with different topographies. First, the purity of the macrophage cell line RAW264.7 was identified with F$\frac{4}{80}$ labeling and was found to be >$99\%$ (Fig. 4B). Then, qRT-PCR analysis revealed that the conditioned media of bm-MSCs in both PT and NT groups were more prone to induce macrophages to overexpress iNOS, IL-6 and TNF-α compared with NC, which were mostly M1 polarization markers. In particular, the macrophages in NT group exhibited highly significant expression of iNOS and IL-6 among the three groups. In contrast, the macrophages in PT and NT groups did not manifest significantly different expressions of Arg-1 and IL-4 compared with NC; furthermore, the IL-10 expression was significantly lower in NT group than in the other groups (Fig. 4A). Western blot verified the higher protein expression of M1 macrophage marker IL-1β and lower protein expression of M2 macrophage marker CD163 in NT group (Fig. 4C and D).
**Figure 4.:** *Effects of CM for bm-MSCs induced by the different surface topographies of Ti specimens on macrophage polarization. (A) qRT-PCR analysis of mRNA expression for M1/M2 macrophage markers (M1: iNOS, IL-6 and TNF-α; M2: Arg-1, IL-4 and IL-10). (B) Flow cytometry with F4/80 labeling for evaluating the purity of mouse macrophage lines RAW264.7. (C) Western blot analysis of protein expression for M1/M2 macrophage markers (M1: IL-1β; M2: CD163). (D) Semi-quantitative analysis of the protein expression level. (A) *P < 0.01 vs NC, #P < 0.01 vs PT; (D) **P < 0.01.*
## Influence of bm-MSCs-derived exosomes regulated by TiO2 nanoporous topography on macrophage polarization
Paracrine is an important pathway of immunomodulatory function of MSCs. To detect whether bm-MSCs-derived exosomes cultured on nanoporous Ti have a significant effect on macrophage M1 polarization, GW4869 was used to block the secretion of exosomes from bm-MSCs, then the CM without exosomes of bm-MSCs was used for indirect co-culturing with macrophages. qRT-PCR analysis revealed that after the exosomes from bm-MSCs were blocked, the CM in NT group induced the down-regulation of iNOS and IL-6, and the up-regulation of IL-4 in macrophages, while the expressions of Arg-1 hardly differed among the three groups (Fig. 5A). Western blot results revealed that the protein expression of M1 macrophage marker IL-1β in NT-G was significantly reduced compared with the other groups, while the protein expression of M2 macrophage marker CD163 was barely changed for both PT-G and NT-G (Fig. 5B and C). Similar results were obtained via flow cytometry; that is the CM of bm-MSCs for both PT-G and NT-G induced evidently reduced expression of M1 macrophage marker CCR7, while there was no significant difference in the expression of M2 macrophage marker CD206 among the three groups (Fig. 5D).
**Figure 5.:** *Effects of CM without exosomes for bm-MSCs induced by the different surface topographies of Ti specimens on macrophage polarization. The secretion of exosomes was blocked by GW4869 (or DMSO as control). (A) qRT-PCR analysis of mRNA expression of M1/M2 macrophage markers (M1: iNOS and IL-6; M2: Arg-1 and IL-4). (B) Western blot analysis of protein expression of the M1/M2 macrophage markers (M1: IL-1β; M2: CD163). (C) Semi-quantitative analysis of the protein expression level. (D) Flow cytometry analysis of M1/M2 macrophage markers (M1: CCR7; M2: CD206). NC-C, negative control with DMSO; PT-C, polished Ti with DMSO; NT-C, nanoporous Ti with DMSO; NC-G, negative control with GW4869; PT-G, polished Ti with GW4869; NT-G, nanoporous Ti with GW4869. *P < 0.05, **P < 0.01, ***P < 0.001.*
To further verify the role of exosomes derived from bm-MSCs in the regulation of macrophage polarization, we extracted and identified the exosomes from bm-MSCs via differential centrifugation. TEM indicated that the precipitations obtained via differential centrifugation from the culture supernatants of bm-MSCs in NC, PT and NT groups all had a double-layer membrane structure, which was cup-shaped or quasi-circular, with a diameter of <200 nm, conforming to the morphological characteristics of exosomes (Fig. 6A). The NTA results indicated that the peak particle size of exosomes from bm-MSCs in PT group was 127 nm, with a peak area of $90.4\%$, and that for NT group was 129.8 nm, with a peak area of $93.5\%$, both satisfying the standard for the particle-size distribution of exosomes (Fig. 6C). Western blot results indicated that bm-MSC-derived exosomes had negative expression of intracellular protein HSP90B1 and positive expression of transmembrane or lipid-binding-related extracellular proteins (CD9 and CD63) and cytoplasmic proteins (TSG101) (Fig. 6B). To determine whether macrophages can take in exosomes, PHK26 was used to label the exosomes derived from bm-MSCs; AF488 was used to label the cell membranes of macrophages, and Hochest 33342 was used to label the nuclei of macrophages. Using confocal laser microscopy, macrophages taking in exosomes were observed (Fig. 6D).
**Figure 6.:** *Identification of exosomes derived from bm-MSCs cultured on the surface of Ti with different topographies and the influence of bm-MSC-derived exosomes on macrophage polarization. (A) TEM observation; scale bar = 200 nm. (B) Western blot analysis of the expression level of exosome surface proteins. (C) NTA of exosome particle-size distribution. (D) Observation of exosome uptaken by macrophages. (E) Western blot analysis of the protein expression level of macrophage polarization markers induced by exosomes derived from bm-MSCs. (F) Semi-quantification of western blot analysis. (G) qRT-PCR analysis of the mRNA expression of macrophage polarization markers induced by exosomes derived from bm-MSCs. (H) Flow cytometry analysis of macrophage polarization marker expression induced by exosomes derived from bm-MSCs. CL, the total protein of bm-MSCs; PBS, solvent control for solubilizing exosomes; NC-Exo, exosomes from bm-MSCs on blank culture plates; PT-Exo, exosomes from bm-MSCs on Ti with polished surface; NT-Exo, exosomes from bm-MSCs on Ti with nanoporous topography. **P < 0.01, ***P < 0.001.*
After characterization and identification, bm-MSC-derived exosomes in NC, PT and NT groups were added into the macrophage culture medium to clarify the regulatory effect of bm-MSC-derived exosomes on macrophage M1 polarization. PBS was used as a solvent control for solubilizing exosomes. qRT-PCR analysis revealed that the mRNA expression of iNOS and IL-6 in NT group was significantly elevated than that of the other groups, whereas the mRNA expression of M2 macrophage markers showed no significant difference. In addition, exosomes from PT group induced significantly higher expression of M1 macrophage markers compared with the PBS and NC groups (Fig. 6G). Western blot results suggested that the protein expression of IL-1β in NT group was significantly elevated, whereas the protein expressions of IL-1β in NC and PT groups were nearly identical and significantly higher than those of the PBS group. There was no significant difference in the expression of CD163 among the four groups (Fig. 6E and F). Flow cytometry results revealed that bm-MSC-derived exosomes in NT group promoted significantly higher expression of M1 macrophage marker CCR7 and rarely changed the expression of M2 macrophage marker CD206 compared with other groups (Fig. 6H). These results suggested that bm-MSC-derived exosomes induced by Ti with nanoporous topography could effectively induce the M1 polarization of macrophages.
## Discussion
A nano-topography with a biomimetic hierarchical structure was constructed on the surface of pure Ti to simulate the gradient structure of natural bone tissue, which can promote the osseointegration of implants. Our research group has been devoted to promoting osteogenesis of materials through physical modification of the Ti surface topography, and we found that TiO2 nanotube structure with different tube diameters was prone to promote osteogenic differentiation of MSCs in vitro [5, 7]. However, under in vivo conditions, the composition around the implant is complex with a diverse microenvironment. This may include osteogenic basic cells, for example, MSCs and various immune cells, among which macrophages related to the tissue inflammatory response are crucial for the prognosis of implant [2]. Upon implantation of biomaterials, MSCs and macrophages contribute jointly to the wound healing and the subsequent regeneration process. Therefore, it is necessary to clarify the crosstalk among biomaterials, MSCs and macrophages.
In the early stage, we comprehensively investigated the direct regulatory effect of Ti surface topography on macrophage polarization and the consequent influence in osteogenic differentiation potential of MSCs by regulating macrophages [5]. In this study, we also verified the ability of the previously constructed nanoporous topography of TiO2 nanotubes with diameters of 80–100 nm to induce the osteogenic differentiation of bm-MSCs in vitro, and the results suggested that the expression of Runx2 and OCN in NT group was significantly elevated compared with the other groups. Runx2 is required for MSCs differentiation along the osteoblast lineage [22]. OCN is not only considered as a late-stage marker of osteogenesis, but also associated with insulin secretion [23]. Studies shows that high serum OCN concentration is beneficial for blood glucose control [24, 25]. So we assumed that the nanoporous topography of Ti may benefit the prognosis of diabetic bone implant materials and indirectly participating in the occurrence and development of diabetes by significantly up-regulating the OCN expression. The promoting effects on the osteogenic differentiation of bm-MSCs may be directly related to the increased roughness and hydrophilicity of the nanoporous topography.
Furthermore, Ti rods with different surface topographies were implanted into the cancellous bone of mice femurs, but the ability of nanoporous topography to induce osseointegration in vivo was inferior to the in vitro results. Histological observations revealed that a large number of inflammatory cells were concentrated around the implant in NT group, including M1-polarized macrophages, which was similar to PT group. The result that the osteogenesis abilities induced by the nanoporous topography in vivo and in vitro were not identical was consistent with our previous studies, and the changes in macrophage functions are critical for the implant prognosis. In addition to the direct regulation influence of nano-topography on macrophage polarization confirmed by previous studies, the immunomodulatory effects of MSCs could not be ignored.
The immunomodulatory effects of MSCs have attracted considerable attention in recent years [14]. The immunomodulatory effects of MSCs are plastic, and paracrine function is an important way to exert immune regulation. Under normal circumstances, MSCs can secrete inflammatory regulatory factors and pro-angiogenic factors after being stimulated by inflammation [26]. Therefore, stem-cell therapy has been extensively studied in the treatment of various immune inflammatory diseases, including colitis [27–29]. EVs, which are important for information communication between cells, contribute largely to maintaining tissue homeostasis [30]. As nanoscale extracellular lipid bilayer vesicles, exosomes can transport a large number of bioactive molecules to target cells, participating in basic physiological processes including immune response [31] and promoting pathological processes including inflammation [32]. The immunomodulatory function of MSCs can be realized by paracrine effects. Exosomes, as important components of paracrine products of MSCs, are affected by plenty of extracellular factors; among them, the properties of cell culture substrate are the key influencing factors. Studies have shown that the micro-patterned topography of Ti could promote exosome biogenesis and secretion, which influence the osteogenesis process [33, 34]. This is similar to the direction of our attention. After exosomes are released, they could be involved in the immunomodulatory process [26]. Studies have revealed that exosomes secreted by human umbilical cord MSCs can inhibit the overactivation of monocytes and macrophages, thus alleviating the acute liver injury [35]. In addition, exosomes derived from adipose MSCs are prone to induce M2 macrophage polarization [36]. Exosomes derived from MSCs can also achieve pulmonary function recovery through macrophage immune regulation [37]. In conclusion, a growing body of evidence suggests that exosomes might be involved in the regulation of macrophage functions. However, the paracrine function of MSCs could be affected by the microenvironment, for example, in the peri-implant microenvironment, bm-MSCs may be affected by the nanoporous topography of Ti, thus altering its paracrine function. Li showed that the topographical cues of materials could modulate paracrine functions of MSCs [38]. Therefore, we aimed to further investigate the impact of the nanoporous topography, which is extremely beneficial to osteogenesis in vitro, on the immune regulation of bm-MSCs. Through indirect co-culturing, we clarified that paracrine products of bm-MSCs can promote M1 polarization of macrophages under the induction of nanoporous topography with 80–100 nm tube diameters, but whether this induction effect is caused by exosomes remains to be verified.
By inhibiting the secretion of exosomes using GW4869 and repeating the above experimental process, we found that the effect of bm-MSCs induced by nanoporous topography on the M1 polarization of macrophages was significantly weakened, whereas the expression of M2 polarization markers was hardly affected, which suggested that the exosomes may be the key role that regulates macrophage M1 polarization through paracrine pathway of bm-MSCs. Compared with the blank control, we found that after the secretion of exosomes was blocked, the effect of bm-MSCs on inducing M1 polarization of macrophages in PT group was also significantly weakened, suggesting that compared with the ordinary culture plates, the matrix stiffness and hardness of Ti could be sensitively recognized by bm-MSCs, thus inducing changes in paracrine regulatory functions. Sridharan showed that the substrate stiffness could regulate the communication and immunomodulatory effects of MSCs on macrophages [39]. Therefore, compared with the Ti substrate, the nanoporous topography on Ti might play a far stronger role in the immune regulation of bm-MSCs. Exosomes were obtained via gradient centrifugation, and after the characterization of exosomes in different groups, we charified the independent effects of exosomes from different bm-MSCs on macrophage polarization. The results confirmed that under the induction of nanoporous topography, bm-MSCs may secrete exosomes containing special bioactive molecules, and the exosomes can be uptaken by macrophages, thus having immunomodulatory effects on macrophage M1 polarization.
The results demonstrate that, induced by the physical topography of Ti surface, bm-MSCs could exert an immunomodulatory effect on macrophage polarization through the exosome-mediated paracrine pathway. The finding complements our previous studies on the communications between bm-MSCs and macrophages in the peri-implant microenvironment, which is highly innovative and has directive significance in the MSC treatment with exosome targeting and the improvement of implant prognosis. However, further research should be performed to determine the key bioactive molecules in exosomes, which can regulate macrophage M1 polarization, and to charify how the nanoporous topography could regulate the immunomodulatory effects of bm-MSCs.
## Conclusion
This study clarified the complex immunomodulatory effects of bm-MSCs on macrophage M1 polarization, suggesting that in the absence of inflammatory stimuli, bm-MSCs can play a pro-inflammatory role under the influence of nanoporous topography with 80–100 nm tube diameters, supplementing the crosstalk among biomaterials, bm-MSCs and macrophages in the peri-implant microenvironment. Together, the biophysical cues such as topography that are presented by biomaterials can be tuned to play active roles in interactions between MSCs and macrophages, which in turn regulate the post-implantation response.
## Funding
This work was supported by the National Natural Science Foundation of China (grant numbers 82101064, 81971752), the Basic Natural Science Research Program of Shaanxi Province (grant number 2021JQ-424) and Xi’ an Jiaotong University school-level project (grant number xjh012020012).
Conflicts of interest statement. There are no conflicts of interest to declare.
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---
title: Treatment Outcomes of Diabetic Patients With Erectile Dysfunction Prescribed
High-Dose Tadalafil
journal: Cureus
year: 2023
pmcid: PMC10008086
doi: 10.7759/cureus.34812
license: CC BY 3.0
---
# Treatment Outcomes of Diabetic Patients With Erectile Dysfunction Prescribed High-Dose Tadalafil
## Abstract
Objective: To assess the treatment outcome of diabetic patients with erectile dysfunction who are prescribed an alternate daily high dose of tadalafil over a 120-day treatment period.
Methods: This was a single-site, retrospective, observational study of 63 diabetic men with erectile dysfunction prescribed an alternate daily dose of 30mg of tadalafil between January 1, 2021, and December 31, 2021. Treatment outcomes accessed medication compliance, adverse drug reactions, and patient treatment satisfaction at 60- and 120-days treatment.
Results: Mean age of patients was 58.3 years and included patients who suffered from comorbidities ranging from hypertension ($54.0\%$), dyslipidemia ($52.3\%$), and depression ($9.5\%$). At 60 days in the study, $69.8\%$ were satisfied and continued the treatment. However, at the end of the 120-treatment period, a low number of men ($17.5\%$) were satisfied with the treatment and therefore did not remain on the treatment protocol. These patients reported a lack of medication dose efficacy ($86.5\%$), non-compliance with treatment as prescribed ($65.4\%$), and adverse drug reactions ($30.8\%$) as reasons for discontinuing treatment. None of the identified patient demographics were significantly associated with 120-day continuous treatment. Similarly, the odds ratio derived from the logistic regression did not demonstrate an association between the selected variables and the outcome of 120-day continuous treatment retention.
Conclusion: This retrospective case series study found that $82.5\%$ of diabetic patients were not satisfied with treatment with alternate dosing of 30mg tadalafil to treat their ED at the end of the 120-day treatment period suggesting an alternative treatment plan.
## Introduction
Erectile dysfunction (ED) is defined as a consistent alteration in the quality of erections that adversely affected the patient’s satisfaction with sexual intercourse [1]. ED is associated with medical conditions such as diabetes and hypertension, lifestyle factors, and medical treatments (beta blockers, thiazides, antidepressants, and prostate cancer surgery) and has been recognized as an early indicator of cardiovascular disease [2]. In Australia, ED is experienced by about one in five men over the age of 40 years [3]. The prevalence of ED with diabetes is much higher at $35\%$-$75\%$ [4-10]. In diabetes, hyperglycemia is considered the main factor for macro and microvascular complications, increasing sexual dysfunction through oxidative stress and impairing erectile and endothelial function. Autonomic neuropathy may also play a role in the pathogenesis, as do associated arteriosclerosis, hypertension, and hyperlipidemia [11].
Current first-line treatments include the phosphodiesterase 5 inhibitors (PDE5Is) sildenafil, vardenafil, avanafil, and tadalafil [1]. These drugs increase the intracellular cyclic guanosine monophosphate concentrations in cavernous tissue leading to the relaxation of arterial and trabecular smooth muscle and increasing arterial inflow and the rigidity of penile erection [12]. The efficacy of PDE5Is in non-diabetic men is about $60\%$-$70\%$ [1]. However, efficacy is lower in diabetic men due to impaired endothelium-derived factors in penile arteries and underlying endothelial dysfunction [13-16]. This may explain why ED is more refractory to treatment in diabetic men.
Previous studies have demonstrated men with diabetes; and ED can benefit from daily low doses of tadalafil (2.5 and 5mg) as an alternative to on-demand treatment of 5 to 20mg [12, 16]. A steady state of tadalafil is reached after five days of daily administration, with a plasma concentration that is roughly 1.6 times higher than that of a single dose [17]. Higher doses may be required in men who require greater serum concentrations for therapeutic effect by higher peak levels. However, higher doses can also be associated with adverse reactions such as headache, flushing, dyspepsia, nasal congestion, and dizziness [18-19].
Presented is a retrospective, observational case series of Australian diabetic patients with ED presenting to a single men’s health clinic in Australia and subsequently prescribed an alternate daily dose of 30mg tadalafil. The treatment outcome of these patients was assessed over a 120-day treatment period.
## Materials and methods
The process for identifying patients in the study is summarized in Figure 1. Green Dispensary Pharmacy South Australia’s electronic dispensing database was accessed to identify patients for whom a prescription for 30mg tadalafil capsule was prescribed during the period between January 1, 2021, and December 31, 2021. Of these patients, the patient case notes from Men’s Health Clinic Australia were accessed that met the inclusion and exclusion criteria. Each man was dispensed 30 capsules of extemporaneously compounded tadalafil 30mg capsules (60 days supply) at each visit. Patients were instructed to take one capsule alternate day continuously. Patients were interviewed by a practice nurse at each visit. Treatment outcome accessed medication compliance, adverse drug reactions, and patient treatment satisfaction. Treatment compliance was defined as taking at least 21 capsules over each visit ($70\%$ of required doses for a total of 42 capsules out of the 60 capsules dispensed), which was confirmed with engagement with the clinic and from the pharmacy dispensing history [16]. Measures of treatment satisfaction in this study were based on the two Global Assessment Questions (GAQ): ‘Has the treatment you have been taking improved your erectile function?’ ( GAG1) and if ‘yes,’ then ‘has the treatment improved your ability to engage in sexual activity?’ ( GAG2). These questions were presented to patients during their visits. Adverse drug reactions included common adverse reactions such as headache, flushing, dyspepsia, nasal congestion, and dizziness.
Inclusion criteria: Eligible men were ≥ 18 years old with a history of diabetes, having insulin treatment or receiving an oral glucose-lowering agent, diagnosis of ED based on the Sexual Health Inventory for Men (SHIM) questionnaire, and a dispensed prescription for 30mg tadalafil.
Exclusion criteria: Men were excluded from the study if they had uncontrolled hypertension, unstable coronary disease, renal and hepatic insufficiency, were newly diagnosed with diabetes during the study period or had radical pelvic surgery, penile prosthesis surgery, used penile devices, hormonal therapy, or concurrent use of other PDE5Is or other ED medications, and nitrate therapy.
All data extraction was performed by the primary researcher (AC). Ethics approval for this study was obtained from the relevant Human Research Ethics Committee (OUM 22-0419AC).
Data analysis Standard descriptive statistics were used to present the demographic and other characteristics of patients. Univariate assessment of categorical and continuous variables was performed initially, using Fischer’s exact test. Logistic regression was subsequently performed on selected variables. An alpha level of 0.05 was selected for statistical significance.
All statistical analyses were performed using Minitab® statistical software (Penn State University).
**Figure 1:** *Process for identifying eligible patients*
## Results
Patients Participant demographic and clinical characteristics are presented in Table 1. The sample ($$n = 63$$) had a mean patient age of 58.3 years with a mean body mass index of 30.6 kg/m². Eight ($12.7\%$) were current smokers, and 11 ($17.4\%$) were current consumers of alcohol. Most patients had comorbidities ranging from hypertension ($54.0\%$), dyslipidemia ($52.3\%$), and depression ($9.5\%$). These patients were prescribed an oral anti-diabetic drug 58 ($92.1\%$), insulin 16 ($25.4\%$), antihypertensive drug 27 ($42.9\%$), or antihyperlipidemic drug 31 ($49.2\%$). All patients suffered from ED for more than 12 months. Out of the 63 patients, 24 ($38.1\%$), 17 ($27.0\%$), and 22 ($34.9\%$) reported having mild, moderate, and severe ED, respectively.
**Table 1**
| Number of Patients, (%) | 63 (100%) |
| --- | --- |
| Age (years) (mean ± SD) | 58.3 ± 10.9 |
| Body mass index (kg/m2) (mean ± SD) | 30.6 ± 5.0 |
| Current smokers (%) | 8 (12.7%) |
| Current consumers of alcohol (%) | 11 (17.4%) |
| Comorbidities (excluding diabetes) (%) | |
| Hypertension | 34 (54.0%) |
| Depression | 6 (9.5%) |
| Dyslipidemia | 33 (52.3%) |
| Medication (%) | |
| Oral anti-diabetic drug | 58 (92.1%) |
| Insulin | 16 (25.4%) |
| Antihypertensive drug | 27 (42.9%) |
| Antihyperlipidemic drug | 31 (49.2%) |
| Erectile dysfunction duration ≥ 12 months (%) | 63 (100%) |
| Erectile dysfunction severity at baseline (SHIM) score (%) | |
| Mild | 24 (38.1%) |
| Moderate | 17 (27.0%) |
| Severe | 22 (34.9%) |
Treatment outcomes Patients were assessed at 60 and 120 days, respectively, investigating treatment outcomes. Of the 63 patients in the study, 44 ($69.8\%$) and 11 ($17.5\%$) were satisfied and remained in the treatment at 60 and 120 days, respectively (Table 2 and Figure 2). At 60 days, of the 19 patients that did not continue the treatment as prescribed, 18 ($94.7\%$) stated that a lack of efficacy was a reason not to continue, 11 ($57.9\%$) patients did not continue due to non-compliance with the medication as prescribed, 11 ($57.9\%$) patients experienced adverse drug reactions and had to stop treatment. These patients experienced an overlap of reasons for discontinuing treatment. Medication dosage was not increased in these 19 patients, and 17 ($89.5\%$) patients were switched to other treatments (Figure 3).
**Figure 2:** *Continuous retention of patients in study at 60 and 120 days* TABLE_PLACEHOLDER:Table 2 **Figure 3:** *Reasons for patient non-retention at 60 days*
At 120 days, of the 52 patients that did not continue the treatment as prescribed, 45 ($86.5\%$) stated that a lack of efficacy was a reason not to continue, 34 ($65.4\%$) patients did not continue due to non-compliance with the medication as prescribed, 16 ($30.8\%$) patients experience adverse drug reactions and had to stop treatment. Medication dosage was increased in 26 ($50.0\%$) patients, and 17 ($32.7\%$) patients were switched to other treatments (Figure 4). These 52 patients that were accessed at 120 days included the 19 patients accessed at 60 days. Many of the reasons for discontinuing treatment overlapped.
**Figure 4:** *Reasons for patient non-retention at 120 days*
Data showed that based on univariate analysis (Fischer’s exact test), none of the identified patient demographics were significantly associated with 120-day continuous treatment. The severity of ED (mild $63.64\%$, moderate $18.2\%$, severe $18.2\%$) was also not significant ($$P \leq 0.174$$). Similarly, the odds ratio derived from the logistic regression did not demonstrate an association between the selected variables and the outcome of 120-day continuous treatment retention (Table 3).
**Table 3**
| Variable | Continuous retention of patients in study at 120 days (n = 11) | P-value | Odds Ratio (95% CI) |
| --- | --- | --- | --- |
| Hypertension | 8 (72.7%) | 0.32 | 1.80 (0.43, 7.61) |
| Depression | 1 (9.1%) | 0.72 | 0.94 (0.01, 8.94) |
| Dyslipidemia | 8 (72.7%) | 0.12 | 2.88 (0.69, 12.08) |
| Current smokers | 1 (9.1%) | 0.57 | 0.64 (0.07, 5.83) |
| Current consumers of alcohol | 3 (27.3%) | 0.29 | 2.06 (0.45, 9.49) |
## Discussion
This study is the first to describe the treatment outcomes of diabetic patients with ED over a 120-day treatment period using alternate day dosing of 30mg tadalafil (high dose). Current prescribing guidelines for ED recommend tadalafil 10-20mg orally at a time before sexual activity or 2.5 to 5mg once daily [20]. However, there is no guidance on dosing based on ED severity or other comorbidities. Doses of up to 40mg daily have been used in patients with pulmonary arterial hypertension and are well tolerated [21]. Even though 30mg of tadalafil is not routinely used in the clinic continuously for ED, our study shows that higher doses or other treatments may be required in diabetic patients with refractory ED or patients unsatisfied with sexual performance.
There were only 11 patients out of the 63 samples ($17.5\%$) that continued the treatment for 120 days. There are several reasons that these diabetic patients did not obtain a favorable treatment outcome at 120 days. Sexual stimulation is required for nitric oxide to be released from nerves and endothelial cells directly into the penis to elicit an erection. Since erectile/endothelial damage and autonomic neuropathy may impair this pathway, likely, diabetic patients with comorbidities may not sustain the level of cGMP required to elicit smooth muscle relaxation and, therefore, an erection [1,14].
All the diabetic patients suffered from ED for more than 12 months before they presented to the clinic. A 30mg dose of tadalafil was chosen as these men did not obtain favorable sexual results with the lower approved doses. With the continuous 30mg alternated day dosing, a steady-state concentration of tadalafil would equate to approximately 1.6 times higher than that of a single dose. The relatively high serum concentration from this dosing regimen may have contributed to some of the reasons for the non-retention of treatment at 120 days. Interestingly, the higher serum concentration of tadalafil did not contribute to positive efficacy. This is in contrast to previous work that showed efficacy at 5mg of tadalafil daily [16]. The difference is attributable to the small sample size of our study.
Univariate analysis of the data presented did not demonstrate a statistically significant association between individual participant demographics and the outcome with 120-day continuous treatment. Similarly, the odds ratio derived from the logistic regression did not demonstrate an association between the selected variables and the outcome of 120-day continuous treatment retention. The large association confidence intervals are likely to reflect the effect of small sample sizes.
While the presented case series demonstrated unfavorable outcomes for diabetic patients using alternate day 30mg tadalafil, several limitations frame the presented observations. Patients' self-reported sexual satisfaction at 60 days and 120 days will affect recall bias. The obtained results from this single-site specialized medical clinic may not be generalizable to other general clinics. The severity of the men’s diabetes and duration was not assessed in this study as all men that presented to the single clinic had been previously diagnosed and treated for diabetes by other healthcare professionals not involved in the study. Medication compliance was defined as taking at least $70\%$ of the required prescribed doses between visits. This was based on previous work by Hartzichristou D et al. [ 16]. $65.4\%$ of patients were not compliant with the prescribed dosing leading to unfavorable outcomes at 120 days.
As an observational case series, this study did not intend to analyze the effectiveness of 30mg tadalafil alternate daily dosing in diabetic patients with ED. Case series are descriptive and do not set out to test hypotheses related to efficacy. Large numbers and effect sizes are needed to make positive observations regarding outcomes in case series. However, case series reports have been recognized as a utility in improving case definition, providing clues, generating hypotheses, and informing follow-up studies [22].
## Conclusions
As diabetes continues to grow more prevalent in the western world, ED in diabetic patients is coincidingly a growing concern. PDEIs have lower efficacy in diabetic men due to impaired endothelium-derived factors in penile arteries and underlying endothelial dysfunction. Therefore, to address the sexual well-being of diabetic patients with ED, the optimal treatment for ED needs to be investigated.
In summary, our retrospective case series study found that $82.5\%$ of diabetic patients were not satisfied with treatment with alternate dosing of 30mg tadalafil to treat their ED at the end of the 120-day treatment period suggesting an alternative treatment plan.
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|
---
title: 'Effect of Metformin on the Prognosis of Gastric Cancer Patients with Type
2 Diabetes Mellitus: A Meta-Analysis Based on Retrospective Cohort Studies'
authors:
- Lingna Li
- Jianing Huang
- Tongmin Huang
- Jie Yao
- Yeyuan Zhang
- Meiling Chen
- Haojie Shentu
- Haiying Lou
journal: International Journal of Endocrinology
year: 2023
pmcid: PMC10008112
doi: 10.1155/2023/5892731
license: CC BY 4.0
---
# Effect of Metformin on the Prognosis of Gastric Cancer Patients with Type 2 Diabetes Mellitus: A Meta-Analysis Based on Retrospective Cohort Studies
## Abstract
### Background
Metformin is one of the most common drugs for type 2 diabetes mellitus (T2DM) treatment. In addition, metformin intends to have a positive effect on the prognosis of several cancers. However, the therapeutic effect of metformin on gastric cancer (GC) remains controversial. This study explores and updates the therapeutic effect of metformin in GC patients with T2DM.
### Methods
We searched through PubMed, Embase, Web of Science, and the Cochrane Library for relevant articles by July 2022. The relationship between metformin therapy and the prognosis of GC patients with T2DM was evaluated based on the hazard ratio (HR) at a $95\%$ confidence interval ($95\%$ CI). Overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) were the primary outcomes analyzed.
### Results
Seven retrospective cohort studies with a combined 2,858 patients met the inclusion criteria. OS and CSS were reported in six studies, and PFS was reported in four studies. Pooled results showed that, compared to the nonmetformin group, the prolonged OS (HR = 0.72, $$p \leq 0.001$$), CSS (HR = 0.81, $$p \leq 0.001$$), and PFS (HR = 0.70, $$p \leq 0.008$$) of the experimental group may be associated with the exposure to metformin.
### Conclusion
Metformin may have a beneficial effect on the prognosis of GC patients with T2DM.
## 1. Introduction
Gastric cancer (GC) is one of the most common cancers and the fourth leading cause of cancer-correlated death in the world [1, 2]. Every year, approximately 990,000 individuals are diagnosed with GC, resulting in 738,000 deaths. Central and South America, Eastern Europe, and East Asia have the highest incidences of GC [3]. In addition to genetic and dietary factors, helicobacter pylori (H. pylori), a Gram-negative bacteria promotes the development of GC [4]. Surgery combined with adjuvant chemotherapy is the only potentially curative treatment for GC [5, 6]. However, the 5-year-related survival rate of GC patients after surgery is between $10\%$ and $30\%$ [7]. Consequently, there is an urgent need to explore and develop other universal and effective therapies for GC treatment.
Type 2 diabetes mellitus (T2DM) is the 8th most prevalent disease globally, and the prevalence rate increased from $2.88\%$ in 1990 to $5.89\%$ in 2019 worldwide [8]. A recent review estimated that in 2012, about 293,000 cancer cases globally were attributed to T2DM [9]. Accordingly, the treatment of T2DM might be beneficial for improving the prognosis and relieving the burden of cancer [10]. Among them, metformin, a first-line pharmacologic treatment for T2DM, poses anticancer properties in an article which formally proposed that metformin could impair the metabolic plasticity and growth of tumor with the combination of hypoglycemia by modulating the PP2A-GSK3β-MCL-1 Axis at the molecular biochemical level [11–14]. Studies have shown that metformin has a favorable effect on the prognosis of several cancers including lung cancer [15–17], prostate cancer [18, 19], colorectal cancer [20, 21], breast cancer [19, 22–24], and endometrial cancer [25].
Nevertheless, whether metformin could improve the prognosis of GC patients with T2DM still remains controversial. A study claimed that metformin decreases the recurrence, all-cause mortality, and cancer-specific mortality rates of GC patients with diabetes after gastrectomy, more significantly with the increase of dose [26]. Another study stated that the use of metformin improves the overall mortality of GC patients with T2DM, but no association was found in a cancer-specific survival (CSS) [27]. A recent study revealed that metformin does not improve the prognosis of GC patients with T2DM [28]. Herein, the aim of our study is to render an update on the new data on the therapeutic value of metformin on the prognosis of GC patients with T2DM by exploring and updating correlative research systematically.
## 2.1. Search Strategy
This meta-analysis was conducted in accordance with the principles of Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) [29]. We systematically searched through PubMed, EMBASE, Web of Science, Medline, and the Cochrane Library to identify clinical articles from inception to July 2022 on the therapeutic effect of metformin on the prognosis of GC patients with T2DM. The search terms were as follows: (“Neoplasm, Stomach” OR “Stomach Neoplasm” OR “Neoplasms, Stomach” OR “Stomach Neoplasms” OR “Gastric Neoplasms” [MeSH Terms] OR “Gastric Neoplasm” OR “Neoplasm, Gastric” OR “Neoplasm, Gastric” OR “cancer of stomach” OR “Stomach cancers” OR “Gastric cancer” OR “cancer, gastric” OR “cancers, gastric” OR “gastric cancers” OR “Stomach cancer” OR “Cancer, Stomach” OR “Cancers, Stomach” OR “Cancer of the Stomach” OR “Gastric Cancer, Familial Diffuse”) and (“Metformin”) [MeSH Terms] OR “Dimethylbiguanidine” OR “Glucophage” OR “Metformin Hydrochloride” OR “Hydrochloride, Metformin” OR “Metformin HCL” OR “HCL, Metformin”) and (“Prognosis” [MeSH Terms] OR “Prognoses” OR “Prognostic Factors” OR “Prognostic Factor” OR “Factor, Prognostic” OR “Factors, Prognostic” OR “Comes” OR “outcome” OR “outcomes” OR “Survival” OR “Mortality” OR “Overall Survival” OR “OS” OR “objective response rate” OR “ORR” OR “progression-free survival” OR “PFS” OR “time to progress” OR “TTP” OR “disease-free survival” OR “DFS” OR “Recurrence-free survival” OR “RFS”). Studies were searched primarily in English. Relevant articles in references of the selected studies were retrieved as well to avoid omissions. The searches were conducted independently by two investigators. Divergences would be tackled at the discretion of a third investigator.
## 2.2. Inclusion and Exclusion Criteria
Inclusion of the included articles was based on the PICOS criterion: [1] Population: GC patients with T2DM; [2] Intervention: exposure to metformin; [3] Control: nonexposure to metformin (other antidiabetic medicines, treatment, or placebo); [4] Outcome: cancer survival indicators; [5] Studies: cohort studies, case-control studies, and cross-sectional studies.
The surplus selection criterion was defined as follows: [1] observational studies on the prognostic effect of metformin on GC patients with T2DM; [2] exposure to metformin for patients in the experimental group and nonmetformin for patients in the control group; [3] available tumor-related survival data. If the data from different publications is from the same participation, priority will be given to the most recent or systematic one.
Exclusion criterion was set as below: [1] studies without full text; [2] data results that cannot be counted; [3] protocols without specific results; [4] studies that explored multiple factors but lacked interesting outcomes.
The observational outcome indicators of prognosis included in this study were overall survival (OS), progression-free survival (PFS), and CSS.
## 2.3. Quality Assessment and Data Extraction
The quality of the selected studies was evaluated using the Newcastle-Ottawa Scale (NOS), which consists of three components: selection, comparability, and outcome. Selection included four parts: representativeness of the exposed cohort; selection of the nonexposed cohort; ascertainment of exposure; and outcomes not present at the start of the study. Outcomes included three items: assessment of outcomes; length of follow-up; and adequacy of follow-up. Studies with a total evaluation score ≥7 were of high quality [30].
Data extracted from the selected articles included author, year of publication, country of research, sample size, sex, age, body mass index (BMI) of the study participants, tumor node metastasis stage (TNM stage), metformin therapeutic scheme and mode of administration, interventions in the control group, GC therapy, and type of gastrectomy. Any disagreement between the two investigators was resolved through arbitration by a third investigator.
## 2.4. Statistical Analysis
Data analysis was performed using Stata software, version 12.0. The association between metformin therapy and the prognosis of GC patients with T2DM was analyzed based on the hazard ratio (HR) at a $95\%$ confidence interval ($95\%$ CI) by utilizing a random-effects model to increase reliability, giving consideration to potential heterogeneity between studies: the difference in the dosage of treatment received, differences at baseline, inconsistent GC staging, and nonuniform treatment of the control groups. Heterogeneity was evaluated using Chi-square test in order to determine if there was a significant difference in the selected data. I2 < $50\%$ indicated low heterogeneity, while I2 ≥ $50\%$ indicated high heterogeneity [31]. If the number of articles included is more than ten, sensitivity analysis was performed to examine the stability of the pooled results, and Begg's regression test was conducted to examine the publication bias of the results. P value less than 0.05 indicated a statistically significant difference.
## 3.1. Search Results
The initial literature search from the online databases revealed 24,367 studies. The additional searches of the reference materials in the retrieved studies generated 36 studies. After removing duplicates, 21,893 studies were retained. After reading the titles and abstracts and removing irrelevant articles, we read the full text of 586 studies for potential inclusion. Of these articles, 579 articles were further excluded: 164 were fundamental research on molecular, biochemical, or animal studies; 361 did not capture relevant outcomes; 46 were nonoriginal articles; five were duplicates; and three had unavailable data. A total of seven studies [26, 27, 32–36] were included for quantitative analysis. The flowchart of the entire selection process is presented in Figure 1.
## 3.2. Study Characteristics
Table 1 shows the basic characteristics of the seven included studies included in our meta-analysis. Two studies were conducted in China, two in Korea, and one each in America, Belgium, and Sweden. All the included articles were cohort studies published in the last six years involving a total of 2,858 patients. The metformin exposure group consisted of 1,620 patients, while the control group contained 1,238 patients. A total of 1,141 patients had stage I-II tumors, while 1,134 patients had stage III-IV tumors. All the studies adjust for factors including but not limited to sex, age, BMI, TNM stages, and gastrectomy. Detailed data on the study population are shown in Supplementary Table 1.
## 3.3. Quality Assessment
The quality assessment for the selected studies was evaluated using NOS. All the studies had NOS scores ranging from 7 to 9, indicating high quality and low risk of bias. A specific assessment with perspective aspects is shown in Supplementary Table 2.
## 3.4. Prognostic Analysis
Six studies reported the HRs of the relationship between metformin and the OS of the GC patients with T2DM. Pooled results reveal that compared with the control group, the OS rate of the metformin exposure group was significantly high (HR = 0.72, $95\%$ CI: 0.59–0.87, $$p \leq 0.001$$). The heterogeneity of the pooled results was significant (I2 = $85.6\%$). The detailed results are shown in Figure 2.
Six studies reported the relationship between metformin and CSS in GC patients with T2DM. The pooled results revealed that the CSS rate was higher in the metformin exposure group than in the control group (HR = 0.81, $95\%$ CI: 0.71–0.91, $$p \leq 0.001$$). The heterogeneity among the studies was very low (I2 = $29.8\%$). The details are shown in Figure 3.
Four studies reported on the PFS. Pooled analysis indicated that metformin increased the PFS of GC patients with T2DM compared with the control group (HR = 0.70, $95\%$ CI: 0.54–0.91, $$p \leq 0.008$$). However, the heterogeneity among the studies was very high (I2 = $82.2\%$) (Figure 4).
## 4. Discussion
Metformin, a first-line treatment for T2DM, has been considered to have an improved effect on the prognosis of cancer patients. However, before this study, the role of metformin in GC remained controversial. This study summarized the current data on the effect of metformin on the prognosis of GC patients with T2DM. Our study showed that metformin might be beneficial for the OS of GC patients with T2DM. Since all the included patients had T2DM, the prognostic effect of metformin may stem from the treatment of diabetes. CSS was further analyzed to rule out this potential factor, implying that metformin may have a positive effect on the CSS of GC patients with T2DM. Additionally, we found that metformin might contribute to a longer PFS in GC patients with T2DM.
To explore mechanisms for the influence of metformin on GC, potential causes were listed as follows: First, T2DM promotes GC progression and related mortality [33]. Therefore, treating T2DM may improve GC prognosis. In T2DM patients, the hyperglycaemic environment provides sufficient energy for the growth and proliferation of GC cells and inhibits apoptosis [37]. A common complication of diabetes mellitus is nerve injury, and persistent hyperglycemia will lead to neuronal damage and promote the perineural metastasis of cancer cells [38]. So metformin reduces hyperglycaemic to limit the proliferation and growth of GC cells by inhibiting hepatic gluconeogenesis and stimulating peripheral glucose uptake. Otherwise, metformin lowers insulin levels in GC patients. This inhibits insulin binding to the insulin receptor (IR) [39]and refrains from activating the insulin-like growth factor (IGF) receptor signalling axis [40, 41], therefore inhibiting tumor growth.
Second, from the cellular metabolism perspective, metformin inhibits the development of GC cells by inhibiting cellular autophagy, glutaminase activity, mitochondrial function, nicotinamide adenine dinucleotide (NADH) dehydrogenase, and inducing phosphorylation of epigenetic enzymes. Metformin inhibits cellular autophagy that supports tumor survival and growth by reducing GC intracellular metabolism in the terminal stage [42, 43] and protects against hypoxia and nutrient deficiency [44].Metformin impairs glutaminase activity [45] and reduces the proliferation of GC cells. A key metabolic characteristic of many cancer cells is their high dependence on glutamine. Metformin impairs mitochondrial activity, inhibits cellular respiration, and subsequently induces GC cell death [46].Under hypoglycemic conditions, metformin strongly induces cell death by inhibiting the NADH dehydrogenase. This breaks the electron transport chain, and the anticancer effect may be greatly enhanced [47].Metformin phosphorylates and inhibits the function of several epigenetic enzymes, such as histone acetyltransferases (HATs), class II histone deacetylases (HDACs), and DNA methyltransferases (DNMT). Epigenomic modifications of metformin enhance its anticancer properties [48].
Third, immunologically, metformin exerts antitumor activities by increasing CD8+ T cells [49, 50], inhibiting apoptosis of CD8+ tumor-infiltrating lymphocytes [49, 51], and neutralizing immune-inhibitory cell populations in the tumor microenvironment [52] to realize effective cancer immunotherapy. Additionally, metformin slows the rate of GC cell transformation rate by inhibiting mediators of the inflammatory response, including transcription factors and inflammatory molecules [53].
More importantly, in terms of molecular pathways, adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) is a crucial pathway that regulates the development of GC cells. The AMPK pathway is activated in two ways. First, metformin directly activates AMPK, thereby inhibiting the downstream Akt/mTOR signalling pathway and resulting in the generation of cancer cells [54–56]. Therefore, inhibition of the PI3K/Akt/mTOR pathway disrupts cancer cell proliferation [57, 58]. Metformin also activates AMPK by inhibiting and impairing the function of mitochondrial complex I [59] and inhibits cellular cell respiration and later triggers GC cell death [46].
Activation of AMPK triggers a series of reactions: [1] the expression of p53, an antioncogene in humans [60]; [2] inhibition of the expression of the fatty acid synthase (FAS) gene, modulating lipogenesis and inhibiting proliferation [57, 61], stemness, and chemoresistance [62] of GC cell; and [3] regulation of cyclin D1 and the cyclin-dependent protein kinases p21 and p27, which contribute to their anticancer effects [63, 64]. The antitumor effects of metformin on GC and peritoneal metastases in vitro relies on pathways related to the AMPK pathway [65].
This study presented that metformin may improve the prognosis of GC patients through the above mechanisms. Although one study reported no association between the use of metformin and GC mortality in GC patients [66], metformin tended to exert a positive effect at different stages of GC. At the stage of gastric disease, metformin can slow down the progression of gastric disease due to H. pylori infection, suggesting that metformin may be beneficial for early gastric disease stage [67]. During the chemotherapy period for GC, metformin, in combination with oxaliplatin could inhibit the proliferation of GC cells and further induce their apoptosis [68]. In a GC cell line model, metformin in coordination with curcumin increased the cytotoxic effects of anticancer drugs on GC cells [69]. Metformin can also replace specific small interfering RNA (siRNA) to inhibit the migration of human gastric adenocarcinoma (AGS) cell lines, a type of GC cells [70]. Generally, numerous studies showed positive results and demonstrated the beneficial effect of metformin on GC patients.
This article is an updated meta-analysis on the prognostic impact of metformin on GC patients with T2DM. The results of our study, including three prognostic indicators—OS, CSS, and PFS, are all statistically significant. However, this study has some limitations as well.
First, the pooled results of OS and PFS are of high heterogeneity. As for the reasons, we have considered the following aspects:In terms of exposure time and dose of metformin, the specific dose and length of exposure time in the experimental group may have influenced the results. Unfortunately, some articles did not mention the dose of metformin given to the experimental group, and two articles that studied the dose relationship had different dose classifications and exposure time was not mentioned, so we could not conduct an in-depth study. In aspects related to blood glucose, the control of blood glucose by metformin may affect the prognosis of patients. Metformin may have a better effect on controlling the glycemic situation better than other hypoglycemic drugs. Unfortunately, due to the insufficient number of articles and the lack of relevant data, we cannot further study the relationship between the prognosis of gastric cancer patients and the blood glucose of patients, and we cannot exclude the possibility that it is because the metformin group had probably a shorter diabetes duration. If available, insulin resistance and the administration of other hypoglycemic drugs were also supposed to be analyzed. Eternal time bias may also explain the source of heterogeneity. For patients assigned to the experimental group, if gastric cancer progression or even death occurred before taking metformin, their progression and mortality from gastric cancer would be underestimated, and the prognostic effectiveness of metformin would be overestimated. Therefore, studies need to be designed to ensure that the three time points (follow-up initiation, compliance with inclusion criteria, and exposure allocation) are uniform. Unfortunately, the seven included cohort studies did not provide sufficient data for us to calculate the magnitude of the immortal time bias, so we could not know the specific impact of the immortal time bias on the pooled results. Therefore, we used a random-effects model to combine effect sizes in the analysis of the data to improve the precision of the estimated confidence intervals and increase the power of the test.
Secondly, in perspective of study regions, three of these seven included articles are from China, two are from Korea, and two are from Europe. So the pooled results tend to show that metformin may improve OS, CSS, and PFS in Asian populations, and more studies are still needed to verify the PFS in European populations.
## 5. Conclusion
The pooled results of this meta-analysis showed that metformin may have a positive effect on the prognosis of GC patients with T2DM. However, further multicenter studies of larger-scale clinical trials are still needed to validate the findings of this meta-analysis.
## Data Availability
The data supporting the findings reported in this study are available in the supplementary materials.
## Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
## Authors' Contributions
Each author contributed significantly to concept and development of the present paper. HL designed the research process. LL and JH searched the database for corresponding articles and extracted useful information from the articles above. JY and YZ used statistical software for analysis. TH drafted the meta-analysis. MC and HS polished this article. All authors had read and approved the manuscript and ensured that this was the case.
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|
---
title: Change in Serum Uric Acid is a Useful Predictor of All-Cause Mortality among
Community-Dwelling Persons
authors:
- Ryuichi Kawamoto
- Asuka Kikuchi
- Daisuke Ninomiya
- Teru Kumagi
journal: International Journal of Analytical Chemistry
year: 2023
pmcid: PMC10008114
doi: 10.1155/2023/7382320
license: CC BY 4.0
---
# Change in Serum Uric Acid is a Useful Predictor of All-Cause Mortality among Community-Dwelling Persons
## Abstract
There is limited research on the association between longitudinal variability in serum uric acid (SUA) and all-cause mortality in the general population, although recent studies have suggested that changes in SUA are associated with all-cause mortality in adults. This study aims to examine the association between percentage change in SUA (%dSUA = 100 × (cohort 2 SUA − cohort 1 SUA)/(time × cohort 1 SUA) and all-cause mortality. This study is based on 1,301 participants, of whom 543 were male (63 ± 11 years) and 758 were female (63 ± 9 years). We obtained adjusted relative risk estimates for all-cause mortality and used a Cox proportional hazards model, adjusted for possible confounders, to determine the hazard ratio (HR) and $95\%$ confidence interval (CI) of %dSUA. Of all the participants, 79 ($6.1\%$) were deceased, and of these, 45 were male ($8.3\%$) and 34 were female ($4.5\%$). The multivariable-adjusted HRs ($95\%$ CI) for all-cause mortality for the first, second to fourth (reference), and fifth %dSUA quintiles were 3.79 (1.67–8.48), 1.00, and 0.87 (0.29–2.61) for male participants and 4.00 (1.43–11.2), 1.00, and 1.19 (0.46–3.05) for female participants, respectively. Participants with a body mass index of <22 kg/m2 had a significantly higher HR, forming a U-shaped curve for the first (HR, 7.59; $95\%$ CI, 2.13–27.0) and fifth quintiles (HR, 2.93; $95\%$ CI, 1.05–8.18) relative to the reference. Percentage change in SUA is independently and significantly associated with future all-cause mortality among community-dwelling persons.
## 1. Introduction
Uric acid is the final oxidation product of purine metabolism in humans. Xanthine oxidase is critical for producing uric acid since it breaks down purine nucleotides. According to experimental and epidemiologic research, increased levels of serum uric acid (SUA) are associated with hypertension [1–3], metabolic syndrome [4, 5], and cardiovascular disease (CVD) incidence [6, 7]. Furthermore, it detrimentally affects the longevity of individuals with these conditions. Some studies have indicated that the relationship between SUA levels and mortality is U-shaped for both genders [8, 9], particularly females [10], whereas others have suggested a U-shaped relationship for both genders [11–13], specifically males [10]. These inconsistent results can be attributed to differences in factors such as gender, age, race, body mass index (BMI), medication, renal function, underlying diseases, and stage of the disease. Furthermore, most studies are based on a single baseline SUA measurement and thus may not reflect the association between mortality and risk of chronic SUA exposure [14].
More recent clinical observations have shown that increased variability in BMI [15], blood glucose (BG), and SUA [16–20] are associated with an increased risk of all-cause mortality. Variations in hemodynamic and metabolic parameters have been associated with poor prognosis. Tian et al. [ 20], for example, show an independent association between variability in SUA levels and greater risk of all-cause mortality, irrespective of baseline SUA and the direction of variability in the general population. However, it is not known whether this presumed relationship varies with the change in SUA levels.
Therefore, the purpose of our study was to examine the relationship between changes in SUA levels and potential risk factors such as gender, BMI, hypertension, hyperglycemia, lipids, renal dysfunction, and all-cause mortality using cohort data for community-dwelling persons.
## 2.1. Study Design and Participants
This research is a prospective cohort analysis that is based on data from the Nomura study, conducted in 2002 (cohort 1) and 2014 (cohort 2) [21]. The participants were rural residents of Seiyo City who underwent a community-based annual health examination. A flowchart of participant enrollment and exclusion is presented in a previous study [21]. In brief, 3,553 participants aged 19–90 years, of whom 1,573 were male and 1,980 were female, enrolled for a community-based health examination. The data collected included demographic and clinical indicators such as age, gender, smoking habits, alcohol consumption, CVD history, medical history, and the results of clinical examinations and laboratory tests. Followup studies were conducted after 19 years for the first group and seven years for the second. The participants' survival status was obtained from the Japanese Basic Resident Registry. For this study, participants in both cohorts who underwent followup examinations for changes in SUA levels were included. The first cohort included 651 such participants and the second included 650. Data for both cohorts ($$n = 1$$,301) were analyzed. All participants were in the age range of 24–88 years when they enrolled in the study. The study was reviewed and approved by the institutional review board of Ehime University Hospital [1903018]. All participants provided written informed consent.
## 2.2. Measurement of Percentage Change in SUA
Measurements of SUA levels obtained during the first visit are denoted SUA1, and those obtained during the second visit are denoted SUA2. The percentage difference between the two values is represented by %dSUA, which is estimated as 100 × (SUA2 − SUA1)/{time (in years) × SUA1}.
## 2.3. Evaluation of Risk Factors
The baseline anthropometric indices measured were weight and height. The participants' BMI was calculated as weight (kg) divided by height squared (m2). Smoking status (pack-years) was the product of the number of years a participant had been a smoker and the average number of packs per day. Based on this measure, the participants were categorized as nonsmokers, ex-smokers, light smokers (<20 pack-years), or heavy smokers (>20 pack-years). Similarly, daily alcohol intake was estimated based on the Japanese liquor unit (22.9 g ethanol, equivalent to a bottle of sake). Participants were classified as nondrinkers, occasional drinkers (<1 unit/day), daily light drinkers (1-2 units/day), or daily heavy drinkers (2-3 units/day). No participants drank more than 3 units/day. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured twice for each participant using an automatic oscillometric blood pressure recorder (BP-103i; Colin, Aichi, Japan). Prior to the measurement, participants were requested to rest for at least five minutes and remain in a seated position. We used the means of two measurements in our analysis. The participants were also asked to fast overnight, so that blood samples collected could be examined for triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), SUA, BG, and creatinine (Cr). The estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology collaboration (CKDepi) equation (mL/min/1.732 m2) with the following coefficients, calculated for the Japanese population. Males: Cr ≤ 0.9 mg/dL, 141 × (Cr/0.9)−0.411 × 0.993age × 0.813; Cr > 0.9 mg/dL, 141 × (Cr/0.9)−1.209 × 0.993age × 0.813; females: Cr ≤ 0.7 mg/dL, 144 × (Cr/0.7)−0.329 × 0.993age × 0.813; Cr > 0.7 mg/dL, 144 × (Cr/0.7)−1.209 × 0.993age × 0.813 [22].
Participants with SBP ≥140 mmHg and DBP ≥90 mmHg or who were taking antihypertensive medication were classified as having hypertension. Participants with TG levels ≥150 mg/dL were classified as having hypertriglyceridemia; those with HDL-C levels <40 mg/dL were considered to have low HDL cholesterol; and those with LDL-C levels ≥140 mg/dL or on antidyslipidemic medication were categorized as having hyper LDL-cholesterolemia. Participants with BG ≥ 126 mg/dL or on antidiabetic medication were classified as diabetic. Males with SUA ≥6.0 mg/dL and females with SUA ≥5.0 mg/dL were classified as having hyperuricemia [9]. An eGFR of <60 mL/min/1.73 m2 was considered an indicator of chronic kidney disease (CKD). Ischemic heart disease, ischemic stroke, and peripheral vascular disease were classified as CVD.
## 2.4. Statistical Analysis
We conducted the statistical analysis using IBM SPSS Statistics (version 26.0; SPSS, Chicago, IL, USA). Normally distributed continuous variables were expressed as mean ± standard deviation (SD), and non-normally distributed variables (e.g., TG and BG) were expressed as median and quartiles. Log-transformed values were used for parameters with nonnormal distributions. The participants were divided into three groups according to %dSUA quintiles (quintile 1: <−$1.88\%$/year; quintile 2–4: −1.87 to $2.50\%$/year; quintile 5: ≥$2.51\%$/year). Categorical variables were compared by conducting a chi-square analysis, and continuous variables were compared by performing a student's t-test on normally distributed variables. A Cox proportional hazards regression was to investigate the factors associated with all-cause mortality and model the relationships between %dSUA and all-cause mortality. We used age as the time axis and adjusted for baseline characteristics and confounding factors such as age, BMI, smoking and drinking habits, history of CVD, hypertension, hypertriglyceridemia, low HDL cholesterol, hyper-LDL cholesterolemia, diabetes, and CKD. Consistency in the observed association between %dSUA and all-cause mortality was determined by performing subgroup analyses. All significant confounding variables except the effect and effect variables were adjusted for in the interaction tests. All p values were two-tailed, and $p \leq 0.05$ was considered significant.
## 3.1. Participants' Baseline Characteristics According to %dSUA Quintiles
Male participants accounted for $41.7\%$ [543] of the total sample (1,301 participants). The mean (±SD) age for male participants was 63 (±11) years, and that for female participants was 63 (±9) years. As shown in Table 1, participants in the first %dSUA quintile were older; were more likely to be smokers; were less likely to be drinkers; had a higher prevalence of hypertension, hyper LDL-cholesterolemia, CKD, and hyperuricemia and were more likely to be on antihypertensive and antilipidemic medication. The results also revealed that higher SUA levels were associated with lower %dSUA quintiles. There was no significant association, however, between %dSUA quintiles and the prevalence of CVD, hypertriglyceridemia, lower HDL-cholesterolemia, or diabetes.
## 3.2. Kaplan–Meier Curve for All-Cause Mortality According to %dSUA Quintiles by Gender
A total of 79 subjects were reported to have died during the median followup period of 10.7 years (interquartile range: 7.3–19.1). The incidence rate for all-cause mortality decreased from 8.46 per 1,000 person-years for the lowest quintile to 4.20 for the second to fourth quintiles and 3.45 for the highest quintile. In Figure 1, Kaplan–Meier estimates suggested that individuals of both genders in the first %dSUA quintile were at a higher risk of all-cause mortality than other participants during the 10.7-year followup period (log-rank test, p ≤ 0.001).
## 3.3. Hazard Ratios for All-Cause Mortality According to %dSUA Quintiles by Gender and BMI
As shown in Table 2, for both genders, the model indicated that participants in the first %dSUA quintile were at a significantly higher risk of all-cause mortality than those in the second to fourth %dSUA quintiles, which were used as the reference. The multivariable-adjusted HRs ($95\%$ CI) for all-cause mortality across the first, second to fourth, and fifth %dSUA quintiles were 3.79 (1.67–8.48), 1.00, and 0.87 (0.29–2.61) for male participants, and 4.00 (1.43–11.2), 1.00, and 1.19 (0.46–3.05) for female participants.
## 3.4. Hazard Ratios for All-Cause Mortality According to %dSUA Quintiles by Subanalysis
In Table 3, participants were stratified by age (<65 and ≥65 years), BMI (<22.0 and ≥22.0 kg/m2), medication (absence and presence of antihypertensive, antidyslipidemic, or antidiabetic medication), eGFR (<60 and ≥60 mL/min/1.73 m2/y), and SUA (<6.0 and ≥6.0 mg/dL for males and <5.0 and ≥5.0 mg/dL for females). Overall, the results showed that the first %dSUA quintile was significantly associated with a higher risk of all-cause mortality. Participants with a BMI of <22.0 kg/m2 exhibited significantly higher HRs, forming a U-shaped curve for the first (HR, 7.59; $95\%$ CI, 2.13–27.0) and the quintiles (HR, 2.93; $95\%$ CI, 1.05–8.18), compared with the reference (the second to fourth quintiles). For participants with BMI ≥22.0 kg/m2, the multivariable-adjusted HRs ($95\%$ CI) for all-cause mortality across the first, second to fourth, and fifth quintiles of %dSUA were 3.08 (1.46–6.48), 1.00, and 0.42 (0.13–1.42), respectively.
## 4. Discussion
This prospective followup study was designed to examine the relationship between potential confounders, including percentage change in SUA levels and all-cause mortality, using data for 1,301 community dwellers. The SUA levels were measured twice for each participant, and data for all-cause mortality were obtained from Japan's Basic Resident Registry. The results indicated the existence of a significant and independent association between a decrease in SUA and all-cause mortality. In participants with a BMI of <22.0 kg/m2, we observed a U-shaped relationship, in that a positive %dSUA was associated with a significantly higher HR for all-cause mortality. To the best of our knowledge, few studies have indicated percentage change in SUA as an important risk factor for all-cause mortality among community-dwelling persons [20].
There are not many reports of SUA fluctuations playing an important role in increasing disease risk [18–20]. Reduced SUA levels in patients with gout may be associated with a lower risk of renal function decline but not with a lower risk for diabetes or CVD [23]. A study conducted on 3,604 male participants aged 45–74 years who enrolled in one of the three MONICA Augsburg surveys during 1984–1995 reported 809 total deaths [24]. A Cox model comparing the extreme quartiles of SUA distribution and all-cause mortality reported an HR of 1.40 ($95\%$ CI, 1.13–1.74) after adjusting for conventional CVD risk factors and diuretic intake. Tian et al. [ 14] examined 63,127 participants without a history of CVD and showed that changes in SUA at either extreme were associated with a higher risk of all-cause mortality. Their study showed HRs ($95\%$ CIs) of 1.15 (1.02–1.29) and 1.20 (1.06–1.35) for the first and fifth quintiles. Our research shows that a decrease or increase in SUA of more than $20\%$ is associated with an increased risk of all-cause mortality. In 309 peritoneal dialysis patients who were not on SUA-lowering medication, there was a higher mortality rate among those whose SUA levels dropped (19 out of 86) than among those whose SUA levels were nondecliner (3 out of 86; $p \leq 0.001$). Furthermore, a Cox regression analysis revealed SUA decline as an independent risk factor for all-cause mortality [16]. Savarese et al. [ 25] conducted a meta-regression analysis on data for 21,373 participants who were part of 11 trials with a mean follow-up period of 2.02 ± 1.76 years, which included 4,533 cardiovascular events. This analysis revealed no relationship between change in SUA from baseline to the end of the followup period and the study's composite outcome or all-cause mortality [25]. It is therefore yet to be convincingly established whether long-term fluctuations in SUA are associated with the risk of all-cause mortality in the general population.
In addition, compared with participants who had a stable SUA, those whose SUA dropped dramatically were older; had a higher BMI and BG; had a higher incidence of hypertension, hyper LDL-cholesterolemia, and hyperuricemia; and had lower eGFR values. These parameters are indicative of cardiometabolic disorders, systemic inflammation, and poor renal function and may have an important influence on the pathogenesis of mortality [15, 17, 26, 27]. The relationship between %dSUA and these parameters may suggest a pathway via which SUA variability affects the risk of all-cause mortality.
Research is yet to provide a comprehensive understanding of the mechanisms underpinning increased all-cause mortality in individuals with fluctuating SUA levels. Uric acid is catalyzed by the enzyme xanthine oxidase, which is harmful to free radicals and has dual pro-oxidant and antioxidant properties [24]. Thus, excessive oxidative stress due to varying SUA levels may lead to induced endothelial dysfunction, affect the extent of activation of the renin-angiotensin system and indirectly contribute to the increased risk of all-cause mortality [28, 29]. In addition, it is known that a rapid increase in SUA increases the percentage of urate crystallization and promotes immune and inflammatory responses [30]. Moreover, studies have shown that SUA values are positively correlated with albumin and negatively correlated with the Charlson Comorbidity Index [31]. Thus, the increased risk of mortality associated with low SUA values could be attributable to a poor nutritional status associated with hypoalbuminemia, and the more severe this comorbidity is, the higher the risk of all-cause mortality. In our study, we observed a significantly higher HR for participants with a BMI of <22.0 kg/m2, forming a U-shaped curve. That was the greater the percentage increase and decrease in SUA, which showed fluctuating SUA levels, the higher the mortality rate.
Further research is needed to identify a clear mechanism to explain this. A key contribution of this research is the prospective design that can be attributed to the long-term study period, which included the followup analyses. Other advantages include measurements of SUA variability, adjustment for several possible confounding factors, and the inclusion of sensitivity analyses. However, our study is also subject to several limitations. First, the sample consisted primarily of relatively healthy middle-aged and older adults (mean age 68 ± 10 years) who lived in rural areas of Japan, where the population is rapidly aging, and who participated in health examinations. In addition, the subjects were cohort participants, and two medical examinations were needed to obtain data on their SUA variability. As a result, only a third of the cohort subjects were included in our analysis, so selection bias cannot be ruled out. Second, we used the all-cause mortality rate as the outcome, based on Japan's Basic Resident Register. However, this register does not contain data on participants who left the region during the study period, limiting the possibility of followups. Third, future research should consider the impact of changes in confounding factors, medication, underlying diseases, and lifestyle, both at baseline assessments and during followup periods. Fourth, we assessed renal function based only on eGFR and not using data on urinary albumin or protein. Finally, the relatively small number of participants and deaths may have weakened the causal relationship between %dSUA and all-cause mortality.
## 5. Conclusion
This study demonstrated that a decreased percentage change in SUA is strongly associated with all-cause mortality irrespective of gender and baseline covariates among community-dwelling persons. In addition, it revealed a relationship between increased percentage change in SUA and a significantly higher HR of all-cause mortality in participants with a BMI of <22 kg/m2. These findings highlight the importance of achieving stable SUA levels and avoiding large fluctuations in SUA levels and may inform the design of future studies to identify and treat true high-risk populations. Further research is needed to evaluate the reproducibility of our results and to further elucidate associations among the tested conditions.
## Data Availability
The datasets analyzed in this study can be available by the corresponding author (Ryuichi Kawamoto, [email protected]) upon reasonable request. The figure data and related data used to support the findings of this study are included within the article.
## Ethical Approval
The study was approved by the ethics committee of the Graduate School of Medicine, Ehime University [1903018]. All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all participants.
## Conflicts of Interest
The authors declare that there are no conflicts of interest.
## Authors' Contributions
RK participated in the study design, performed the statistical analysis, and drafted the manuscript. RK, AK, DN, and TK contributed to the acquisition and interpretation of the data. RK conceptualized, designed, and conducted the statistical analysis. RK and TK conceived the study, participated in its design and coordination, and helped draft the manuscript. All authors have read and approved the manuscript.
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|
---
title: Ameliorative Effect of Rice Husk Methanol Extract on Liver and Kidney Toxicities
Induced by Subchronic Codeine Administration
authors:
- Chinonso U. Nnadiukwu
- Eugene N. Onyeike
- Catherine C. Ikewuchi
- Kingsley C. Patrick-Iwuanyanwu
journal: International Journal of Food Science
year: 2023
pmcid: PMC10008116
doi: 10.1155/2023/3940759
license: CC BY 4.0
---
# Ameliorative Effect of Rice Husk Methanol Extract on Liver and Kidney Toxicities Induced by Subchronic Codeine Administration
## Abstract
Background and Objective. Rice husk remains a key by-product of rice milling generated in significant amount. Accumulated evidence indicates that rice husk contains numerous bioactive compounds; however, its application is limited. This study was designed to introduce an in vivo application of rice husk extract, against opioid-induced liver and kidney injuries. Codeine was considered a psychotic inducer in this study due to its global alarming misuse recently. The hepatorenal ameliorative proclivity of rice husk extract against codeine-induced toxicity on the liver and kidney in male albino Wistar rats was examined. To this effect, thirty-six [36] albino Wistar rats of weight 100-110 g were utilized and weight-matched animals placed in 6 groups of 6 rats each. After 30 days of the combined administration of codeine and the rice husk extract, the experimental animals were assayed for basic liver and renal markers such as AST, ALP, ALT, total protein, albumin, conjugated and total bilirubin, urea, creatinine, and electrolytes (sodium, potassium, chloride, and bicarbonate). Rice husks were collected from a local rice mill, and the extraction was done with methanol. Findings. Rice husk extract (RHE) significantly ameliorated the recorded hepatic damage. More so, the extract showed a significant action on the renal markers as well. A histopathology examination of the liver and kidney tissues revealed that RHE showed a hepatorenal ameliorative potential in a dose-dependent manner. Conclusion. Phytonutrient from RH possesses a healing ability against opioid-induced hepatorenal toxicity. Thus, RH is safe for human and may be adopted to obviate and manage codeine-induced hepatorenal damage or injury. Significance and Novelty. Data on the application of RHE as a phytonutrient to combat liver and kidney injuries were demonstrated. Future studies should evaluate its potential on other organs.
## 1. Introduction
The fact that the liver and kidney play a crucial part in drug metabolism explains why practically every medicine has been linked to hepatorenal toxicity. Hepatic metabolism is primarily a system that transforms substances into products that are more readily eliminated by the kidney and typically have a lesser pharmacologic action than the original compound [1]. Drug metabolites excreted by the kidneys may have higher activity and/or greater toxicity than the original drug, and they may also cause cellular damage that results in renal failure.
Codeine, a commonly used opioid, is useful as analgesic agent for the treatment of moderately severe acute or chronic pain [2]. Codeine has no significant analgesic effect; it is only through its bioactivation by CYP2D6 leading to morphine that it can impact its analgesic effect. It is converted in the liver by an enzyme cytochrome P450 enzyme (CYP) 2D6 to morphine which itself is an active substance and 2 to 4 times more potent than codeine [3]. Further, biotransformation results in inactive metabolites, which are excreted by kidneys.
In the field of pain management, there is increasing interest in the role of genetics on drug targets and metabolism [4]. Variations in the structure of genes are called genetic polymorphisms [5]. A gene that has been altered by a polymorphism is referred to as an allele of the original gene [5]. Polymorphisms can also influence the effects or actions of medications (pharmacodynamics (PD)) brought on by receptor binding properties of a particular drug, including adverse drug effects and efficacy [6]. Polymorphisms in the CYP450 enzymes have been found to have significant effects on drug metabolism. There is a spectrum of responses after the same dose of an opioid has been given to different individuals, ranging from no pain relief to toxicity, depending on whether the individual is a poor metaboliser (PM), extensive metabolisers (EM), or ultrarapid metabolisers (UM) [7].
The impact of polymorphisms of CYP450 enzymes on opioid effects can be seen in opioids that are commonly used in clinical practice. CYP450 enzymes have been found to be directly responsible for variability in the effects of codeine (CYP2D6), tramadol (CYP2D6), fentanyl (CYP3A5), and methadone (CYP2B6) [5]. It is important to note that the CYP450 system can also play a crucial role in transforming a drug that has no intrinsic active properties into a drug that does. When this occurs, the parent compound is referred to as a prodrug. Codeine is an example of a prodrug as it requires metabolism to morphine by the CYP450 system for it to have any opioid activity or analgesia [5, 8]. The National Survey on Drug Use and Health 2019 has confirmed the massive scale of drug problem. Over $15\%$ of the adult population are into psychoactive drug substances [9]. While the widespread illicit drug use lingers, the survey also noted a major gap in health care system meeting the needs for treatment and care for individuals with drug abuse challenges. The abuse of prescription opioids such as codeine has reached an alarming rate currently in several countries.
The annual production of rice generates huge quantities of by-products such as the straw, bran, and husk. Currently, only about $20\%$ of the by-products are used for practical purposes. However, most by-products are gotten rid of either through ground burying or via burning, thereby creating environmental pollution leading to greenhouse gas emission. Several studies have proven that bioactive compounds rich in natural products exhibit various therapeutic activities [10]. The various waste products, such as broken rice, rice bran, rice straw, and rice husk, can be fully utilized by food industries and pharmacological companies as their raw materials. In recent years, this by-product from different regions of the world has been ascertained to possess high phenolic compound [11]. As per Punvittayagul et al. [ 12], rice husk possesses antioxidants and polyphenolic compounds that shields the inner materials from oxidative damage. So many nutraceuticals derived from rice husk contain significant amount of phytochemicals, minerals, and vitamins with antioxidant activities. According to Jeon et al. [ 13], phenolic compounds from RHE have shown significant antioxidant proclivity against singlet oxygen and also inhibited hydrogen peroxide-induced damage to DNA in human lymphocytes. Kim et al. [ 14] also supported the data of Jeon et al. [ 13] that RHE showed effective antioxidant actions. Several research works have shown that protective layer of seeds contains strong antioxidants such as hydrocinnamic acid derivatives, phytic acid, vanillin, flavonoids, and syringaldehyde [15, 16]. Nilnumkhum et al. [ 17] suggested that the phenolic compounds specifically vanillic acid in purple rice husk extract (PRHE) can be effective as cancer chemopreventive agent and antimutagenic agent. Chung et al. [ 18] reported that methanolic extracts from rice hulls (MERH) could possess great antioxidant activity and chemopreventive bioactive properties against initiation stage of breast cancer. Punvittayagul et al. [ 12] reported that purple rice husk extract contains phytochemicals effective for suppression of hepatocyte proliferation, apoptosis stimulation, and detoxifying enzymes in the liver. Sankam et al. [ 19] also reported that PRHE contains significant amount of tocols that could exhibit strong inhibitory effect on hepatocarcinogenesis. According to Khat-Udomkiri et al. [ 20], xylooligosaccharides extracted from RH could serve as an improved source of nutraceutical for diabetes. Johar et al. [ 21] had effectively extracted cellulose fibre from RH with the aid of acid hydrolysis action. In response to these findings, we hypothesized that concurrent treatment with rice husk extract would reduce liver and kidney toxicities during exposure to codeine. This research established the ameliorative potential of RHE on liver and kidney damages in codeine-induced Wistar albino rats.
## 2.1. Procurement of Experimental Rats
A total of thirty-six [36] albino Wistar rats with weights between 100 and 110 g were used for this experiment. The rats were procured and housed in the Department of Pharmacology, University of Port Harcourt, Rivers State, Nigeria. The rats were left for 1 week to accustom to the laboratory conditions during which they were administered normal feed (Topfeeds grower mash) and clean water. The rats were handled according to the approved experimental protocol of the University of Port Harcourt Ethics Committee with Approval No. UPH/CEREMAD/REC/MM$\frac{79}{026.}$ The animals were grouped into six groups comprising six rats per group.
## 2.2. Collection of Plant Sample
Rice husks were collected from a local rice mill at Awgu town, Enugu State, South East Nigeria, in December 2020. The husks were further ground, and the samples were filtered through a 48-mesh sieve. Rice husk powder was packed in plastic bags and taken to the laboratory. The powdered samples were kept in an airtight container till further use.
## 2.3. Preparation of Plant Extract
The powdered rice husk sample (100 g) was weighed and packed in the Whatman No. 1 filter paper. The packed sample was subjected to Soxhlet extractions with 400 ml (1: 4 w/v) of methanol as the solvent. The extraction was allowed to run for about 5-9 hours (60 to 65 cycles) until a colourless liquid solution was obtained. The resultant extract was concentrated at 40-50°C using a rotary vacuum evaporator with an ultracryostat and further dried in an electrothermal oven. The brown paste solid obtained was stored in an airtight container in the refrigerator at 4°C till future use.
## 2.4. Experimental Design
The experimental rats were grouped into six groups of six rats each. The group I rats served as the normal control and were given only normal chow and clean water throughout the experimental period. The group II rats received only high dose of codeine at 10 mg/kg body weight of rats. The group III rats received 500 mg rice husk extract/kg body weight only. The group IV to VI rats were administered 10 mg codeine/kg b.w alongside rice husk extract at three different concentrations (250 mg, 500 mg, and 1,000 mg), respectively. After 30 days of treatment, the rats were sacrificed under exposure to light ether anaesthesia and blood collected via cardiac puncture for the liver and renal function analysis. The liver and kidney of the rats were also harvested for histopathology examination.
## 2.5. Method of Analysis
Biochemical assay examined on the liver includes alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total protein, albumin, and conjugated and total bilirubin, while urea, creatinine, and electrolytes (sodium, potassium, chloride, and bicarbonate) were assayed on the kidney. These assays were conducted with their individual diagnostic kits (Fortress Diagnostics, Antrim Technology, BT41 1QS, United Kingdom) according to the manufacturer's instructions.
## 2.6. Histopathological Examination
The liver and kidney tissues were cut into sections to moderate their thickness after being fixed in $10\%$ formaldehyde to reduce bacteria load and tissue damage. Tissues were dehydrated starting with $50\%$ alcohol for two hours, $70\%$ alcohol for another two hours, $95\%$ alcohol for twelve hours (overnight), and then absolute ($100\%$) alcohol for two hours with mild agitation. After the sections have been made, the slides were stained using haematoxylin and eosin (H&E) stains. They are the most used combination of stains for routine histopathology examination. After staining, the sectioned tissues were prepared as a permanent preparation for microscopic examination by mounting the section in a suitable medium under a glass cover slip using a mountant. The slides were then viewed under a microscope at 400x magnification, and the photomicrograph was captured and interpreted accordingly.
## 2.7. Statistical Analysis
All numerical data were subjected to statistical analysis. The analysis of variance (ANOVA) of SPSS (version 25.0) was applied to the sequence of observations for the purpose of comparative analysis. Values were reported as mean ± standard error of mean (SEM), while Duncan's multiple comparisons were used to test for significant differences between the treatment groups. The results were considered significant at p values of less than 0.05 ($p \leq 0.05$), that is, at $95\%$ confidence level.
## 3. Results
The results of the liver and renal function assay recorded are presented in Tables 1 and 2, respectively. Referring to Table 1, the AST activity of the codeine control group was significantly higher ($p \leq 0.05$) than the rest of the groups with a value of 55.30 U/l. The normal control group recorded the least AST activity with 33.65 U/l. There was no significant difference in ALT activity among the groups, though the codeine control group recorded higher ALT activity than the other groups. More so, the ALP activity of the codeine control group was significantly higher ($p \leq 0.05$) than the rest of the groups. The total protein concentration of the RHE control group was significantly higher ($p \leq 0.05$) than that of the rest of the groups with the exception of the group that received codeine with 1,000 mg/kg RHE. The codeine control group recorded the least total protein concentration. The albumin concentration of the RHE control group was also significantly higher ($p \leq 0.05$) than that of the rest of the groups. The codeine control group also recorded the least albumin concentration. The total and conjugated bilirubin concentrations of the codeine control group were significantly higher ($p \leq 0.05$) than that of the rest of the groups, while the RHE control group recorded the least total and conjugated bilirubin concentrations, respectively. From the renal marker result recorded in the study and as referred to Table 2, the urea and creatinine concentrations of the codeine control group were significantly higher ($p \leq 0.05$) than that of the rest of the groups, while the RHE control group recorded the least concentrations of urea and creatinine, respectively. There was also a noticeable gradual decline of these markers with increase in RHE concentration. More so, the electrolyte level showed that sodium ion, potassium ion, chloride ion, and bicarbonate ion concentrations of the codeine control group were significantly higher ($p \leq 0.05$) than that of the rest of the groups, while the RHE control group recorded the least electrolyte concentrations. The decline in the electrolytes was also concentration dependent. The photomicrograph of the histopathology examination on the liver and kidney tissues is shown in Figures 1–12. From the photomicrograph of the hepatic tissues examined, the normal control showed a histological normal liver with intact hepatocytes (H), sinusoids (S) containing Kupffer cells, and congested hepatic artery (HA) (refer to Figure 1). The codeine control group (refer to Figure 2) showed a histological distorted liver with periportal infiltration of inflammatory cells, patent portal vein (PV), hepatic artery (HA), and bile duct (BD), though the hepatocytes and sinusoids remained intact. The RHE control group (refer to Figure 3) showed a histological normal liver with intact hepatocytes and sinusoids containing Kupffer cells and congested capillaries. The group that received both codeine and 250 mg/kg RHE (refer to Figure 4) showed a mildly distorted liver, with sinusoids, capillaries and increased inflammatory cells, intact hepatocytes, patent bile duct (BD), and congested hepatic artery. The group that received both codeine and 500 mg/kg RHE (refer to Figure 5) showed a histological normal liver with congested central vein (CV), intact hepatocytes, and sinusoids containing Kupffer cells. The group that received both codeine and 1,000 mg/kg RHE (refer to Figure 6) showed a histological normal liver with intact hepatocytes, sinusoids containing Kupffer cells, and congested central vein. From the photomicrograph of the renal tissues examined, the normal control showed a histological normal kidney with glomeruli (G) containing mesangial cells, mesangial matrix, and capillaries, patent Bowman's capsule (C), and renal tubules (T) lined by simple epithelial cells (refer to Figure 7). The codeine control group (Figure 8) showed a histological normal kidney. The RHE control group (refer to Figure 9) showed a histological normal kidney with glomeruli containing mesangial cells, mesangial matrix, and capillaries, patent Bowman's capsule spaces, and intact renal tubules. The groups that received both codeine and RHE at 250 mg/kg, 500 mg/kg, and 1,000 mg/kg (refer to Figures 10–12) showed histological normal kidneys, respectively.
The effect of RHE on hepatic tissues of codeine-administered male albino Wistar rats is described in the Figures 1–6.
The effect of RHE on renal tissues of codeine-administered male albino Wistar rats is described in the Figures 7–12.
## 4. Discussion
This study was conducted to examine the ameliorative effect of rice husk methanol extract against codeine-induced toxicity of the liver and kidney in animal model. Codeine was considered a narcotic agent due to its alarming misuse in Africa. The Federal Government of Nigeria has recently placed ban on the production and importation of codeine containing cough syrup [22]. Studies have reported the antioxidant and anti-inflammatory properties of rice husk [14, 16]. Data from present study revealed that rice husk extract was effective against liver and kidney toxicities in a dose-dependent manner. The highest ameliorative activity of RHE was recorded when administered at the highest concentration of 1000 mg/kg (refer to Tables 1 and 2). Despite wide use of codeine for years, limited records have been linked to elevated serum enzymes during clinical procedures, and there is also no evidence associating its high-dose intake to kidney and liver impairments and/or further inflammation of the organs. Hepatotoxicity induced by codeine intake is among the most commonly used model system for the screening of misuse of opioid drugs. As per Owoade et al. [ 23], codeine intake stimulated serum AST, ALT, and ALP elevation. This research revealed a significant rise in ALT, ALP, AST, and total and conjugated bilirubin and a significant decrease in albumin and total protein concentrations in the serum of the codeine control group. Salashoor et al. [ 24] posited that elevated liver enzymes could be linked to centrilobular necrosis and extreme degeneration of the liver. Thus, elevated liver enzymes recorded may reflect hepatic damage. According to Nnadiukwu et al. [ 25], elevated serum ALT is an indication of liver disease. Elevated ALP activity recorded by the codeine control group may be considered a sensitive marker of early stage of cholestasis [25]. ALT or AST is a liver-specific enzyme, and its elevated concentrations are usually linked to lot of health problems. Bilirubin is a metabolic product of haemoglobin breakdown which undergoes conjugation with glucuronic acid in hepatocytes to increase its water solubility. According to El-Gizawy et al. [ 26], bilirubin determination is used to measure hepatic function, necrosis severity, conjugation, and excretory efficiency of hepatocytes, and any abnormal increase in bilirubin levels in the serum denotes hepatobiliary illness and severe hepatocellular dysfunction. Interestingly, the recorded increased levels of these enzymes and bilirubins were decreased in the group treated with RHE. The histopathological study of the liver (refer to Figures 1–6) also revealed that high-dose codeine administration contributed to some histological distortion and inflammation of the hepatocytes. However, this was not the case with the normal control group alongside the treated groups except for the group that received RHE at 500 mg/kg that showed a histological normal liver with congested central vein. This implied that the RHE prevented the liver damage which was confirmed by the decreased concentration of the hepatic markers and reduced histopathological injury of the hepatic tissues among the treated groups. This action of RHE may be attributed to the presence of terpenoid which has the ability to effectively ease liver-related injuries and/or diseases. According to Gong et al. [ 27], plant-derived terpenoids can effectively alleviate liver fibrosis and liver injury-related diseases. Andro, a plant-derived terpenoid, could lower ALT and hepatic total cholesterol levels and reduces the expression of NLRP3 (NLR family pyrin domain containing 3) and related inflammatory factors by increasing antioxidant and anti-inflammatory activities in nonalcoholic fatty liver disease (NAFLD) mice [28]. Meanwhile, decreased total protein and albumin concentration recorded by the codeine-administered group may be linked to the reduction in hepatocytes manifested by variation on the hepatic cell membrane which in reverse may lead to low hepatic proclivity to synthesize protein and albumin [29]. These decreased total protein and albumin were enhanced by the RHE suggesting increased healing capacity of the extract on the liver. According to Okokon et al. [ 29], elevated total protein and albumin indicate the restoration of endoplasmic reticulum and ameliorative action that synthesizes protein. Kidney markers such as urea, creatinine, and electrolytes were assayed to estimate renal toxicity. According to McCann et al. [ 30], urea assay in clinical studies is crucial in estimating amino acid metabolism, its elimination via urinary excretion and nephrotoxicity of xenobiotics, whereas plasma creatinine is adopted to measure glomerular filtration rate and renal function [31]. The present study recorded significant increased urea and creatinine levels in the codeine-administered group than the rest of the groups. This can be reported as an evidence of renal injury, which may lead to impaired kidney function. Owoade et al. [ 23] recorded an increase in urea and creatinine concentrations in tramadol-administered rats. However, treatment with RHE in doses of 250 mg, 500 mg, and 1000 mg, respectively, significantly reduced the high urea and creatinine concentrations. According to Ishimoto et al. [ 32], the kidney is crucial in maintaining stable electrolyte concentrations in the blood irrespective of physiological body adjustment. The study recorded elevated plasma electrolytes (sodium, potassium, chloride, and bicarbonate) in the codeine-administered rats which were restored to normal after RHE administration. The histopathological study of the kidney showed no obvious change in the codeine-administered groups (refer to Figure 8), while the groups cotreated with codeine and RHE (refer to Figures 10–12) showed intact renal tubule, patent Bowman's capsular space, and glomeruli containing mesangial cells, mesangial matrix, and capillaries after the exposure period of 30 days. According to Monir et al. [ 33], reactive oxygen species have been reported as the hallmark mechanism for the development of kidney injury/damage via increased kidney biomarkers. Natural compounds that possess high antioxidant and anti-inflammatory effects are expected to possess a renal protective effect [34]. Several studies have reported the antioxidant effect of vitamin E against kidney injury/damage. Vitamin E was able to bind to superoxide free radicals and prevent damage caused by reactive oxidant species [35]. RHE contains significant amount of vitamin E and other vitamins. A meta-analysis by Cho et al. [ 36] reported that vitamins and analogues are effective in the prevention of kidney injury/damage. The improvement in the kidney biomarkers seen in the RHE-treated groups explains the enhanced kidney histology.
## 5. Conclusion
This study affirmed that high-dose codeine intake could lead to alteration in the biochemical indices of the liver and kidney as well as organ damage. The study also revealed that RHE contains vitamins and phytochemicals with antioxidant and anti-inflammatory properties against codeine-induced hepatorenal organ damage in rats. This research offers valuable facts on rice husk extract for application in alternative medicine and thus calls for further investigation and isolation of bioactive compounds of RHE.
## Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon request.
## Conflicts of Interest
The authors declare no conflict of interest regarding this article.
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|
---
title: 'Adherence to physical rehabilitation delivered via tele-rehabilitation for
people with multiple sclerosis: a scoping review protocol'
authors:
- Geraldine Goldsmith
- Jessica C Bollen
- Victoria E Salmon
- Jennifer A Freeman
- Sarah G Dean
journal: BMJ Open
year: 2023
pmcid: PMC10008230
doi: 10.1136/bmjopen-2022-062548
license: CC BY 4.0
---
# Adherence to physical rehabilitation delivered via tele-rehabilitation for people with multiple sclerosis: a scoping review protocol
## Abstract
### Introduction
Using tele-rehabilitation methods to deliver exercise, physical activity (PA) and behaviour change interventions for people with multiple sclerosis (pwMS) has increased in recent years, especially since the SARS-CoV-2 pandemic. This scoping review aims to provide an overview of the literature regarding adherence to therapeutic exercise and PA delivered via tele-rehabilitation for pwMS.
### Methods and analysis
Frameworks described by Arksey and O’Malley and Levac et al underpin the methods. The following databases will be searched from 1998 to the present: Medline (Ovid), Embase (Ovid), CINAHL (EBSCOhost), Health Management Information Consortium Database, ProQuest Dissertations and Theses Global, Pedro, Cochrane Central Register of Controlled Trials, US National Library of Medicine Registry of Clinical Trials, WHO International Clinical Trials Registry Platform portal and The Cochrane Database of Systematic Reviews. To identify papers not included in databases, relevant websites will be searched. Searches are planned for 2023. With the exception of study protocols, papers on any study design will be included. Papers reporting information regarding adherence in the context of prescribed therapeutic exercise and PA delivered via tele-rehabilitation for pwMS will be included. Information relating to adherence may comprise; methods of reporting adherence, adherence levels (eg, exercise diaries, pedometers), investigation of pwMS’ and therapists’ experiences of adherence or a discussion of adherence. Eligibility criteria and a custom data extraction form will be piloted on a sample of papers. Quality assessment of included studies will use Critical Appraisal Skills Programme checklists. Data analysis will involve categorisation, enabling findings relating to study characteristics and research questions to be presented in narrative and tabular format.
### Ethics and dissemination
Ethical approval was not required for this protocol. Findings will be submitted to a peer-reviewed journal and presented at conferences. Consultation with pwMS and clinicians will help to identify other dissemination methods.
## Introduction
Adherence is an important predictor of health outcomes1 with higher adherence levels associated with improved treatment success across a range of healthcare interventions and patient populations.2–4 Adherence to treatment regimes has the potential to impact healthcare costs; one striking example is where the NHS could save £500 million annually if medication adherence was improved in five key health conditions.5 This example illustrates the same impact non-adherence may have on other treatment intervention costs, including those relevant to this review in exercise and physical activity (PA) programmes.
Adherence is often used interchangeably with terms such as compliance, participation and concordance,6 despite the different meanings of these words.7 The issue is complicated further within therapeutic exercise prescription where various parameters of adherence, that is, the aspects of adherence that can be measured, have been identified.7 These parameters include the frequency of exercise (eg, repetitions or sets), the quality of the exercises performed, but also attendance which is only a proxy marker of actually doing the exercises. These authors suggest that while several parameters may be relevant to therapeutic exercise, there is a lack of consensus regarding the relevance and importance in specific contexts.7 For clarity and to encompass the multifaceted nature of the concept and parameters of adherence, within this paper the term adherence will be used, as defined by the WHO; ‘the extent to which a person’s behaviour; taking medication, following a diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider’1 (p3). For clarity within this paper, the WHO’s definitions of exercise and PA will be used, with the term physical rehabilitation describing physical exercise and PA programmes prescribed with a therapeutic purpose. PA is defined by WHO as ‘any bodily movement produced by skeletal muscles that requires energy expenditure’8 (pvii), and exercise as a subcategory of PA which is ‘planned, structured, repetitive, purposeful in the sense that the improvement or maintenance of one or more components of physical fitness is the objective’8 (pvi). Throughout this paper, the term tele-rehabilitation will refer to the range of technologies which are being used as the method of communication between the rehabilitation professional and patient,9 allowing for a double communication loop, whereby the patient’s performance is monitored and relayed to the clinician who can then respond with appropriate feedback.10 Although the beneficial effects of physical rehabilitation are well documented,11 adherence to such programmes remains problematic with adherence levels to home-based unsupervised exercise reported to be as low as $30\%$.12 As adherence is a key predictor of the effectiveness of exercise programmes,7 there is a clear need to improve adherence to physical rehabilitation programmes to maximise clinical outcomes. However, without a gold standard way to measure adherence to unsupervised exercise13 and a lack of validated measures,14 it can be difficult to interpret study results4; a poor outcome could be due to poor adherence or a genuine lack of intervention efficacy.
To improve patient adherence to physical rehabilitation, some studies have integrated behavioural change techniques (BCTs) into their programmes.15–17 BCTs are the active ingredient, or unit of change, within behavioural interventions used to modify a specified behaviour.15 18 One typical BCT for physical rehabilitation would be setting an exercise behaviour goal.17 The integration of BCTs into physical rehabilitation programmes has elicited positive results across clinical populations including people living with multiple sclerosis (pwMS).15–17 19 20 *Multiple sclerosis* (MS) is a progressive condition of the central nervous system, affecting over 130 000 people in the UK.21 Typically, people experience a myriad of sensory, cognitive and motor impairments with subsequent limitations in function,22 23 which can impact negatively on engagement in exercise and PA.24 The benefits of exercise for improving symptoms, functional abilities and quality of life for pwMS are well established25 and physical rehabilitation programmes are frequently prescribed to pwMS.21 However, pwMS are less physically active than the general population25 and often experience difficulties in attending outpatient rehabilitation appointments,26 limiting their access to physical rehabilitation advice and prescription.
Tele-rehabilitation has been proposed as a potential solution for patients who have difficulty in attending outpatient rehabilitation services,27 and has been promoted within a wider rehabilitation context during the SARS-CoV-2 pandemic28 with its use likely to continue in clinical practice in line with the Department of Health’s Long Term Plan.29 Within the context of rehabilitation, the ‘double communication loop’ is an essential element of the tele-rehabilitation as it allows clinicians to adjust or progress a patient’s exercise programme according to their performance.10 When delivering interventions via tele-rehabilitation, the interaction between clinician and patient may be synchronous (in real-time), asynchronous (not in real-time) or mixed, with the feedback received by the clinician delivered online (within the intervention) or offline (with a delay).10 30 Within rehabilitation, technology can be used in various contexts including; to enable the communication between the patient and clinician, for example, video conferencing, or as the rehabilitation intervention itself, for example, using wii fit games to improve upper limb motor function. Distinguishing between these two uses of technology is important when attempting to evaluate adherence to physical rehabilitation programmes, as using technology as the rehabilitation intervention may affect adherence.13 31 This review is interested in exploring whether adherence levels and the effectiveness of BCTs delivered via tele-rehabilitation, within the context of a physical rehabilitation programme, may differ to those delivered in face-to-face settings.
Studies investigating the use of tele-rehabilitation to deliver exercise programmes and interventions to increase PA for pwMS have sought participants’32–35 and therapists’33 views on the programmes, however, the authors are not aware of any reviews that have provided an overview of these data. Difficulties with exercise progression were reported by therapists when their communication with participants was conducted via email and online exercise diaries as they were unable to observe participants perform their exercises.33 Participants have found the tele-rehabilitation delivery acceptable,33 35 and that it increased flexibility32–34 as well as reduced the transport and physical energy costs of attending appointments.32 Systematic reviews have investigated the use of tele-rehabilitation to deliver physical rehabilitation,36 37 as well as the use of BCTs across tele-rehabilitation and in-person settings for pwMS.15 19 20 However, these reviews have either; focused on PA levels as the outcome of interest,15 19 20 did not explore adherence to the intervention,36 or included limited information detailing participants’ adherence to the prescribed physical rehabilitation programmes.37 This scoping review aims to address this gap by specifically focusing on detailing the reported level of adherence to physical rehabilitation programmes, use of BCTs and the experiences of pwMS and therapists in adhering to these tele-rehabilitation delivered programmes.
## Methods and analysis
A scoping review methodology is particularly relevant in emerging fields38 such as tele-rehabilitation; mapping the extent and nature of research and identifying research gaps.39 The methods developed for this scoping review are based on the six stage framework described by Arksey and O’Malley39 and Levac et al.38 This protocol is reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews.40
## Stage 1: identifying the research question
This scoping review aims to provide an overview of the literature regarding adherence to physical rehabilitation delivered via tele-rehabilitation for pwMS. The specific research questions identified are:
## Stage 2: identifying relevant studies
Scoping searches were used to identify free text and controlled subject heading terms for the population, intervention and outcome, and a draft search strategy for Medline (Ovid) was developed (strategy 1). As outcomes may not be reported within the title and abstract or picked up by the controlled vocabulary function,41 a further search strategy (strategy 2) without terms relating to outcomes (ie, adherence) was devised with assistance from an information specialist who provided advice regarding free text terms, appropriate databases and supplementary searching. The two strategies were then compared for duplicates. All papers identified in search strategy 1, were also identified in search strategy 2, however, search strategy 2 identified further relevant papers that were not identified by the first strategy. A search strategy without terms relating to outcomes will therefore be used within the scoping review to ensure that relevant papers are not missed. Draft search strategies are presented within online supplemental file 1.
The search strategies will be adapted for the following databases: Embase (Ovid), CINAHL (EBSCOhost), Health Management Information Consortium (HMIC) Database, ProQuest Dissertations and Theses Global (PQDT), Pedro, Cochrane Central Register of Controlled Trials, US National Library of Medicine Registry of Clinical Trials, WHO International Clinical Trials Registry Platform portal. Databases will be searched from 1998 to the present day to reflect the start of the scientific publication of tele-rehabilitation studies.42 To identify relevant papers not included in bibliographic databases, the following organisation and associated conference websites will be searched: MS Society, MS Trust, Chartered Society of Physiotherapy, UK Society of Behavioural Medicine, International Society of Behavioural Medicine, Open Grey, National MS Society, Rehabilitation in MS and European Committee for Treatment and Research in MS.
A search log will be completed to record searches including the source and dates covered, platform, date of search and number of records yielded. Following study selection, the reference lists of all included studies will be searched for additional relevant papers.
## Stage 3: study selection
Papers reporting information relating to adults (18+) with MS will be included. If mixed populations are included within a study, the study will be included if separate data are reported for adult participants with MS. Information on physical rehabilitation; physical exercises, or PA prescribed for a therapeutic purpose must be included, whether this relates to individual or group delivery, as part of a multifactorial rehabilitation programme or a single intervention. Papers describing tele-rehabilitation provision of the physical rehabilitation programme meeting Laver et al’s9 and Di Tella et al’s10 descriptions will be included, whether the tele-rehabilitation is used as the sole programme delivery method or in combination with another method such as face-to-face.
The review will include papers reporting information relating to the concept of adherence as defined by the WHO.1 Information relating to adherence may comprise; a method of reporting adherence, adherence levels (eg, exercise diaries, pedometers, questionnaires, measurement scales), investigation of pwMS’ or therapists’ experiences of adherence or a discussion of adherence.
Study protocols will be excluded, with papers of all other study designs included. In order to minimise language bias, studies in all languages will be eligible with relevant studies translated to English where possible; any that we are unable to translate will be excluded from the review and reason for exclusion noted as language.
Search results will be downloaded into EndNote and duplicates removed. If there are multiple reports of the same study, these will be compared to ensure that adequate information is obtained and the study’s results are only used once. The study selection process will be piloted independently by two team members on 25 studies to check the interpretation of the eligibility criteria and consistency of use. Following the piloting process, the eligibility criteria may be refined.
One reviewer will screen the titles and abstracts of papers, with $20\%$ checked independently by a second reviewer. Papers appearing to meet the eligibility criteria and those where it is unclear from the title and abstract as to whether the criteria have been met will have their full text screened by one reviewer, with $20\%$ screened by a second reviewer. Discrepancies between the two reviewers at any stage of the screening process will be resolved via consensus and discussion with a third person. These processes meet the requirements set out by Plüddemann et al43 for the study selection stage of restricted systematic reviews and reflect the method used by other authors.44 The number of studies excluded at both stages of screening alongside reasons for exclusion will be recorded to enable the completion of a Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram and narrative summary of the screening decision process.
## Stage 4: charting the data
Data extraction will be piloted on five included studies independently by two reviewers, with processes amended as required. This piloting process will ensure that the instructions are applied consistently and the planned data extraction allows relevant characteristics of each study to be presented in the review alongside a quality assessment and details relating to the research questions and review aim.
Following the methodology in other scoping reviews and protocols,45 46 and meeting the minimum requirements for restricted systematic reviews as set out by Plüddemann et al,43 data will be extracted into a custom data extraction form by one reviewer. To reduce data bias error, partial verification ($20\%$) of data extraction will be undertaken by a second reviewer.43 Disagreements will be resolved by consensus and discussion with a third reviewer. The date of extraction and reviewer undertaking the extraction will be recorded alongside key information regarding the paper including: author; year and country of publication; aim and objectives of paper; participant/population information; methods (including study design, blinding, randomisation, time points of data collection); intervention description (including details of tele-rehabilitation methods; synchronous/asynchronous delivery, online/offline feedback, type of technology, exercise or PA interventions, BCTs, comparators); outcomes and results (including adherence measurement tools, eg, session attendance or exercise diaries, information relating to participants’ experiences); key findings and discussion points relating to the research questions. Where relevant information is missing from articles, authors will be contacted for further information where possible.
## Stage 5: collating, summarising and reporting the results
This stage will involve three distinct steps; analysis of the data, reporting of the results and outcome in relation to the research questions; and consideration of the meaning of the findings.38 The methodological quality of included studies will be assessed independently by one reviewer with $20\%$ of decisions checked by a second reviewer, both using tools from the Critical Appraisal Skills Programme suite.47 A third reviewer will be available to mediate any disagreements.
## Analysis of the data
The planned analysis of data will occur in two stages. First, an analysis of the data extracted from all included studies with regard to their characteristics and quality appraisal will be undertaken. Second, the research questions will be used to structure and organise an analysis of study findings, outcomes and interventions. Where appropriate, frequency counts will be used for information including study design, use of adherence measures (eg, exercise diary), reporting of adherence levels and intervention characteristics (eg, type of tele-rehabilitation and BCTs used). Data relating to participant adherence levels will be analysed through frequency counts to identify the number of studies reporting a level of adherence and using each measurement tool. Data relating to participants’ and prescribers’ experiences of adherence within studies will be analysed through the use of categories and themes.
## Reporting of the results and outcome in relation to the research questions
The characteristics of all included studies incorporating study design, population, interventions and quality appraisal will be presented in a table and narrative summary. Extracted data and the review’s findings will be presented in a narrative summary addressing each of the research questions, with the use of tables and diagrams to aid interpretation where appropriate. Data relating to the level of adherence described by studies (research question 1) will be reported with reference to the measurement tool used (research question 2) and adherence parameter measured to provide greater context and appropriate grouping of data relating to adherence levels.
## Consideration of the meaning of the findings
Findings will be summarised in the context of whether the review’s aims have been met and research questions answered. Discussion of the key findings will be included alongside identification of gaps in current knowledge and corresponding implications for future research. The quality assessment of papers may aid the interpretation of results48 and identification of gaps within the literature.38
## Stage 6: consultation
It is planned that consultation will involve pwMS and therapists working with pwMS in community settings (those working with pwMS in their own homes and outpatient settings). The aims of the planned consultation are to share and discuss preliminary findings, inform future research and develop dissemination methods. The discussion of preliminary findings provides the opportunity to discuss findings within the context of pwMS and clinicians’ experiences of adherence to physical rehabilitation delivered via tele-rehabilitation.
## Patient and public involvement and engagement
Patient and public involvement and engagement (PPIE) was sought in the development of the review’s aims and research questions alongside the identification of possible dissemination methods to use following the completion of the review. The PPIE was undertaken with a group of four pwMS who provided written feedback on a lay summary of the scoping review protocol. Videoconferencing was then used with three pwMS from this group to explore further their priorities and experience of the use of tele-rehabilitation. This included discussions regarding their general experiences of tele-rehabilitation use alongside specific questions regarding adherence when therapeutic physical rehabilitation programmes have been delivered via tele-rehabilitation.
The feedback regarding the importance of the proposed area of research was useful in shaping the context of the review and its research questions. The PPIE feedback has been incorporated into the protocol through formulation of a research question relating to the experiences of pwMS and identification of dissemination methods as detailed below.
## Ethics and dissemination
Ethical approval was not required in the development of this protocol as the planned methodology involves the review of publicly available data.
The findings of the review will be submitted to a peer-reviewed journal and for presentation at conferences. Further dissemination to clinicians working with pwMS will be guided by the consultation process. PPIE in the design of this protocol identified discussion with MS charities, for example, MS Trust regarding the use of their social media or website platforms and newsletters to promote key findings as an important potential method of dissemination to pwMS, their families and carers. Further PPIE input through the consultation stage of the review may identify further appropriate dissemination methods for pwMS.
## Patient consent for publication
Not applicable.
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|
---
title: A cross-sectional study of anxiety and depression caseness in female competitive
figure skaters in Sweden
authors:
- Moa Jederström
- Sara Agnafors
- Christina L Ekegren
- Kristina Fagher
- Håkan Gauffin
- Laura Korhonen
- Jennifer Park
- Armin Spreco
- Toomas Timpka
journal: BMJ Open Sport & Exercise Medicine
year: 2023
pmcid: PMC10008236
doi: 10.1136/bmjsem-2022-001491
license: CC BY 4.0
---
# A cross-sectional study of anxiety and depression caseness in female competitive figure skaters in Sweden
## Abstract
### Objectives
Little is known about figure skaters’ mental health. This study aimed to describe anxiety and depression caseness (defined as a screening condition qualifying for psychiatric examination) in competitive figure skaters and analyse factors associated with such caseness.
### Methods
A cross-sectional study was performed in April 2019 among all competitive figure skaters in the south-eastern region of Sweden ($$n = 400$$). The primary outcomes were anxiety caseness, measured using the short-form Spielberger State-Trait Anxiety Inventory and depression caseness, measured using the WHO-5 index. Multivariable logistic regression models were employed to determine the association between anxiety caseness and explanatory factors.
### Results
In total, $36\%$ ($$n = 142$$) of the invited skaters participated. Only females ($$n = 137$$), mean age 12.9 (SD 3.0) years) were selected for analysis. Of the participating skaters, $47\%$ displayed anxiety caseness and $10\%$ depression caseness. Overweight body image perception (OR 5.9; $95\%$ CI 2.0 to 17.6; $$p \leq 0.001$$) and older age (OR 1.2; $95\%$ CI 1.1 to 1.4; $$p \leq 0.005$$) were associated with anxiety caseness. Skaters reporting no caseness were younger than those reporting only anxiety caseness (mean age difference −1.9 years; $95\%$ CI −3.1 to −0.7; $$p \leq 0.001$$) or anxiety and depression caseness (OR −3.5 years; $95\%$ CI −5.6 to −1.5 years; $p \leq 0.001$).
### Conclusion
Anxiety caseness was associated with overweight body image perception and older age in female competitive figure skaters. Older skaters reported generally worse mental health. More research on the mental health of figure skaters is warranted, considering comorbidity and focusing on those needing further assessment and support.
## Introduction
In parallel to its general positive effects, sports participation may put the athlete at risk of physical and mental ill health with an impact on overall well-being and performance.1 Physical and mental health are interrelated and jointly impact athletes’ well-being and performance. For example, anxiety has been found to predict sports injury risk in soccer players,2 and injured elite athletes express more anxiety and depressive symptoms.3 In some sports, specific associations have been reported. For instance, in elite athletics, women who have experienced sexual abuse are more likely to suffer from injuries4 and express suicidal ideation.5 Similarly, adolescent and adult aesthetic athletes, such as figure skaters, ballet dancers and rhythmic gymnasts, are at increased risk of developing a distinctive pattern of health problems, including anxiety, eating disorders and injuries.6–9 A recent study of young female Swedish figure skaters showed that older age and an increased number of skipped meals per week were associated with sustaining a sports injury episode.10 However, little is known about figure skaters’ mental health. The mean age of female figure skaters competing at a high level is lower than in most other Olympic sports.11 While risk factors for mental health issues have been studied to some extent in general youth populations, previous research on the mental health of young female figure skaters has included only small samples, and skaters are often grouped with other aesthetic athletes (eg, gymnasts and ballet dancers).12 To address these gaps in knowledge, the primary aim of this study was to describe the prevalence of anxiety and depression caseness (separately and in combination) in a representative, geographically defined Swedish population of licensed competitive figure skaters. The term caseness denotes a screening condition qualifying for psychiatric examination, that is, a notion of disease predisposition that warrants clinical assessment to prevent progression to significant pathology.13 The secondary aim was to examine determinants associated with anxiety and depression caseness. Among Swedish adolescents, older age has been reported to be positively associated with the rate of mental health complaints, especially among girls.14
## Study design
This study employed a cross-sectional design. Data were collected using an online questionnaire to analyse sports injuries and assess mental health. The study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
## Setting and participants
Licensed competitive skaters from all figure skating clubs in the Swedish South-eastern Regional Figure Skating Federation (part of the Swedish Figure Skating Association, which had a population of 5000 competitive skaters in the 2018–2019 season) were invited ($$n = 400$$, age 5–31 years) to complete an online questionnaire. Clubs sent an email about the study and a link to the questionnaire to all guardians of skaters younger than 15 years old and all skaters 15 years or older. Participants received four email reminders during the response time. A previous article based on the same population that analysed sports injuries10 includes a detailed presentation of the data collection procedures and a non-response analysis. The sample was found to be representative according to sex and birthyear. Skaters from the lowest competitive level (star competitions level) were under-represented, skaters from the intermediate competitive level (club competitions level) were overrepresented and the highest competitive level (A-level and elite level) was representatively sampled.
## Ethics Statement
Ethical approval was obtained from the Regional Ethics Committee in Linkoping, Sweden (Dnr $\frac{2018}{483}$-31). The study follows the WMA Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. Informed written consent was obtained from all skaters and guardians of skaters younger than 15 years. The skaters could at any time withdraw their participation without stating a cause.
## Patient and public involvement statement
Former competitive figure skaters assisted with designing and testing the questionnaire. To test how younger skaters would perceive the questions, children not yet eligible for competition and their parents were also asked to test and evaluate the questionnaire during a pilot phase.
## Data collection
Data were collected in April–July 2019 through an online questionnaire (Lynes™). Clubs also contributed aggregated data (sex, date of birth (year and month) and competitive level) on the total population of their licensed competitive skaters.
Participating skaters completed different questionnaires based on age (</≥12 years). Parents of children <12 years of age were expected to help interpret questions if their children asked for help but to otherwise not oversee their children when participating. The web questionnaire was designed based on questionnaires previously used with athletics populations15–18 and within the 2014 study Health Behaviours in School-aged Children by the Public Health Agency of Sweden, on behalf of the WHO.19 Full details of the questionnaire have been previously published.10 In brief, the questionnaire took approximately 20 min to complete and included questions on skater characteristics and skating level, physical and mental health, and injury status.
## Outcome variables
Anxiety caseness (yes/no) was defined as a short-form Spielberger State-Trait Anxiety Inventory (short-STAI) score >12. Short-STAI is validated for adults20 and children aged 5–17 years.21 In the study in which short-STAI was developed, the authors converted short-STAI (6 questions scored 1–4; total score range 6–24) to a scale ranging from 20 to 80 for comparison with STAI-S (20 questions scored 1–4; total score range 20–80).20 In STAI-S, scores of 1–2 may indicate ‘no anxiety’, while scores of 3–4 may indicate ‘anxiety’. An STAI-S-cut-off score of >40 has been used in previous studies.22 23 This study used a corresponding cut-off score of >12 for short-STAI. By scoring >12, the responding athlete, on average, was less than moderately calm, relaxed and content and more than somewhat tense, upset and worried.
Depression caseness (yes/no) was defined as a WHO-5 score ≤40 for children between 9 and 12 years and ≤36 for adolescents between 13 and 16 years,24 and ≤50 for those ≥17 years.25 The depression caseness scores were calculated based on these age cut-offs for the WHO-5 instrument. The WHO-5 has been reported to be sensitive and specific when screening for depression.25 Each item is scored from zero (none of the time) to five (all of the time). In this study, the percentage score of the scale was used. The total index score ranges from the absence of well-being to the highest imaginable well-being.25
## Explanatory variables
Age (continuous) was self-reported by the skater, who stated the year and month of birth.
A severe sports injury episode (yes/no) was defined as any injury or pain that had occurred in connection with training or competition in figure skating that resulted in >21 days of lost or altered participation in figure skating.26 An ongoing sports injury episode (yes/no) was defined as a current injury or pain that had occurred in connection with training or competition in figure skating, which prevented the skater from fully participating in training or competition.
The number of skipped main meals per week (continuous) was indicated by combining the skater’s responses on how often they ate breakfast, lunch and dinner on weekdays and weekends, respectively.
Body mass index (BMI) was calculated from self-reports of weight and height, and for respondents <18 years of age presented as International Obesity Task Force-BMI (IOTF-BMI). This measure assesses underweight and overweight BMI values adjusted for age.27 The Syndrome of Relative Energy Deficiency in Sport (RED-S syndrome) (yes/no) was indicated if a skater reported irregular menstruation or was underweight, according to IOTF-BMI.
Body image perception (underweight/normal/overweight) was indicated based on the respondent’s categorisation of themselves.
‘Figure skating load’ (low/high) was used to attribute high skating load to those competing at the elite level or national level and/or being at a double loop or higher skating level.
Mean weekly training hours (≤6 hours/7–12 hours/≥13 hours) were indicated through self-report.
A dichotomous variable for parental education level (high/low) was created based on the skater having at least one parent who had completed postsecondary education (yes/no) and was used as an indicator of socioeconomic status.28
## Data analysis
All items related to skater characteristics, figure skating and determinants related to health status (physical and mental health) were presented descriptively using percentages for categorical variables and means and SD for continuous variables.
The low number of skaters reaching the cut-off level restricted the development of meaningful multiple models of determinants for depression caseness. Binary logistic regression analyses of determinants were therefore limited to anxiety caseness. Simple binary logistic regression analyses were initially used to identify explanatory factors associated with anxiety caseness. Then, a multivariable model was constructed by excluding the non-significant explanatory variables (p≥0.05) using backward elimination (Wald). Nagelkerke R2 was obtained for the multivariable model to estimate its accountability level. Associations with $p \leq 0.05$ were considered to be statistically significant. All statistical tests were two sided. Three additional multivariable models were analysed as a sensitivity analysis, where significant variables were removed sequentially.
The analysis of factors associated with depression caseness was restricted to age and comorbidity with anxiety caseness. The skaters were classified into four groups based on their reported short-STAI and WHO-5 scores: [1] depression caseness and anxiety caseness, [2] depression caseness and no anxiety, [3] no depression and anxiety caseness and [4] neither depression nor anxiety caseness.
The age of participants in each group was compared with analysis of variance (ANOVA) with post-hoc Bonferroni correction.
All analyses were performed using The Statistical Package for the Social Sciences (SPSS) for Windows V.27.0.
## Results
In total, 142 ($36\%$) of the skaters invited participated; only girls (137 ($96\%$)) were selected for analysis due to identification risk among boys (figure 1).
**Figure 1:** *Study recruitment flow chart.*
## Participant characteristics
The mean age of the participants was 12.9 (SD 3.0) years (range 6–23 years) (table 1). Most skaters’ parents were born in Sweden, and for the majority, at least one parent had completed postsecondary education. Most skaters lived with both guardians. Most participants ate breakfast, lunch and dinner every day. More than half of the participants exercised 7 hours per week or more outside school hours, with a majority practising figure skating between 4 and 9 hours each week. All participants were singles skaters, with $17\%$ being elite skaters or skating at the national level (A-level) and $63\%$ having landed double loop or more advanced jumps.
**Table 1**
| Unnamed: 0 | Participantsn (%) |
| --- | --- |
| Age | 6–23 years |
| Mean (SD) | 12.9 (3.0) |
| <12 years | 47 (34) |
| 12–15 years | 68 (50) |
| >15 years | 22 (16) |
| Parent’s country of origin | |
| Both parents born in Sweden | 103 (75) |
| No parent born in Sweden | 18 (13) |
| One parent born in Sweden | 16 (12) |
| Parental education level | |
| At least one parent completed upper secondary education | 15 (11) |
| At least one parent completed post-secondary education | 122 (89) |
| Housing (n=136) | |
| Living with one guardian | 9 (7) |
| Living with both guardians | 120 (88) |
| Other (alternating between guardians, with a partner, etc) | 7 (5) |
| Number of skipped main meals each week | |
| | 97 (71) |
| 1–3 skipped main meals | 22 (16) |
| 4–6 skipped main meals | 5 (4) |
| 7–9 skipped main meals | 7 (5) |
| >10 skipped main meals | 6 (4) |
| Total exercise per week (days) | |
| Every day | 7 (5) |
| 4–6 days per week | 100 (73) |
| ≤3 days per week | 30 (22) |
| Total exercise per week (hours) | |
| ≤1 hour | 5 (4) |
| 2–3 hours | 11 (8) |
| 4–6 hours | 44 (32) |
| 7–9 hours | 35 (26) |
| 10–12 hours | 28 (20) |
| ≥13 hours | 14 (10) |
| Figure skating per week (hours) | |
| 2–3 hours | 12 (9) |
| 4–6 hours | 67 (49) |
| 7–9 hours | 32 (23) |
| 10–12 hours | 19 (14) |
| ≥13 hours | 7 (5) |
| Resting days per week in the last 12 months (means) | Resting days per week in the last 12 months (means) |
| 0 days | 6 (4) |
| 1–2 days | 100 (73) |
| 3–6 days | 31 (23) |
| Discipline | |
| Singles skating | 137 (100) |
| Competitive level | |
| Elite skater/A-competitions | 23 (17) |
| Club competitions | 61 (45) |
| Star competitions | 53 (39) |
| Skating level | |
| Landed up to double toeloop | 51 (37) |
| Landed double loop and higher | 86 (63) |
About one-third of the skaters had experienced a severe sports injury episode in the past year, and nearly one-fifth had an ongoing one.10 Most skaters rated their health as ‘excellent’ or ‘good’. According to self-reported data on height and weight, using IOTF-BMI, $12\%$ were underweight and $4\%$ were overweight. Regarding pubertal status, almost half had reached menarche, of whom $42\%$ had irregular menstruation. Nearly one-third were careful not to gain weight, and one-fifth had a body image perception of being overweight. About a tenoth had been asked to gain weight (mainly by parents), and $7\%$ had been asked to lose weight (mainly by coaches) (table 2, online supplemental table 1).
**Table 2**
| Unnamed: 0 | Participantsn (%) |
| --- | --- |
| Severe injury episode in the last 12 months | |
| Yes | 42 (31) |
| No | 95 (69) |
| Ongoing injury episode | |
| Yes | 26 (19) |
| No | 111 (81) |
| Self-rated health (point prevalence) | |
| Excellent | 70 (51) |
| Good | 62 (45) |
| Fair | 5 (4) |
| Poor | 0 (0) |
| International Obesity Task Force Body Mass Index (IOTF-BMI), n=132 | |
| Underweight (IOTF-BMI ≤18.5) | 16 (12) |
| Normal | 111 (84) |
| Overweight (IOTF-BMI ≥25) | 5 (4) |
| Menarche | |
| No | 71 (52) |
| Yes | 66 (48) |
| Menarcheal age (n=63) | |
| ≤11 years | 12 (19) |
| 12 years | 15 (24) |
| 13 years | 23 (37) |
| ≥14 years | 13 (21) |
| Irregular menstruation | |
| Yes | 28 (20) |
| No/not menarche | 109 (80) |
| Weight reduction behaviour (n=90)* | |
| No, my weight is fine | 71 (79) |
| No, but I should lose some weight | 11 (12) |
| No, because I need to put on weight | 4 (4) |
| Yes | 4 (4) |
| Weight concern (n=133) | |
| Yes, careful not to gain weight | 39 (29) |
| Yes, careful not to lose weight | 9 (7) |
| No, I do not care if I gain or lose weight | 85 (64) |
| Body image perception (n=136) | |
| Underweight | 10 (7) |
| Normal | 97 (71) |
| Overweight | 29 (21) |
| Asked to adjust weight | |
| Yes, to gain weight | 13 (9) |
| Yes, to lose weight | 10 (7) |
| No | 114 (83) |
## Anxiety and depression caseness
The mean short-STAI score among the skaters was 12.5 (SD 2.1), with $47\%$ displaying anxiety caseness (score >12). The mean WHO-5-score was 62.6 (SD 17.4), with $10\%$ of the skaters reporting scores indicative of depression caseness (score ≤50 for those ≥17 years, ≤40 for children 9–12 years, ≤36 for adolescents 13–16 years). About half ($$n = 71$$, $52\%$) of the skaters reported neither anxiety nor depression caseness, 12 skaters ($9\%$) reported scores indicating caseness in both categories, while 52 ($38\%$) skaters reported only anxiety caseness and only 2 ($1\%$) skaters reported only depression caseness.
The reported anxiety caseness prevalence of $47\%$ can be compared with findings from similar studies. For example, $37\%$ of European girls aged 15 years feel nervous more than once a week.29 The observed mean WHO-5 score of 62.6 is similar to scores found in adolescent European girls participating in sports (61.7 for individual sports and 63.7 for team sports, respectively).30 Participants with anxiety and depression caseness were older than those with only anxiety caseness and those with no reported caseness. These findings are consistent with previous research and increased prevalence rates of mental health conditions during ageing through adolescence.14 It could be that young female adolescents predisposed to anxiety stay longer in sports, which warrants further investigations into personality traits such as perfectionism and competitiveness in a figure skating population.
## Factors associated with caseness
Anxiety caseness was in the simple binary logistic regression associated with older age, a previous or ongoing injury episode, the RED-S-index, skipping more weekly main meals, overweight body image perception and higher figure skating load. The determinants remaining in the multiple model were older age and overweight body image perception (table 3).
**Table 3**
| Unnamed: 0 | Simple models | Simple models.1 | Multiple model* | Multiple model*.1 | Multiple model: age excluded† | Multiple model: age excluded†.1 | Multiple model: body image perception excluded‡ | Multiple model: body image perception excluded‡.1 | Multiple model: age and body image perception excluded§ | Multiple model: age and body image perception excluded§.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value |
| Age (years) | 1.3 (1.1 to 1.5) | <0.001 | 1.2 (1.1 to 1.4) | 0.005 | | | 1.3 (1.1 to 1.5) | <0.001 | | |
| Previous or ongoing injury episode (No/Yes) | 2.5 (1.2 to 5.0) | 0.012 | | | | | | | 2.5 (1.2 to 5.0) | 0.012 |
| RED-S indicators (no/yes) | 2.1 (1.0 to 4.5) | 0.047 | | | | | | | | |
| Skipped main meals per week (continuous) | 1.1 (1.0 to 1.3) | 0.020 | | | | | 1.1 (1.0 to 1.3) | 0.036 | | |
| Body image perception: | | | | | | | | | | |
| Normal (reference category) | | <0.001 | | 0.003 | | <0.001 | | | | |
| Underweight | 2.8 (0.7 to 10.7) | 0.127 | 3.1 (0.8 to 12.1) | 0.107 | 2.8 (0.7 to 10.7) | 0.127 | | | | |
| Overweight | 9.0 (3.2 to 25.8) | <0.001 | 5.9 (2.0 to 17.6) | 0.001 | 9.0 (3.2 to 25.8) | <0.001 | | | | |
| Figure skating load (low/high) | 2.1 (1.0 to 4.3) | 0.041 | | | | | | | | |
| Mean weekly training hours: | | | | | | | | | | |
| ≤6 (reference category) | | 0.927 | | | | | | | | |
| 7–12 | 1.1 (0.5 to 2.3) | 0.771 | | | | | | | | |
| ≥13 | 1.2 (0.4 to 3.9) | 0.736 | | | | | | | | |
| Parental education level (high/low) | 1.5 (0.8 to 3.0) | 0.213 | | | | | | | | |
When in the sensitivity analysis excluding age from the multiple model, only overweight body image perception was significantly associated with anxiety caseness (table 3). Excluding body image perception from the multiple model resulted in older age remaining in the model and skipping more main meals becoming significantly associated with anxiety caseness. A previous or ongoing injury episode was introduced as a determinant when excluding age and body image perception from the model. Nagelkerke R2 was reduced from 0.28 (age and body image perception in the model) and 0.20 (either age or body image perception in the model, respectively) to 0.06 when both variables were excluded from the model (table 3).
Skaters reporting neither anxiety nor depression caseness were younger than those reporting only anxiety caseness or both anxiety and depression caseness (table 4). The group with only depression caseness was too small ($$n = 2$$) to be included in the comparative analysis.
**Table 4**
| Unnamed: 0 | Participantsn (%) | Age mean (SD) | Group comparisonMean age difference (95% CI) p value | Group comparisonMean age difference (95% CI) p value.1 |
| --- | --- | --- | --- | --- |
| | Participantsn (%) | Age mean (SD) | Anxiety caseness+depression caseness | Anxiety caseness+no depression caseness |
| Anxiety caseness+depression caseness | 12 (9) | 15.4 (3.9) | – | – |
| No anxiety caseness+depression caseness | 2 (2) | 15.0 (−)* | † | † |
| Anxiety caseness+no depression caseness | 52 (38) | 13.8 (2.6) | −1.7 (−3.8 to 0.5) p=0.186 | – |
| Neither anxiety nor depression caseness | 71 (52) | 11.9 (2.7) | −3.5 (−5.6 to −1.5) p<0.001 | −1.9 (−3.1 to −0.7) p=0.001 |
## Discussion
This study investigated the prevalence of anxiety and depression caseness among competitive female figure skaters and factors associated with anxiety and depression caseness. In the multiple model, overweight body image perception and older age were associated with anxiety caseness. Skaters reporting anxiety and depression caseness were older than those reporting no caseness and those reporting only anxiety caseness.
## Factors associated with anxiety caseness
We found a significant association between anxiety caseness and overweight body image perception in the sensitivity analysis when excluding the age variable from the multiple model. Correspondingly, age remained a significant explanatory factor when excluding overweight body image perception. This indicates a strong association between body image and reported anxiety caseness. Due to the cross-sectional study design, no conclusions can be drawn about the causality in the interaction between anxiety and body image. However, the association between overweight body image perception and anxiety caseness is concerning but not unexpected since body dissatisfaction in adolescence is associated with several psychiatric diagnoses.31 Furthermore, low body satisfaction during early and middle adolescence predicts later signs of more global mental distress (such as depressive symptoms and lower self-esteem).32 It may be associated with anxiety disorder symptoms.33 It also predicts weight-reduction behaviour.31 34 Our findings suggest that methods for identifying mental health problems at different levels need more attention. However, the observations made in this study should be interpreted in light that participating skaters rated their health more positively than the general population and were, to a lesser extent, engaging in weight reduction behaviours.35 Notably, the figure skaters in this study reported body dissatisfaction to a lesser extent than the general Swedish population reported, and they ate breakfast more often than their peers.35 *It is* notable that skaters, on the one hand, rate their health more positively than the general population. The majority report a normal IOTF-BMI, but at the same time, report a high prevalence of anxiety and depression caseness. Body image self-discrepancy has been proposed in early adolescence as a risk factor for depressive symptoms.36 In adolescents, older age has been found to moderate the link between physical activity and a body image perception of being overweight and between overweight perception and life satisfaction.37 Furthermore, it has recently been pointed out that the complexity of separating normal states related to performance issues vs a mental illness or mental disorders may not be sufficiently considered in current research.38 An individual may simultaneously express overall high well-being and symptoms of mental illness, such as anxiety.39 Increased but short-term stress reactions related to challenging sports situations, such as competitions or temporary setbacks, are a normal part of figure skating. For children and adolescents, such short-term stress reactions may also occur in relation to exams, for example, the national tests in school, which in Sweden takes place every spring. Fluctuations in mood may also be caused by training load, with a high training load associated with mood disturbances.38 Also, it is important to consider that caseness in terms of a screening condition that qualifies for psychiatric examination in no way equals a psychiatric diagnosis, which is a complex process.13 40
## Strengths and limitations
This is one of few studies targeting a total population of competitive figure skaters. The measures used (short-STAI and WHO-5) were chosen because they had been validated for all age groups (children, adolescents and adults) covered by this study, aiming to investigate the mental health of a community sample of Swedish competitive figure skaters. After this study was planned, additional measures for assessing anxiety and depression were published. For example, Gouttebarge et al presented a mental health screening measure validated for elite athletes 16 years or older.41 *In this* study, the response rate of $36\%$ is a potential source of bias, and several other factors may also have impacted our findings. The cross-sectional design introduces a possible recall bias. Moreover, in this study, irregular menstruation was considered an RED-S indicator. However, irregular menstruation may occur for other reasons, such as in the years following menarche, endometriosis or polycystic ovary syndrome. This study defined anxiety caseness as reporting a short-STAI-score >12 based on the same principle used for the STAI-S.20 This was a semantically defined cut-off score with obvious face validity, but the authors have no empirical evidence regarding associations between reported anxiety caseness based on the cut-off score and downstream clinical illness. The anxiety caseness prevalence reported may be influenced by factors not measured in this study. For example, the timing of when the questionnaire’s administration may be important. The questionnaire was sent after the competitive season had ended, but during a time of the year when most Swedish schools have final exams, which may have influenced the reported anxiety and depression caseness levels. Also, those with the worst mental health may not have been represented in this study due to no longer participating in figure skating. Another limitation is that parents of skaters <12 years of age were advised to help interpret the questionnaire if needed. This might have affected the participants' willingness to fully disclose their symptoms and may have led to under-reporting symptoms among younger skaters. A randomised sample might be more representative of figure skaters in Sweden. However, the south-eastern region contains figure skaters of all levels and figure skating clubs of all sizes and conformities. Thus, we consider the results reasonably representative of young female Swedish figure skaters.
## Future research
Our findings have several implications for future research. First, girls and young women report more internalising symptoms such as anxiety and depression than their male counterparts. Therefore, comparisons with male figure skaters are warranted. Second, there is a gender imbalance with the under-representation of female athletes in research on sports and exercise psychology,42 highlighting the importance of future research on mental health among female athletes. Third, there have been several recent reports on mental abuse and an unhealthy sports culture in figure skating. Both have potentially negative impacts, ranging from injuries and disordered eating/eating disorders to anxiety, depression and suicidal ideation.43 Therefore, studies of the figure skating culture concerning abusive behaviour and its impact on physical and mental health among young figure skaters are warranted. Fourth, prospective studies are needed to identify causal risk factors associated with adverse mental health consequences such as anxiety, depression and eating disorders.
## Conclusion
Anxiety caseness was associated with overweight body image perception and older age in female competitive figure skaters. Older skaters reported generally worse mental health. More research on the mental health of figure skaters is warranted, considering comorbidity and focusing on those needing further assessment and support.
## Data availability statement
Data are available on reasonable request. According to the Swedish Transparency and Secrecy Act, all filled-in questionnaires are public acts. The material may be shared on requests ensuring that the confidentiality demands can be handled.
## Patient consent for publication
Consent obtained directly from patient(s).
## Ethics approval
This study involves human participants and was approved by Regional Ethics Committee in Linkoping, Sweden (Dnr $\frac{2018}{483}$-31). The study follows the WMA Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. Informed written consent was obtained from all skaters and guardians of skaters younger than 15 years. The skaters could at any time withdraw their participation without stating a cause.
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|
---
title: Suppressive effects of processed aconite root on dexamethasone-induced muscle
ring finger protein-1 expression and its active ingredients
authors:
- Taishi Kondo
- Tomoaki Ishida
- Ke Ye
- Marin Muraguchi
- Yohei Tanimura
- Masato Yoshida
- Kan’ichiro Ishiuchi
- Tomoki Abe
- Takeshi Nikawa
- Keisuke Hagihara
- Hidetoshi Hayashi
- Toshiaki Makino
journal: Journal of Natural Medicines
year: 2022
pmcid: PMC10008256
doi: 10.1007/s11418-022-01606-5
license: CC BY 4.0
---
# Suppressive effects of processed aconite root on dexamethasone-induced muscle ring finger protein-1 expression and its active ingredients
## Abstract
Processed aconite root (PA), the tuberous root of *Aconitum carmichaelii* prepared by autoclaving, is a crude drug used in Japanese traditional Kampo medicine and traditional Chinese medicine for the symptoms of kidney deficiency, that is related to the muscle atrophy in modern medicine. The objective of the present study is to evaluate the effectiveness of PA on muscle atrophy and to find its active ingredients using dexamethasone-induced muscle ring finger protein-1 (MuRF1) mRNA expression in murine myoblast C2C12 cells. Dexamethasone-induced MuRF1 expression was significantly suppressed by methanol-soluble part of boiling water extract of PA in a concentration-dependent manner with its IC50 value of 1.5 mg/ml. By the activity-guided fractionations of PA extract using the partition between organic solvents and its aqueous solution, the activity of PA did not transfer into the fraction containing aconitine-type diterpenoid alkaloids but into BuOH layer. Then, we found higenamine and salsolinol as the active ingredients in PA. Higenamine and salsolinol significantly suppressed dexamethasone-induced MuRF1 expression, and their IC50 values were 0.49 and 50 µM, respectively. The contents of higenamine and salsolinol in the decoctions of commercially available fourteen PA products are 0.12 and 14 µg/ml as the average values, and varied with the coefficient of variation (CV) values of 97 and $63\%$, respectively. Higenamine also significantly suppressed dexamethasone-induced mRNA expressions of muscle atrophy F-box protein (MAFbx)/atrogin1, casitas B-lineage lymphoma-b (Cbl-b), troponin, branched-chain amino acid aminotransferase 2 (BCAT2), and Bcl-2 binding and pro-apoptotic protein3 (Bnip3). Although the quality control of PA is regulated by the contents of diterpene alkaloids, salsolinol and higenamine can be used as the marker compounds to certificate the pharmacological activities of PA.
## Introduction
Sarcopenia and frailty, a syndrome of the loss of skeletal muscle mass and strength that occurs with aging, become a common medical and social topics in aging of the population [1]. In Japanese traditional Kampo medicine and traditional Chinese medicine, the physical problems related to aging are considered as the deficiencies of kidney, which is imaginary organ to stock the so-called life energy, and it is considered that adults have been able to live by using this energy of kidney [2, 3]. Goshajinkigan is one of the Kampo formulas to supply the kidney energy to treat kidney deficiencies, and used to treat low back pain [4], diabetic complications [5], and chemotherapy-induced peripheral neuropathy [6]. In our previous animal or in vitro studies, the extract of goshajinkigan has the ability to reduce sarcopenia symptoms in senescence-accelerated mouse P8 [7], to ameliorate allodynia in chronic constriction injury model mice [8] and in streptozotocin-induced diabetic model mice [9], and to suppresses voltage-gated sodium channel Nav1.4 current in murine myoblast C2C12 cells [10] and Nav1.7 current in HEK293 cells expressing Nav1.7 [9].
It is revealed that a muscle-specific ubiquitin ligase is one of the causative genes of skeletal muscle atrophy, and that the enhancement of proteolysis mainly due to the increased activity of the ubiquitin/proteasome system is greatly involved [11]. Muscle ring finger protein-1 (MuRF1) and muscle atrophy F-box protein (MAFbx)/atrogin-1 are the specific ubiquitin ligases in skeletal and cardiac muscle, and their expressions are increased in muscle atrophy caused by sciatic nerve transection [12]. These are considered to be important factors responsible for muscular atrophy, because the excessive MAFbx/atrogin-1 expression produced atrophy in myotubes, whereas mice with the deficiencies of either MAFbx/atrogin-1 or MuRF1 gene were resistant to muscular atrophy [13].
Among crude drugs composed in goshajinkigan, processed aconite root (PA), the tuberous root of *Aconitum carmichaelii* prepared by autoclaving, has the effectiveness of to supply kidney energy [14]. PA is considered to be one of the active components of goshajinkigan for the prevention from chemotherapy-induced peripheral neuropathy and mechanical hyperalgesia in diabetic mice, and its active ingredient containing in PA is neoline [9, 15, 16]. However, the preventive effects on muscular atrophy have not been evaluated.
In this study, we evaluated the effectiveness of PA extract on dexamethasone-induced MuRF1 expression in C2C12 cells in vitro and found the active ingredients contained in processed aconite root.
## Materials
Processed aconite root (lot: F2F0243) was purchased from Uchida Wakanyaku (Tokyo, Japan). Processed aconite root (10 g) was boiled in 200 ml of water for 30 min. After filtrating and freeze-drying, methanol was added into the lyophilized powder, and centrifuged (3 × 103 rpm, 15 min), and the parts dissolved in methanol were taken to evaluate the activity. All the samples were dried up, and dissolved in DMSO. Extraction rates from the weight of each crude drug to methanol-soluble part were $13\%$.
## Isolation of salsolinol from PA extract
Processed aconite root (lot: F2F0243, Uchida; 1.0 kg) was boiled in 8 l of deionized water for 30 min. After filtering by gaze, the residue was further boiled, and this operation was repeated twice. After freeze drying the whole boiling water extract (373 g yielded), the powder was extracted with methanol for three times. After removal of the solvent under the reduced pressure, the resulting extract (124 g) was dissolved in 800 mL of acidified water at pH 3 with HCl and extracted with EtOAc (800 ml × 3). After adjusting the pH of water layer to 10 by adding NH3 solution, the water layer was extracted with EtOAc (800 ml × 3). After adjusting the pH of water layer was adjusted to 7 by adding HCl, the remained water layer was extracted with water saturated n-BuOH (800 ml × 3). The resulting extracts were concentrated under the reduced pressure to give acidic EtOAc (acidic layer, 4.0 g), alkaline EtOAc (alkaline layer, 4.2 g), n-BuOH (BuOH layer, 10 g), and water (water layer, 96 g) fractions. The BuOH layer (9.9 g) was subjected to silica gel (BW-200, Fuji Silysia, Fuji, Japan; 200 g) column chromatography and eluted with mobile phase using each 600 ml of CHCl3/MeOH mixture 10:1, 5:1, 3:1, 2:1, 1:1, 1:10, and 0:1, stepwise. At last silica gel column was washed by CHCl3/MeOH/H2O 6:4:1. The eluate was monitored by TLC (TLC Silica gel 60 F254; Merck, Kenilworth, NJ, USA) and combined to fraction (Fr.) 1 (0.39 g), Fr. 2 (1.1 g), Fr. 3 (1.1 g), Fr. 4 (1.1 g), Fr. 5 2.1 g), Fr. 6 (2.4 g), Fr. 7 (0.62 g), Fr. 8 (0.10 g), and Fr.9 (0.27 g). Fraction 3 (14 mg) was separated using preparative HPLC (column, Cosmosil 5C18-AR-II (10 i.d. × 250 mm), Nacalai Tesque, Kyoto, Japan; mobile phase, $5\%$ acetonitrile (0–5 min), 5–$100\%$ acetonitrile (5–35 min), 4 ml/min), and from the peak eluted at 13.3 min, salsolinol (2.4 mg) was obtained and identified using NMR and MS spectrometry [17].
## Cell culture
C2C12 myoblasts were purchased from Bioresources Center (Tsukuba, Japan) and maintained and proliferated at 37 °C with $5\%$ CO2 in Dulbecco's modified eagle medium (DMEM, Sigma-Aldrich, St. Louis, MO, USA), supplemented with $10\%$ fetal bovine serum (FBS, Sigma), 100 U/ml penicillin, and 0.1 mg/ml streptomycin (Nacalai Tesque, Kyoto, Japan). At a confluence of $80\%$, C2C12 myoblasts were transferred to next generation by $0.25\%$ trypsin (Sigma)/$0.02\%$EDTA⋅2Na.
## Plasmid transfection and differentiation in C2C12 cells
C2C12 cells (4.0 × 106 cells) were planted into 10 cm dish and maintained for one night. pGL3-MuRF1 was constructed in our previous study [18], and pCMVβ-gal was from Prof. Jeffrey L. Wrana [19]. After the medium was exchanged into FBS-free one, these genes were transfected into the cells using Hily Max® (Dojindo, Tokyo, Japan) and Opti-MEM® (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instruction, and the cells were maintained for 8 h. Then, the cells were harvested with $0.25\%$ trypsin/$0.02\%$EDTA⋅2Na, transplanted into 24-well plate (4.0 × 104 cells/well), and maintained for overnight. Then, the medium was exchanged into DMEM containing $2\%$ horse serum (Sigma), 100 U/ml penicillin, and 0.1 mg/ml streptomycin (differentiation medium to induce myotube formation), and the cells were incubated for 72 h.
## Luciferase assay for MuRF1 induction
The MuRF1-transfected differentiated cells were incubated with the medium containing dexamethasone (Sigma) (1 µM) with/without the extract of PA, (±)-salsolinol hydrochloride (Cayman Chemical, Ann Arbor, MI, USA), and higenamine (Chengdu Must Bio-Technology, Chengdu, China) for 24 h. There are no standard drugs to use sarcopenia in clinics; the positive control cannot be used in this experiment. After washing cell surfaces by phosphate-buffered saline, the cells were lysed with 80 µl/well of lysis buffer [20 mM dithiothreitol, 2 mM EDTA, $10\%$ glycerol, $1\%$ Triton X-100 in phosphate buffer (0.2 M. pH 7.5)] by shaking the plates for 30 min at room temperature. the lysates (25 µl) were transferred into 96-well white plate and reacted with 50 µl of the luciferase assay regent (20 mM tricine (Sigma), $0.05\%$ magnesium carbonate basic (Nacalai), 2.5 mM MgSO4, 10 µM EDTA, 0.53 mM ATP Mg (Sigma), 33 mM dithiothreitol, 270 µM coenzyme A trilithium salt from yeast (Fujifilm Wako Pure Chemicals, Osaka, Japan), and 0.45 mM luciferin K (Fujifilm) in 0.1 M phosphate buffer (pH 8.0). And then, the luminescence signals of all wells were measured using a microplate reader (Wallac 1420 Workstation, PerkinElmer, Waltham, MASS, USA). Otherwise, the lysate (10 µl) was transferred to 96-well plate and reacted with 80 µl of the ONPG regent (1.1 mM MgCl2, 3.2 µM O-nitrophenyl-β-d-galactopyranoside (Fujifilm), 0.71 mM 2-mercaptoethanol in 0.1 M phosphate buffer, pH 7.5) for 30 min at room temperature. The absorbance at 405 nm of all wells were measured by a plate reader. By calculating the amount of the level of luminescence signals divide by the level of absorbance at 405 nm, the luciferase activity was obtained. Data are expressed as the percent of the activity of control group in graphs. The percent of the inhibition in each data were calculated by the following formula: (the luciferase activity − the average value of normal group)/[(the average value of control group) − (the average value of normal group)] × 100, and the half-maximal inhibitory concentration (IC50) was calculated from the least square regression line made from 3 points that crossed at $50\%$ of the percent of the inhibition value and the logarithmic concentration values.
## MTT assay
Differentiated C2C12 myoblasts (1 × 104/well) were plated in a 96-well plate, treated with the medium containing dexamethasone with/without the sample, and incubated for 24 h. After the surface of the cells was rinsed with PBS, the medium containing MTT (Nacalai, 50 µg in 100 µl) was added to the cells, and further incubated for 4 h. After the cells were rinsed with PBS, DMSO (100 µl) was added to each well to dissolve the resulting formazan, and the absorbance at 570 nm was measured.
## Quantitative real-time polymerase chain reaction
Differentiated C2C12 cells were planted into 24-well plate (4.0 × 106 cells/well) and incubated with the medium containing higenamine with or without dexamethasone (1 µM) for 24 h. After washing cell surfaces by phosphate-buffered saline, total RNA was extracted using RNA iso plus (Takara Bio, Shiga, Japan), and reverse transcribed to first-strand cDNA using PrimesScriptTM™ Master Mix (Takara Bio) according to the manufacture’s instruction. Quantitative real time PCR was performed in StepOne Real-time PCR system using twofold diluted Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA). The primer sequences used are shown in Table 1. Relative quantification of target gene was calculated using the − 2ΔΔCt method. Data are expressed as fold changes of the target gene/glyceraldehyde-3-phosphate dehydrogenase (GAPDH) compared with those of the control. Table 1List of oligonucleotide primer pairs used in RT-qPCRGeneForwardReverseProduct size (bp)MuRF1ACGAGAAGAAGAGCGAGCTGCTTCCCTGTACTGGAGGATCAGA91MAFbx/atrogin-1ACCCATGCAGGACTCCCAGACTTAAGCCACACCCCTCTTGCTTTTG87Cbl-bAAGTGGCCAAGTTCCATTGCTGCGAACCATCGGAAGATGA111TroponinAGGCTATGTCTGGCATGGAAGAGTGTCATACAGCAAGCCA92MyHCACCTTCAGCTCTGAGTTTGCACGCTTCTGGAGCTTAAGGA111IGF1TCCTTCTCAAGCCTGAGGTTGGTTAGCAATGCCCAGTTGA98Bnip3CTGCACTTCAGCAATGGCAAATGCTGGGCATCCAACAGTA93BCAT2TGGCTCAACATGGACAGGATTCAATGAGCTGGCGGATACA98GAPDHCAAGATTGTCAGCAATGCATCCCCTTCCACAATGCCAAAGTTG87MuRF1 muscle ring finger protein-1, MAFbx muscle atrophy F-box protein, Cbl-b casitas B-lineage lymphoma-b branched-chain, MyHC myosin heavy chain, IGI1 insulin-like growth factor-1, Bnip3 Bcl-2 binding and pro-apoptotic protein 3, BCAT2 branched-chain amino acid aminotransferase 2, GAPDH glyceraldehyde-3-phosphate dehydrogenase
## Measurement of the concentrations of higenamine and salsolinol in the decoctions of PA commercial products
Samples of commercial PA products (PA1, PA2, and PA3 of Japanese Pharmacopoeia XVIII Edition grade and the dried tuberous root of A. carmichaelii or A. japonicum, prepared by the processing described above, were purchased from Uchida Wakanyaku, Sanwa Shoyaku (Tokyo, Japan), Tsumura (Tokyo, Japan), Matsuura Yakugyo (Nagoya, Japan), and Tochimoto Tenkaido (Osaka, Japan). Table 2 presents the sample lists with the names of the distributers and lot numbers. Unprocessed aconite root, the dried tuberous root of A. carmichaelii, was purchased from Tochimoto Tenkaido (lot number, Lot. 32009004). Some samples were supplied as small pieces by cutting the whole crude drug into 2–4 mm blocks. The samples (about 30 g) were powdered using a mill, and passed through a sieve (300 µm). The powdered samples (25 mg) were mixed with 0.50 ml of ion-exchanged water and heated at 100 °C for 30 min. Following the centrifugation (1.2 × 104×g, 5 min), the supernatant was kept at − 20 °C until analysis. A 15 µl aliquot of the supernatant was mixed with 135 µl of $1\%$ formic acid and 30 µl of methyllycaconitine (Santa Cruz Biotechnology, Dallas, TX, USA, 34 µg/ml) in $1\%$ formic acid for use as an internal standard. Following centrifugation (1.2 × 104×g, 10 min), the supernatant was transferred into a glass vial for LC–MS/MS system (Waters Quattro Premier XE, Milford, MA, USA) with an electrospray ionization source in the positive ion mode and multiple reaction monitoring. HPLC separation was performed under the following conditions: column, Waters Acquity UPLC HSS C18 1.8 µm, 2.1 × 100 mm; mobile phase, linear gradient elution system, $0.05\%$ AcOH in H2O (solvent A): $0.05\%$ AcOH in acetonitrile (solvent B) (A/B) = $\frac{99}{1}$–$\frac{80}{20}$ for 0–1 min; $\frac{80}{20}$–$\frac{50}{50}$ for 1–2 min; $\frac{50}{50}$–$\frac{15}{85}$ for 2–3.5 min; $\frac{15}{85}$–$\frac{99}{1}$ for 3.5–3.6 min; $\frac{99}{1}$ for 3.6–6.5 min at a flow rate of 0.2 ml/min. The injection volume of the sample was 10 µl. Both quadrupoles were maintained at the unit resolution and the transitions (precursor to daughter) monitored were 179.9 → 162.9 m/z for salsolinol (retention time, 2.4 min), 272.0 → 106.8 m/z for higenamine (2.9 min), and 683.4 → 108.4 m/z for methyllycaconitine (3.4 min). Linear regressions of the concentration ranges of salsolinol and higenamine were calibrated by the peak area ratio of these compounds to methyllycaconitine using the least-squares method (r2 > 0.99).Table 2Concentrations of salsolinol and higenamine in the decoctions of commercially available processed aconite root (PA) or unprocessed aconite root (uzu)VendorLot #SalsolinolHigenaminePA1Uchida26211420 ± 50.18 ± 0.11PA1UchidaC4S024313 ± 20.046 ± 0.017PA1UchidaD8K024323 ± 110.077 ± 0.022PA1UchidaD8K0S1927 ± 110.059 ± 0.005PA1UchidaE85024325 ± 150.24 ± 0.08PA1UchidaF2F024315 ± 10.10 ± 0.02PA1Sanwa–16 ± 50.15 ± 0.00PA1TsumuraK0433118 ± 20.40 ± 0.06PA1MatsuuraH6L119 ± 80.26 ± 0.02PA2UchidaE9H05193.5 ± 0.70.014 ± 0.003PA2Tochimoto1808045.2 ± 2.00.023 ± 0.006PA2Tochimoto314150014.8 ± 2.40.022 ± 0.003PA3UchidaE7S05140.24 ± 0.19n.d. UzuTochimoto1808044.9 ± 1.00.011 ± 0.005Mean ± SD14 ± 90.12 ± 0.12CV (%)$63\%$$97\%$PA1, PA2, and PA3 were defined in processed aconite root (PA) section in Japanese Pharmacopoeia 18th Edition [22]. Each dried sample was powdered by mill, and each powder (25 mg) was decocted with 0.50 ml water for 30 min, then, centrifuged (15,000 rpm for 5 min), and the supernatant was analyzed using LC–MS/MS. " n.d." for higenamine means less than 0.0017 µg/ml. Each data (µg/ml) is the mean ± SD for 3 batches of the decoction in each lot. CV, coefficient of variation
## Statistical analysis
One-way analysis of variance (ANOVA) followed by Bonferroni's multiple test was used to compared multiple data. Data are expressed as mean ± standard error (SE), and $P \leq 0.05$ considered significant. All analyses were conducted using Mac Statistic Analysis Ver 3.0 (Esumi, Tokyo, Japan).
## Results
Dexamethasone-induced MuRF1 promoter expression in differentiated C2C12 cells at 1 µM was significantly suppressed by methanol-soluble part of the boiling water extract of PA in a concentration-dependent manner with its IC50 value of 1.5 mg/ml (Fig. 1). By MTT assay, no cytotoxicity was observed by the concentration of 2.0 mg/ml (data not shown).Fig. 1Effect of processed aconite root (PA) extract on dexamethasone-induced MuRF1 promoter expression in differentiated C2C12 cells. Differentiated C2C12 cells were transfected with pGL3-MuRF1 and pCMVβ-gal plasmids, and treated with or without dexamethasone (1 µM) and the methanol-soluble part of boiling water extract of PA for 24 h. Control group was treated with dexamethasone without the extract. Cell lysates from cells were used in luciferase assay. Data are mean ± SD ($$n = 5$$). * $P \leq 0.05$ and ***$P \leq 0.001$ compared with control group by Bonferroni's multiple tests.
Next, we tried to isolate the active ingredients by activity-guided fractionation. We obtained acidic layer, alkaline layer, BuOH layer, and water layer from PA as described in “Materials and methods”. Then, we evaluated the suppressive activities of them on dexamethasone-induced MuRF1 promoter expression and found that the activity was transferred into BuOH layer (data not shown). The BuOH layer was subjected to silica gel column chromatography, and the activity was transferred into Fr. 3 (data not shown). By preparative HPLC using ODS column, we isolated and identified salsolinol from Fr. 3 (Fig. 2A). Among the related compounds to salsolinol in other the constituents of PA, we obtained commercial reagent of higenamine [20]. Using commercial reagents, higenamine and salsolinol exhibited concentration-dependent suppressions on dexamethasone-induced MuRF1 promoter expression in differentiated C2C12 cells, and the IC50 values of higenamine and salsolinol were 0.49 and 50 µM, respectively (Fig. 2B, C).Fig. 2Effect of higenamine and salsolinol on dexamethasone-induced MuRF1 promoter expression in differentiated C2C12 cells. A Chemical structure of higenamine and salsolinol. B, C Differentiated C2C12 cells were transfected with pGL3-MuRF1 and pCMVβ-gal plasmid and treated with or without 1 µM dexamethasone and higenamine (B) or salsolinol (C) for 24 h. Control group was treated with dexamethasone without higenamine or salsolinol. Lysates from cells were used in luciferase assay. Data are mean ± SD ($$n = 3$$). * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$ compared with control group by Bonferroni's multiple tests We measured the contents of higenamine and salsolinol in the decoctions of commercially available thirteen kinds of PA products and one kind of unprocessed aconite root (Japanese name, uzu) product, and the data are shown in Table 2. The PA decoction used in the experiments mentioned above contained 0.10 and 15 µg/ml of higenamine and salsolinol, respectively. Their contents in fourteen products were varied and the coefficient of variation (CV) values were $97\%$ and $63\%$, respectively.
We evaluated the suppressive effects of higenamine on MuRF1 mRNA expression. Although higenamine did not exhibit any inhibitions on MuRF1 mRNA expressions in normal condition of differentiated C2C12 cells, these compounds significantly suppressed dexamethasone-induced MuRF1 mRNA expressions (Fig. 3A). Higenamine also significantly suppressed dexamethasone-induced mRNA expressions of MAFbx/atrogin1 and casitas B-lineage lymphoma-b (Cbl-b), which are belonging to muscle-specific ubiquitin ligase similar to MuRF1, troponin which is integral to muscle contraction in skeletal and cardiac muscle, branched-chain amino acid aminotransferase 2 (BCAT2) that attenuates muscle protein degradation, and Bcl-2 binding and pro-apoptotic protein3 (Bnip3) that is the marker of autophagy. The suppression on Cbl-b and Bnip3 exhibited concentration-dependent manners (Fig. 3B–F). Dexamethasone significantly suppressed mRNA expressions on myosin heavy chain (MyHC) and insulin-like growth factor-1 (IGF1), but higenamine at 3.0 and 9.0 µM did not exhibited significant effect on theses suppressions (data not shown). Higenamine at 3.0 and 9.0 µM did not exhibit any effects on the mRNA expressions of these target genes in C2C12 cells without dexamethasone treatment. Fig. 3Effect of higenamine on mRNA expressions of muscle ring finger protein-1 (MuRF1) (A), muscle atrophy F-box protein (MAFbx)/atrogin 1 (B), casitas B-lineage lymphoma-b branched chain (Cbl-b) (C), troponin (D), branched-chain amino acid aminotransferase 2 (BCAT2) (E), Bcl-2 binding and pro-apoptotic protein 3 (Bnip3) (F) in differentiated C2C12 cells. Differentiated C2C12 cells were treated with the medium containing 1 µM dexamethasone and/or higenamine (3 or 9 µM) for 24 h, and total mRNA samples were collected and analyzed mRNA expressions of these genes using real-time PCR. Relative quantification of target gene was calculated using the − 2ΔΔCt method, and data are expressed as fold changes of the target gene/glyceraldehyde-3-phosphate dehydrogenase (GAPDH) compared with those of the control. N the medium without dexamethasone and higenamine, C the medium containing dexamethasone (1 µM) without higenamine. Data are mean ± SD ($$n = 4$$). Different alphabetical letters a, b, and c over the columns in each figure panel indicate statistically significant differences at $P \leq 0.05$ evaluated by Bonferroni's multiple tests
## Discussion
The root of *Aconitum carmichaelii* is a well-known crude drug to relieve pain related to cold symptoms [14]. Its raw root contains toxic diterpene alkaloids, such as aconitine and mesaconitine, LD50 values of which are 0.5–1.8 g/kg for oral administration in mice [21]; therefore, various processing methods to reduce the toxicity of the root of *Aconitum carmichaelii* have been developed, and the eighteenth edition of the Japanese Pharmacopoeia (JPXVIII) registers the dried material of the autoclaved root of A. carmichaelii as the item name processed aconite root (PA) [22]. Highly toxic diterpene alkaloids are degraded into less toxic diterpene alkaloids (e.g., benzoylmesaconine) by heating or autoclaving [21], and the toxicity of benzoylmesaconine is about one-eight hundredth of mesaconitine [23]. Then, PA is considered to be a safe and effective herbal agent to relieve pain. Although the analgesic activity of benzoylmesaconine in tail pinch test using mice was much less than that of mesaconitine about one-thousandth [24], neoline remained in PA after the heat processing of A. carmichaelii raw root to exhibit the effectiveness for neuropathic pain [15, 16].
In the present study, we evaluated the effectiveness of PA related to another traditional knowledge of the effectiveness to supply the kidney energy [14]. In traditional Kampo medicine and traditional Chinese medicine, the deficiency of kidney energy is related to aging, and several diseases associated with old age, such as frequent urination, lumbar pain, lethargy, blurred vision, sarcopenia, and frailty [3, 25]. Therefore, we explored the effectiveness of PA to prevent muscular atrophy and evaluated its suppressive effect on dexamethasone-induced MuRF1 expression in C2C12 cells in vitro. Then, we found that it was significantly suppressed by methanol-soluble part of boiled water extract of PA with its IC50 value of 1.5 mg/ml. This titer was weaker than the effect of PA boiled water extract on oxaliplatin-induced reduction of neurite elongation in dorsal root ganglion neurons (the effective concentration, 0.1 mg/ml) [15].
In the process of activity-guided fractionation of PA extract, we found that the active ingredients of the suppressive effect on dexamethasone-induced MuRF1 expression did not transfer into the fraction containing aconitine-type diterpene alkaloids, the major pharmacologically active ingredients of PA [15, 16, 24]. Then, we finally found higenamine and salsolinol as the active constituents in PA in the fraction containing relatively hydrophilic and neutral compounds. Since higenamine and salsolinol are alkaloidal compounds but they have both phenolic hydroxyl groups and amine motif in their chemical structures, it can be reasonable to find these compounds in this fraction. Higenamine was isolated from the raw root of A. japonicum as the cardiac tonic principle [20], and salsolinol was isolated from the raw root of A. carmichaelii [17].
We measured the contents of higenamine and salsolinol in the decoctions of available PA or unprocessed aconite root products (uzu). When 25 mg of PA sample used in the first experiments was decocted in 0.5 ml of water, the decoction contained 0.10 and 15 µg/ml of higenamine and salsolinol, respectively. By these data, the contents of higenamine and salsolinol in PA sample were 0.00020 (w/w) % and 0.030 (w/w) %, respectively. The IC50 of the methanol-soluble part of boiling water extract of PA (1.5 mg/ml) can be converted to 12 mg/ml of PA by the ratio yielded, and this concentration was further converted to 23 ng/ml (= 0.086 µM) of higenamine and 3.5 µg/ml (19 µM) of salsolinol. Using the IC50 values of higenamine and salsolinol at 0.49 and 50 µM, respectively, it is considered that higenamine and salsolinol contributed the effectiveness of PA about $18\%$ and $38\%$, respectively. The contents of salsolinol and higenamine were variated among the commercially available PA products. The contents of salsolinol and higenamine in PA3 product were the lowest among PA products analyzed, and those in PA2 were tended to be lower than those in PA1. The processing method of PA3 was the treatment with calcium hydroxide after rinsing in salt solution, and that of PA2 was heating or autoclaving after rinsing in salt or rock salt solution [22]. Since salsolinol and higenamine are hydrophilic compounds, they may easily be extracted and lost in the process of rinsing in salt solution. The contents of salsolinol and higenamine in uzu were similar to those of PA2 products of the same pharmaceutical company, suggesting that the contents of salsolinol and higenamine may not change by heat processing and may play as the active ingredients of PA after heat processing, like neoline [16].
Since the titer of higenamine in the present activity was about 100-fold higher than that of salsolinol, the further pharmacological evaluations were focused on higenamine. Dexamethasone stimulates glucocorticoid receptor in skeletal muscle to activate Krüppel-like factor 15, that promotes the expressions of MuRF1, MAFbx/atrogin-1, and Cbl-b to induce ubiquitin–proteasome-dependent protein degradation and muscle atrophy, the expression of Bnip3 to induce autophagy, and the expression of BCAT2 to induce the feedback system and to suppress the function of glucocorticoid receptor [26]. Since higenamine did not exhibit any effects on the mRNA expressions of MuRF1, MAFbx/atrogin-1, Cbl-b, Bnip3, BCAT2, and troponin in normal condition of differentiated C2C12 cells, higenamine did not affect the homeostasis of C2C12 cells. However, higenamine significantly suppressed their mRNA expressions stimulated by dexamethasone; therefore, higenamine has the protective effects against the violation of dexamethasone in differentiated C2C12 cells. Although the molecular target of higenamine to protect dexamethasone-induced muscle atrophy is unknown, higenamine did not affect the signal transduction from IGF1 receptor stimulation to myosin heavy chain expressions but that from glucocorticoid receptor stimulation into skeletal muscle atrophy in the catabolic processes, since higenamine did not counteract the suppression of MyHC and IGF1 mRNA suppressions induced by dexamethasone in differentiated C2C12 cells.
Higenamine is well-known β2-adrenoceptor agonist and is registered in the World Anti-Doping Agency (WADA) Prohibited Substances and Methods list [27]. Higenamine has vasodilating and anti-inflammatory effects on aorta [28, 29], anti-aggregating activity on platelets [30], and anti-apoptotic effects on hypoxia-induced brain injury [31, 32]. Higenamine protects the cardiac injury induced by ischemia/reperfusion, collagen-induced arthritis, and the apoptosis gastric smooth muscle cells in diabetes via the activation of β2-adrenoceptor and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways [33–35]. On the other hand, trimetazidine, an anti-anginal agent, significantly counteracted dexamethasone-induced skeletal muscle atrophy and the phosphorylation of PI3K and AKT in C2C12 cells, suggesting that dexamethasone would induce skeletal muscle atrophy by the suppression of PI3K/AKT signaling pathways [36]. Considering the above results together, higenamine might suppressed dexamethasone-induced muscle atrophy by activating β2-adrenoceptor and PI3K/AKT signaling pathways.
When higenamine (50 mg/kg) was orally administered into rabbits, the maximum blood concentration was 2.9 µg/ml appeared at 10 min after the administration [37]. When 3 g of PA1 sample (Tsumura, K04331) was decocted in 60 ml of water, about 24 µg of higenamine can be collected, and the dosage in human is calculated as 0.48 µg/kg. By this dosage and the result of pharmacokinetic study in rabbits [37], the maximum blood concentration of higenamine by taking PA1 (3 g) in human can be calculated as 28 pg/ml. Since this estimated blood concentration of higenamine was much lower than its IC50 value (0.10 µg/ml) in the present study, the contribution of higenamine as the active ingredients in PA to the prevention from muscular atrophy could be small in clinic, and further studies to find other active ingredients in PA were demanded.
The pharmacology of PA related to the effectiveness for the deficiency of kidney energy in traditional Japanese Kampo medicine and traditional Chinese medicine might be the protective effects on muscular atrophy, and we found higenamine and salsolinol as the active ingredients. Although the clinical contribution of these compounds to the prevention from muscular atrophy is not high, these compounds can be considered as the active ingredients of PA and be used as the marker compounds for the quality control of PA.
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|
---
title: Intra- and inter-session reliability and repeatability of an infrared thermography
device designed for materials to measure skin temperature of the triceps surae muscle
tissue of athletes
authors:
- Cesar Calvo-Lobo
- Marta San-Antolín
- Daniel García-García
- Ricardo Becerro-de-Bengoa-Vallejo
- Marta Elena Losa-Iglesias
- Julia Cosín-Matamoros
- Israel Casado-Hernández
- Eva María Martínez-Jiménez
- Victoria Mazoteras-Pardo
- David Rodríguez-Sanz
journal: PeerJ
year: 2023
pmcid: PMC10008311
doi: 10.7717/peerj.15011
license: CC BY 4.0
---
# Intra- and inter-session reliability and repeatability of an infrared thermography device designed for materials to measure skin temperature of the triceps surae muscle tissue of athletes
## Abstract
### Background
Infrared thermography devices have been commonly applied to measure superficial temperature in structural composites and walls. These tools were cheaper than other thermographic devices used to measure superficial human muscle tissue temperature. In addition, infrared thermography has been previously used to assess skin temperature related to muscle tissue conditions in the triceps surae of athletes. Nevertheless, the reliability and repeatability of an infrared thermography device designed for materials, such as the Manual Infrared Camera PCE-TC 30, have yet to be determined to measure skin temperature of the triceps surae muscle tissue of athletes.
### Objective
The purpose was to determine the procedure’s intra- and inter-session reliability and repeatability to determine skin temperature within the Manual Infrared Camera PCE-TC 30 thermography device in the triceps surae muscle tissue of athletes, which was initially designed to measure the superficial temperature of materials.
### Methods
A total of 34 triceps surae muscles were bilaterally assessed from 17 healthy athletes using the Manual Infrared Camera PCE-TC 30 thermography device to determine intra- (at the same day separated by 1 h) and inter-session (at alternate days separated by 48 h) reliability and repeatability of the skin temperature of the soleus, medial and lateral gastrocnemius muscles. The triceps surae complex weas measured by a region of interest of 1 cm2 through five infrared thermography images for each muscle. Statistical analyses comprised intraclass correlation coefficient (ICC), standard error of measurement (SEM), minimum detectable change (MCD), systematic error of measurement, correlation (r), and Bland-Altman plots completed with linear regression models (R2).
### Results
Intra- and inter-session measurements of the proposed infrared thermography procedure showed excellent reliability (ICC[1,2] = 0.968–0.977), measurement errors (SEM = 0.186–0.232 °C; MDC = 0.515–0.643 °C), correlations ($r = 0.885$–0.953), and did not present significant systematic error of measurements ($P \leq 0.05$). Adequate agreement between each pair of measurement moments was presented by the Bland-Altman plots according to the limits of agreement and non-significant linear regression models (R2 = 0.000–0.019; $P \leq 0.05$).
### Conclusions
The proposed procedure to determine skin temperature within the Manual Infrared Camera PCE-TC 30 thermography device presented excellent intra- and inter-session reliability and repeatability in athletes’ triceps surae muscle tissue. Future studies should consider the SEM and MDC of this procedure to measure the skin temperature of soleus, medial, and lateral gastrocnemius muscles to promote triceps surae muscle prevention and recovery in athletes.
## Introduction
Infrared thermography was proposed as one of the most relevant non-ionizing radiation tools to assess skin temperature related to muscle tissue conditions, such as compartmental syndrome. Nevertheless, there is a lack of scientific evidence for other musculoskeletal conditions (Sanchis-Sánchez et al., 2014). Among different application fields, the use of inflammation and perfusion-based conditions may be evaluated by thermographic imaging in sports medicine (Ramirez-GarciaLuna et al., 2022). This device has been used to determine the superficial temperature of muscle tissue of lower limbs from athletes for injury prevention (Côrte et al., 2019), muscle activity assessment (Rodriguez-Sanz et al., 2019), and evaluations before and after treatments (Rodriguez et al., 2018; Benito-de-Pedro et al., 2019). In addition, assessments before and after running were performed in the triceps surae from athletes as a main focus linked to musculoskeletal conditions (Rodríguez-Sanz et al., 2017, 2018; Rodriguez et al., 2018; Benito-de-Pedro et al., 2019).
Different thermography methods to determine skin temperature in the calves’ muscular region showed good correlations among them (R2 = 0.885–0.924) and between both sides (R2 = 0.754–0.881; $r = 0.868$–0.939) within adequate agreement by Bland-Altman plots (Ludwig et al., 2014). Thermography assessment was used to measure the cutaneous temperature of triceps surae muscles in soccer players with equinus condition vs. non-equinus condition after running (Rodriguez-Sanz et al., 2018), as well as at rest (Rodríguez-Sanz et al., 2017). In addition, this tool was applied to determine the treatment effects after different physical therapy interventions, such as dry needling and ischemic compression, in triathletes (Benito-de-Pedro et al., 2019). In addition, infrared thermography was utilized to measure skin temperature changes after compressive vs. standard stockings use in athletes (Rodriguez et al., 2018). All these evaluations were carried out by the FLIR/SC3000/QWIP Thermacan thermographic tool to measure the skin temperature of the triceps surae muscle tissue.
The FLIR/SC3000/QWIP Thermacan infrared thermal device presented an 8–9 µm spectral range, a temperature sensitivity of 0.02 K, a display of 320 × 240 pixels with 20° lens and a spatial resolution of 1.1 mrad, being frequently used to evaluate the superficial temperature in the human tissue with adequate reliability and repeatability (Rodríguez-Sanz et al., 2017, 2018; Rodriguez et al., 2018; Benito-de-Pedro et al., 2019), by both manual and automatic thermographic software package measurement methods with an adequate agreement and excellent intraclass correlation coefficient (ICC > 0.80) (Requena-Bueno et al., 2020). Prior statistical procedures, such as ICC and Bland-Altman plots, including limits of agreement (LoA), were used to compare infrared thermographic values in the lower limbs showing that both manual and automatic definition devices presented an excellent ICC from 0.92 to 0.99 with an adequate agreement by visual distribution and similar LoA by the ThermoHuman® devices (Fernandez-Cuevas et al., 2017; Requena-Bueno et al., 2020), excellent inter-session reproducibility with an ICC of 0.88 using the digital infrared camera IRTIS-2000® (Zaproudina et al., 2008), and almost perfect agreement in replication with an ICC from 0.94 to 0.97 by the Thermofocus® thermal imaging device (Petrova et al., 2018).
Nevertheless, these devices were more expensive than an infrared thermography device designed to evaluate superficial temperature on materials. One of these thermographic devices was the Manual Infrared Camera PCE-TC 30. This tool displayed a sensor resolution of 80 × 80, a measurement range from 0 °C to 250 °C, a display of 320 × 240 pixels, a thermal sensitivity of 80 mK, and an 8 mm lens (De Villoria et al., 2011; Pérez-Urrestarazu et al., 2014). Despite this, the thermal imaging system presented very low parameters concerning geometrical resolution (80 × 80 pixels) and thermal sensitivity of 80 mK, while other thermal imaging systems displayed high definition resolution (1,280 × 960 pixels) and higher thermal sensitivity of 20 mK (Fernandez-Cuevas et al., 2017; Requena-Bueno et al., 2020), the Manual Infrared Camera PCE-TC 30 geometrical resolution and thermal sensitivity features could present adequate reliability to measure triceps surae muscle tissue temperature variations (Rodríguez-Sanz et al., 2017, 2018; Rodriguez et al., 2018; Benito-de-Pedro et al., 2019). Indeed, the PCE-TC 30 thermal camera has already been employed to assess the thermal behavior of fencing uniforms in athletes (Lamberti et al., 2020), but the reliability and repeatability of this tool directly on the skin of the human muscle have yet to be determined.
This infrared thermography device was used to measure superficial temperature in structural composites and walls according to quality inspections, such as reproducibility, stability, reliability, and operating temperature (De Villoria et al., 2011; Pérez-Urrestarazu et al., 2014). Although this tool is used to assess thermal temperature in the fencing uniforms of athletes (Lamberti et al., 2020), it has not been applied directly to measure superficial human muscle tissue temperature, which could be of interest in the triceps surae of athletes (Rodríguez-Sanz et al., 2017, 2018; Rodriguez et al., 2018; Benito-de-Pedro et al., 2019; Requena-Bueno et al., 2020).
We hypothesized that this device—developed initially for non-Vivo structures—could provide adequate reliability and repeatability to determine skin temperature in the triceps surae muscle human tissue of athletes, being less expensive than the infrared thermography device used in human studies. Thus, the purpose of the present study was to determine the intra- and inter-session reliability and repeatability of the procedure to assess skin temperature within the Manual Infrared Camera PCE-TC 30 thermography device in the triceps surae muscle tissue of athletes, which was designed initially to measure the superficial temperature of materials.
## Study design
The present study was carried out from January 2020 to May 2021 according to The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria (Appendix 1). The Helsinki Declarations, as well as specific human experimentation ethical rules, were taken into account. The ethics committee for clinical interventions of the Hospital Clínico San Carlos, Madrid (Spain) approved this study with internal code number $\frac{20}{021}$-E on January 20, 2020. Before the study began, all participants signed the informed consent form. In addition, the present study was supported by Contract 83 between Complutense University and PCE Ibérica S.L. (Reference number: 6-2020), providing a specific grant for this research project.
## Sample size
The sample size determination was calculated by bi-variate correlations statistical procedures through G*Power 3.1.9.2 program (G*Power©, from Dusseldorf University, in Germany), considering a 0.4 correlation coefficient to achieve a moderate correlation (Lobo et al., 2016) between infrared thermography measurements, applying a 1-tailed hypothesis, a 0.05 α error and a 0.80 power. Lastly, the sample size was 34 triceps surae muscles to achieve the required thermography measurements for a 0.801 actual power.
## Participants
Thirty-four triceps surae muscles were bilaterally analyzed from 17 healthy athletes considering a consecutive sampling recruitment procedure. Inclusion criteria comprised specifically healthy athletes aged 18–65 years providing the consent information document previously, carrying out sports activities and training for at least 2 h as well as 1 day per week, with moderate (level-II) or vigorous (level-III) intensities for physical activity with metabolic equivalent indexes greater than 600 METs/min/week, measured by the International Questionnaire for Physical Activity (IPAQ) (Roman-Viñas et al., 2010).
Exclusion criteria included muscle soreness, congenital dysfunctions, neuromuscular conditions, rheumatic alterations, body mass index (BMI) greater than 31 kg/m2, previous neurological conditions, prior surgeries and skin pathologies. Some alterations in the lower limbs region (i.e., chronic ankle instability presence, prior sprains or previous fractures) were also excluded according to the thermographic influence of compartmental, stress, inflammation, and perfusion-based conditions (Sanchis-Sánchez et al., 2014; Ramirez-GarciaLuna et al., 2022). Lastly, difficulties or inability to carry out the procedure to complete the study course, explained below, were considered as exclusion criteria.
## Procedure
Intra- and inter-session reliability and repeatability of the skin temperature of the triceps surae muscles were bilaterally assessed by the Manual Infrared Camera PCE-TC 30 to determine measurement agreement and concordance at the same day separated by 1 h (considered as intra-day measurements) and alternate days separated by 48 h (considered as inter-day measurements), respectively. Indeed, participants were asked to continue with their daily life and physical exercise routine (avoiding unusual efforts or activity changes) between measurements and not taking prescribed medications at the prior week nor vasomotor substances (i.e., caffeine) on the same measurement day, as well as heavy metals were not allowed. A period of 5 min of acclimatization of the subjects to the room was applied (Fig. 1). All measurements were assessed with patients standing up in a relaxed position in the same room within a 24.1 °C ± 1 °C temperature and a $45\%$ ± $10\%$ humidity, without direct ventilation flow toward examiners or participants (Rodríguez-Sanz et al., 2017).
**Figure 1:** *Infographics for evaluating and analyzing the infrared thermography images of the triceps surae complex.*
## Descriptive data
Descriptive data including sex (categorized as male or female), age (measured in years), height (measured in cm), weight (measured in kg), and BMI (expressed as kg/cm2 following the Quetelet’s index) (Garrow, 1986), main sports category (divided into fitness considered as bodybuilding exercise or soccer), side (categorized as right or left), and dominance (expressed as yes or no), and smoker (expressed as yes or no) were detailed (Calvo-Lobo et al., 2019). As a tool with adequate psychometric properties, metabolic equivalent index per minute per week (METs/min/week) was evaluated by the IPAQ to determine physical activity and its categorization as moderate (600–1,500 METs/min/week) and vigorous (≥1,500 METs/min/week) physical activity (Gauthier, Lariviere & Young, 2009).
The final sample comprised 34 triceps surae muscles bilaterally from 17 healthy athletes, nine ($52.9\%$) males and eight ($47.1\%$) females, with mean ± SD ($95\%$ CI) age of 41.76 ± 14.42 (36.73–46.79) years, the weight of 68.57 ± 14.57 (63.40–73.64) kg, the height of 1.69 ± 0.09 (1.66–1.73) m, and BMI of 23.45 ± 3.47 (22.24–24.66) kg/m2. Regarding the main sports category, these athletes performed in fitness ($$n = 14$$; $82.40\%$) and soccer ($$n = 3$$; $17.6\%$). All athletes presented the dominant right side ($$n = 17$$; $100\%$); most were non-smokers ($$n = 12$$; $70.60\%$). Considering the IPAQ, the mean ± SD ($95\%$ CI) of metabolic equivalents index per minute per week was 3,276.08 ± 1,876.49 (2,621.34–3,930.82) METs/min/week, including eight ($47.10\%$) athletes who performed vigorous physical activity and nine ($52.90\%$) athletes who performed moderate physical activity. Table 1 shows the normality statistics and significance according to the Kolmogorov-Smirnov test.
**Table 1**
| Variables | Variables.1 | Descriptive data | K-S statistic | K-S P-value |
| --- | --- | --- | --- | --- |
| Age (years) | Age (years) | 41.76 ± 14.42 (36.73–46.79)‡ | 0.126 | 0.189 |
| Weight (kg) | Weight (kg) | 68.57 ± 14.57 (63.40–73.64)‡ | 0.098 | 0.200 |
| Height (m) | Height (m) | 1.69 ±0.09 (1.66–1.73)‡ | 0.142 | 0.078 |
| BMI (kg/m2) | BMI (kg/m2) | 23.45 ± 3.47 (22.24–24.66)‡ | 0.095 | 0.200 |
| IPAQ (METs/min/week) | IPAQ (METs/min/week) | 3,276.08 ± 1,876.49 (2,621.34–3,930.82)‡ | 0.11 | 0.200 |
| Soleus at baseline (°C) | Soleus at baseline (°C) | 31.31 ± 1.41 (30.82–31.81)‡ | 0.119 | 0.200 |
| Soleus after 1 h (°C) | Soleus after 1 h (°C) | 31.47 ± 1.80 (31.34–32.13)‡ | 0.09 | 0.200 |
| Soleus after 48 h (°C) | Soleus after 48 h (°C) | 31.55 ± 1.03 (31.18–31.91)‡ | 0.096 | 0.200 |
| Medial gastrocnemius at baseline (°C) | Medial gastrocnemius at baseline (°C) | 31.33 ± 1.40 (30.84–31.82)‡ | 0.138 | 0.098 |
| Medial gastrocnemius after 1 h (°C) | Medial gastrocnemius after 1 h (°C) | 31.66 ± 1.81 (31.42–32.22)† | 0.155 | 0.039* |
| Medial gastrocnemius after 48 h (°C) | Medial gastrocnemius after 48 h (°C) | 31.55 ± 1.08 (31.17–31.93)‡ | 0.097 | 0.200 |
| Lateral gastrocnemius at baseline (°C) | Lateral gastrocnemius at baseline (°C) | 31.24 ± 1.36 (30.76–31.71)‡ | 0.086 | 0.200 |
| Lateral gastrocnemius after 1 h (°C) | Lateral gastrocnemius after 1 h (°C) | 31.77 ± 1.11 (31.38–32.16)‡ | 0.146 | 0.065 |
| Lateral gastrocnemius after 48 h (°C) | Lateral gastrocnemius after 48 h (°C) | 31.52 ± 1.08 (31.14–31.90)‡ | 0.117 | 0.200 |
## Infrared thermography
We used a Manual Infrared Camera PCE-TC 30 (PCE Instruments UK Ltd, Southampton, United Kingdom), which displayed a sensor resolution of 80 × 80, a measurement range from 0 °C to 250 °C, a display of 230 × 240 pixels, a thermal sensitivity of 80 mK and an 8-mm lens (De Villoria et al., 2011; Pérez-Urrestarazu et al., 2014). The infrared thermography imaging process was performed with the participant standing up in a relaxed position 1 m from the camera. Bilaterally, the triceps surae complex, including lateral (Figs. 1A and 1B) and medial (Figs. 1C and 1D) gastrocnemius, as well as soleus (Figs. 1E and 1F) muscles, was measured by a region of interest (ROI) through five infrared thermography images for each muscle. Removing the highest and lowest values, the mean of the three measurements was used for data analysis.
Infrared images and data were analyzed by a blinded and experienced evaluator using the Guide™ Report Express (PCE Instruments UK Ltd, Southampton, United Kingdom) (Rodríguez-Sanz et al., 2017). This software provided the mean thermal value (°C) of the selected ROI of 1 cm2 coinciding with the center of a landmark for each muscle (Fig. 2). These landmarks were used to determine superficial skin temperature and placed superior to the Achilles tendon for the soleus muscle and in the thickest part of the medial and lateral gastrocnemius muscles according to prior similar studies (Benito-de-Pedro et al., 2019; Rojas-Valverde et al., 2021).
**Figure 2:** *Infrared thermography images of the triceps surae complex, including left (A) and right (B) lateral gastrocnemius, left (C) and right (D) medial gastrocnemius, and left (E) and right (F) soleus muscles, including the thermal values of the region of interest.*
## Statistical analyses
The 24.0 Statistical Package Program for Social Science (named SPSS, from IBM-Corp, in Armonk, NY, USA) was used for data analyses. The α error was set at 0.05, and thus a P-value lower than 0.05 was considered statistical significance. The Kolmogorov-Smirnov statistical test and visual inspection of histograms were considered to detail normality distribution. Data adjusted to normal distribution were detailed through means ± standard deviations (SD) in conjunction with the upper and lower limits of $95\%$ confidence interval (CI). Data adjusted to non-normal distribution were detailed through medians ± interquartile ranges (IR). Infrared thermography measurements for intra- and inter-session evaluations were compared through paired-sample Student t-tests considering parametric tests and Wilcoxon tests regarding non-parametric tests. ICC analyzed the reliability and repeatability between each pair of measurements for bidirectional absolute agreement and Pearson (r) or Spearman (ρ) correlation coefficients as parametric or non-parametric tests, respectively. Furthermore, ICC[2,1] values were specifically interpreted as poor for <0.40 ICC[2,1], weak for 0.40–0.59 ICC[2,1], good for 0.60–0.74 ICC[2,1], and excellent for 0.75–1.00 ICC[2,1] (Calvo-Lobo et al., 2019).
Next, correlation coefficients were specifically interpreted as weak for 0.00–0.40 r or ρ, moderate for 0.41–0.69 r or ρ, and strong for 0.70–1.00 r or ρ (Lobo et al., 2016). Standard errors for measurements (SEM) values were detailed through SD × \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\sqrt {\left({1\; - \; ICC} \right)}$\end{document}(1−ICC). After, minimum detectable changes (MDC) values were detailed through \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\sqrt 2 \times 1.96 \times SEM$\end{document}2×1.96×SEM for $95\%$ CI. Both MDC and SEM were detailed through Bland and Altman recommendations (Calvo-Lobo et al., 2019). Limits for agreement (LoA) for each pair of measurements were detailed through differences means \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\pm \; 1.96 \times SD$\end{document}±1.96×SD for $95\%$ CI in line with Bland and Altman (Bland & Altman, 2010; Calvo-Lobo et al., 2019).
In addition, Bland-Altman plots were shown to detail visual agreements for each pair of measurements showing systematic measurement errors of the differences in means distributions for each pair of measurements located at the Y-axis with regards to the means for each pair of measurements located at the X-axis. These Bland-Altman plots were shown in conjunction with linear regression models. R2 coefficients were calculated to detail the adjustment quality. The mean values for each pair of measurements were considered independent variables. Lastly, the differences for each pair of measurements were considered dependent variables (Bland & Altman, 2010).
## Intra-session reliability and repeatability
According to Table 2, intra-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the triceps surae muscle tissue of athletes showed excellent reliability (ICC[1,2] = 0.969–0.977), measurement errors (SEM = 0.186–0.212 °C; MDC = 0.515–0.587 °C) and did not present any statistically significant systematic error of measurements ($P \leq 0.05$).
**Table 2**
| Infrared thermography (°C) | Baseline measurements (95% CI) | After 1 h measurements (95% CI) | ICC(1,2) (95% CI) | SEM | MDC | P-value* |
| --- | --- | --- | --- | --- | --- | --- |
| Soleus | 31.31 ± 1.41 [30.82–31.81] | 31.33 ± 1.40 [30.84–31.82] | 0.977 [0.954–0.988] | 0.212 | 0.587 | 0.822‡ |
| Medial gastrocnemius | 31.47 ± 1.80 [31.34–32.13] | 31.66 ± 1.81 [31.42–32.22] | 0.966 [0.931–0.983] | 0.208 | 0.576 | 0.467† |
| Lateral gastrocnemius | 31.55 ± 1.03 [31.18–31.91] | 31.55 ± 1.08 [31.17–31.93] | 0.969 [0.938–0.984] | 0.186 | 0.515 | 0.982‡ |
Regarding intra-session measurements, bivariate correlations were excellent for the soleus ($r = 0.953$; $P \leq 0.001$), medial gastrocnemius (ρ = 0.885; $P \leq 0.001$), and lateral gastrocnemius ($r = 0.939$; $P \leq 0.001$).
In addition, Bland-Altman plots presented an adequate agreement for intra-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the soleus (Fig. 3), medial gastrocnemius (Fig. 4) and lateral gastrocnemius (Fig. 5) muscles of athletes, due to visual distributions of the difference means for each pair of measurements at Y axis concerning the mean for each pair of measurements at X-axis did not present any systematic measurement error and most thermographic measurements were between the upper and lower LoA. In addition to Bland-Altman plots, linear regression models did not show any statistical significance for soleus (R2 = 0.000; β = 0.003; F1,32 = 0.003; $$P \leq 0.954$$), medial gastrocnemius (R2 = 0.002; β = −0.017; F1,32 = 0.065; $$P \leq 0.800$$), and lateral gastrocnemius (R2 = 0.019; β = −0.050; F1,32 = 0.626; $$P \leq 0.435$$) intra-session measurements.
**Figure 3:** *Bland-Altman plots agreement for intra-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the soleus muscle tissue of athletes.Completed with the upper and lower limits of agreement (LoA).* **Figure 4:** *Bland-Altman plots agreement for intra-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the medial gastrocnemius muscle tissue of athletes.Completed with the upper and lower limits of agreement (LoA).* **Figure 5:** *Bland-Altman plots agreement for intra-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in athletes’ lateral gastrocnemius muscle tissue.Completed with the upper and lower limits of agreement (LoA).*
## Inter-session reliability and repeatability
According to Table 3, inter-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the triceps surae muscle tissue of athletes presented excellent reliability (ICC[1,2] = 0.956–0.974), measurement errors (SEM = 0.187–0.232 °C; MDC = 0.518–0.643 °C), and did not present any statistically significant systematic error of measurements ($P \leq 0.05$).
**Table 3**
| Infrared thermography (°C) | Baseline measurements (95% CI) | After 48 h measurements (95% CI) | ICC(1,2) (95% CI) | SEM | MDC | P-value* |
| --- | --- | --- | --- | --- | --- | --- |
| Soleus | 31.31 ± 1.41 [30.82–31.81] | 31.24 ± 1.36 [30.76–31.71] | 0.974 [0.948–0.987] | 0.222 | 0.615 | 0.338‡ |
| Medial gastrocnemius | 31.74 ± 1.12 [31.34–32.13] | 31.77 ± 1.11 [31.38–32.16] | 0.956 [0.913–0.978] | 0.232 | 0.643 | 0.682‡ |
| Lateral gastrocnemius | 31.55 ± 1.03 [31.18–31.91] | 31.52 ± 1.08 [31.14–31.90] | 0.968 [0.937–0.984] | 0.187 | 0.518 | 0.719‡ |
Considering inter-session measurements, bivariate correlations were also excellent for the soleus ($r = 0.949$; $P \leq 0.001$), medial gastrocnemius ($r = 0.914$; $P \leq 0.001$), and lateral gastrocnemius ($r = 0.938$; $P \leq 0.001$).
Lastly, Bland-Altman plots showed an adequate agreement for inter-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the soleus (Fig. 6), medial gastrocnemius (Fig. 7), and lateral gastrocnemius (Fig. 8) muscles of athletes, since visual distributions of the difference means for each pair of measurements at the Y axis concerning the mean for each pair of measurements at the X axis did not present any systematic measurement error, and most thermographic measurements were between the upper and lower LoA. In conjunction with the Bland-Altman plots, linear regression models did not show any statistical significance for soleus (R2 = 0.014; β = 0.039; F1,32 = 0.463; $$P \leq 0.501$$), medial gastrocnemius (R2 = 0.000; β = −0.009; F1,32 = 0.014; $$P \leq 0.907$$), and lateral gastrocnemius (R2 = 0.019; β = −0.050; F1,32 = 0.621; $$P \leq 0.436$$) inter-session measurements.
**Figure 6:** *Bland-Altman plots agreement for inter-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the soleus muscle tissue of athletes.Completed with the upper and lower limits of agreement (LoA).* **Figure 7:** *Bland-Altman plots agreement for inter-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in the medial gastrocnemius muscle tissue of athletes.Completed with the upper and lower limits of agreement (LoA).* **Figure 8:** *Bland-Altman plots agreement for inter-session measurements of the infrared thermography device designed for materials (Manual Infrared Camera PCE-TC 30) in athletes’ lateral gastrocnemius muscle tissue.Completed with the upper and lower limits of agreement (LoA)*
## Discussion
The proposed procedure within the Manual Infrared Camera PCE-TC 30 thermography device presented excellent intra- and inter-session reliability and repeatability with an adequate agreement avoiding systematic errors of measurement to measure skin temperature of soleus, medial and lateral gastrocnemius muscles. Although this tool was initially designed to assess the superficial temperature of materials (De Villoria et al., 2011; Pérez-Urrestarazu et al., 2014), it could be a less expensive device to promote triceps surae muscle prevention and recovery in athletes (Rodríguez-Sanz et al., 2017, 2018; Rodriguez et al., 2018; Benito-de-Pedro et al., 2019). In addition, the PCE-TC 30 thermal camera may be employed to assess the thermal behavior of fencing uniforms in athletes (Lamberti et al., 2020). Our study supports that this device may be directly used to determine superficial human muscle tissue temperature in the triceps surae of athletes with adequate reliability and repeatability (Rodríguez-Sanz et al., 2017, 2018; Rodriguez et al., 2018; Benito-de-Pedro et al., 2019; Requena-Bueno et al., 2020).
The PCE-TC 30 thermography device showed similar excellent reliability (ICC > 0.8) compared to the infrared ThermoHuman® device (Fernandez-Cuevas et al., 2017; Requena-Bueno et al., 2020), the digital infrared camera IRTIS-2000® (Zaproudina et al., 2008), and Thermofocus® thermal imaging device (Petrova et al., 2018), which were specifically designed for human tissue temperature measurements in lower limbs. Indeed, different ThermoHuman® devices showed an excellent intra-session (ICC = 0.99; LoA = 0.0 ± 0.4–0.1 ± 0.4 °C) and inter-session (ICC = 0.92; LoA = 0.1 ± 0.4–0.1 ± 0.5 °C) reliability before and after running, respectively, detailing small differences effect sizes (Cohen’s d < 0.4) for foot skin temperature (Requena-Bueno et al., 2020).
Specifically, the digital infrared camera IRTIS-2000® applied in the calf region showed adequate intra-session (ICC = 0.84) and inter-session (ICC = 0.66) reliability with similar temperature mean ± SD values for anterior (31.2 °C ± 0.6 °C), posterior (30.8 °C ± 0.6 °C), and lateral (31.3 °C ± 0.6 °C) calf regions (Zaproudina et al., 2008). Finally, the Thermofocus® thermal imaging device presented almost perfect intra-session agreement in different foot regions (ICC = 0.94–0.97), showing non-significant replication interactions (P-values = 0.23–0.84) (Petrova et al., 2018). Despite the use of such modern thermal imaging systems with better definition resolutions could provide more reliable measurements and improve the quality of the thermograms obtained, the reliability and measurement errors provided by the Manual Infrared Camera PCE-TC 30 thermography may be enough to detect temperature differences linked to clinical musculoskeletal changes (Côrte et al., 2019).
Athletes may be exposed to physical stress under training loads and competitions with overload reactions which could cause blood flow changes affecting skin temperature (Merla et al., 2010). Infrared thermography may not display anatomical abnormalities, although functional changes may be shown and linked to skin temperature control (Merla et al., 2010; Ring & Ammer, 2012). Infrared thermography use was proposed as a complementary tool to apply preventive measures, such as cryotherapy, physiotherapy, training load reduction, and massage or recovery boot use, to avoid muscle conditions in professional soccer players. The asymmetry reference values range from 0.5 °C to 1 °C between both right and left lower limbs was proposed to initiate this preventive protocol, which reduced up to $63\%$ muscle injuries in a professional soccer season by thermographic monitoring (Côrte et al., 2019). Thus, the Manual Infrared Camera PCE-TC 30 thermography device could be used to determine these cut-off values due to the MDC values varied from 0.515 °C to 0.587 °C and from 0.518 °C to 0.643 °C for intra- and inter-session evaluations, respectively.
Our research group carried out a prior thermographic study addressing the thermal skin evaluation of the triceps surae muscles. This study showed that skin temperature after running was deeply linked to electromyography, which may indirectly reflect triceps surae muscle activity. Although the Manual Infrared Camera PCE-TC 30 thermography device has not yet been correlated with electromyography values, thermal values could be related to muscle activity in the triceps surae muscles of athletes using this tool according to a similar study using the FLIR/SC3000/QWIP Thermacan-infrared thermal device (Rodriguez-Sanz et al., 2019).
Although some less expensive commercially-available thermal cameras could be suitable for skin temperature assessment, employing the PCE-TC 30 camera to assess triceps surae muscle temperature provided reliable and repeatable measures with MDC cut-off values useful to determine preventive protocols for muscle injuries (Côrte et al., 2019).
## Future studies
Further studies should be designed as randomized clinical trials to determine if these asymmetries reference cut-off values could prevent triceps surae muscle injuries (Côrte et al., 2019). According to prior studies (Hiemstra et al., 2007; Chung et al., 2015), the uninvolved normal side after injury may often be not normal, i.e., presenting temperature values different from healthy subjects, and cut-off values should also be detailed in the future muscle recovery studies. In addition, thermographic measurement of the triceps surae with this device should be analyzed by intra- and inter-rater reliability determining SEM and MDC values and correlated with the other high-end infrared devices as possible gold standards such as ThermoHuman® (Fernandez-Cuevas et al., 2017; Requena-Bueno et al., 2020), IRTIS-2000® (Zaproudina et al., 2008) and Thermofocus® (Petrova et al., 2018) tools. Furthermore, correlations with electromyography measurements of the triceps surae muscle activity should be carried out (Rodriguez-Sanz et al., 2019). Lastly, the intramuscular temperature should also be correlated with this device to determine concurrent validity concerning a gold standard (Burnham, McKinley & Vincent, 2006).
## Limitations
Various limitations should be considered for the use of this thermographic device. First, the MDC was superior to the lower limbs asymmetry reference range from 0.3 °C to 0.4 °C proposed as a cut-off for following-up before a preventive protocol (Côrte et al., 2019) and therefore, this device should not be used for values lower than 0.5 °C–0.6 °C.
Second, the concurrent validity of this device has not been performed for human tissue temperature, and this validity should be assessed in the future (Burnham, McKinley & Vincent, 2006).
Third, our sample only comprised healthy athletes, and further studies should analyze skin temperature with this tool over injured muscle tissue (Alburquerque Santana et al., 2022).
Fourth, our sample size calculation was accurately detailed to determine a moderate bivariate correlation between measurements, but our sample size was low to achieve the actual power to perform comparisons classifying groups depending on sport category, BMI, and other valued characteristics. In addition, despite statistical analyses were carried out according to our prior sample size calculation model to detail bivariate comparisons for intra- and inter-session measurements, future nested type studies should be designed as nested statistical models such as analyses of variance (ANOVA) to determine more accurate temperature comparisons.
Finally, in spite of the device was calibrated according to the manufacturer, the fact that the PCE-TC 30 thermography device was not designed for skin temperature assessment could affect the repeatability of the measurement to a lesser extent than its accuracy. Nevertheless, a comparison between the temperature assessed by a validated thermal camera and the PCE-TC 30 was not reported. Future studies should evaluate its accuracy due to a possible wrong estimation of the absolute skin temperature. Procedures for thermographic assessment in sports and exercise sciences have been reviewed by Moreira et al. [ 2017] in a consensus statement of the experts in the field. However, our reliability study aimed to standardize the proposed procedure and contributed to improving the methods behind measures.
## Conclusion
The proposed procedure within the Manual Infrared Camera PCE-TC 30 thermography device designed initially to measure the superficial temperature of materials presented excellent intra- and inter-session reliability and repeatability to measure skin temperature in the triceps surae muscle tissue of athletes. Future studies should consider the measurement errors of this procedure to measure the skin temperature of soleus, medial, and lateral gastrocnemius muscles to promote triceps surae muscle prevention and recovery in athletes.
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|
---
title: Anti-oxidant effect of nitrite in the pancreatic islets of type 2 diabetic
male rats
authors:
- Asghar Ghasemi
- Sevda Gheibi
- Khosrow Kashfi
- Sajad Jeddi
journal: Iranian Journal of Basic Medical Sciences
year: 2023
pmcid: PMC10008387
doi: 10.22038/IJBMS.2023.68245.14900
license: CC BY 3.0
---
# Anti-oxidant effect of nitrite in the pancreatic islets of type 2 diabetic male rats
## Abstract
### Objective(s):
Nitrite, a nitric oxide (NO) donor, increases insulin secretion from pancreatic islets and has positive metabolic effects in type 2 diabetes (T2D). Here, we test the hypothesis of whether nitrite-induced insulin secretion is due to blunting of diabetes-induced oxidative stress in the islets.
### Materials and Methods:
T2D was created in male rats using a combination of streptozotocin at 25 mg/kg and a high-fat diet. Wistar rats were assigned to 3 groups ($$n = 6$$ in each group), including control, T2D, and T2D+nitrite; the latter group consumed drinking water containing sodium nitrite (50 mg/l) for eight weeks. At the end of the study, mRNA levels of NADPH oxidase (Nox1, 2, 3, and 4), superoxide dismutase (SOD1, 2, and 3), glutathione peroxides (GPX1 and 7), glutathione reductase (GR), catalase, thioredoxin (TXN1 and 2), and thioredoxin reductase (TXNRD1) were measured in the isolated pancreatic islets.
### Results:
In the islets of diabetic rats, mRNA expressions of Nox1, 2, and 4 were higher, whereas expressions of SOD1, 2, catalase, GPX1, 7, GR, and TXN1 were lower than controls. Nitrite significantly (all P-values<0.05) decreased gene expression of Nox1 (0.39-fold) and Nox4 (0.23-fold) and increased gene expression of SOD1 (2.2-fold), SOD2 (2.8-fold), catalase (2.7-fold), GPX1 (2.2-fold), GPX7 (6.0-fold), GR (3.0-fold), TXN1 (2.1-fold), and TXNRD1 (2.3-fold) in diabetic rats.
### Conclusion:
Nitrite decreased oxidative stress in isolated pancreatic islets of rats with T2D by suppressing oxidants and augmenting anti-oxidants. These findings favor the notion that nitrite-induced insulin secretion is partially due to decreased oxidative stress.
## Introduction
Diabetes is linked with an astonishing death rate of one person every eight seconds [1]. Therefore, type 2 diabetes (T2D) management needs to change from only a glycemic-based to a pathophysiological-based view [2]. In addition, T2D is related to reduced bioavailability of nitric oxide (NO) [3]; thus, a novel strategy for the management of T2D is to increase NO bioavailability [4].
Experimental studies have displayed that nitrite, an NO donor, has favorable metabolic effects in T2D via increasing insulin secretion from the pancreas and decreasing insulin resistance (IR) [5-7]. Nitrite improves IR in T2D by multiple mechanisms, including (i) enhancing glucose uptake in the adipose tissue [8]; (ii) insulin-independent movement of glucose transporter 4 (GLUT4) to the membrane [6] via sirtuin3-AMP-activated protein kinase (AMPK)-dependent pathway [9] in skeletal muscle cells; (iii) decreasing adipocyte size [5]; (iv) decreasing transcription of cytokines involved in inflammation in the adipose tissue [5]; and (v) browning of white adipose tissue (WAT) [10]. Nitrite stimulates insulin secretion by increasing pancreatic islet insulin content [7] and blood flow [11]. Moreover, we recently reported that nitrite administration potentiates pancreatic insulin secretion via increasing insulin exocytosis [12].
Oxidative stress is related to the onset and development of T2D [13]. Pancreatic β-cells constitutively have a weak anti-oxidant defense system [14-16] and are at high risk of oxidative impairment [17]. The sensitivity of rat islets to peroxide radicals is 25 times higher than the liver [18]. The anti-oxidant protective capacity of the pancreatic β-cells may not be sufficient to deal with the challenges of modern lifestyles [14]. Under a hyperglycemic environment in T2D, reactive oxygen species (ROS) are formed in pancreatic islets [19]. This can impair insulin secretion [17, 20] and contribute to β-cell loss and function [16]. Nitrite has anti-oxidant properties in the heart and vascular tissue [21-23], as well as in lipopolysaccharide (LPS)-activated mouse macrophages and human monocytes [24]. Favorable metabolic effects of nitrite and its stimulatory effects on insulin secretion, synthesis, and exocytosis from pancreatic β-cells in T2D rats have been reported [7, 11, 12]; however, the underlying mechanisms of nitrite-induced insulin secretion have not been fully elucidated. Oxidative stress plays a key role in decreased insulin secretion; therefore, we asked the question as to whether nitrite’s stimulatory effect on glucose-induced insulin secretion (GIIS) in T2D is mediated by reducing oxidative stress in the pancreatic islets. We thus measured mRNA levels of the genes that play a key role in islets’ oxidative stress; for this purpose, we used isolated pancreatic islets from diabetic rats.
## Materials and Methods
Animals Male Wistar rats ($$n = 18$$, 2-month-old, 190–210 g) were kept in regular conditions with water and food ad libitum. The ethics committee of the Research Institute for Endocrine Sciences affiliated with Shahid Beheshti University of Medical Sciences approved all experimental procedures of the current study (IR.SBMU.ENDOCRINE.REC.1400.055; approved date: 2021-08-15).
Timeline of the study This study is an interventional experimental study; Figure 1 presents the timeline of the study. Rats were allocated to 3 groups ($$n = 6$$ in each group), including control, T2D, and T2D+nitrite. Animals in the T2D+nitrite group consumed drinking water containing sodium nitrite at 50 mg/L for 8 weeks, whereas rats in other groups received tap water only. This nitrite dose was chosen based on previous reports from our laboratory [7] and others [6, 25, 26], showing it to be safe, and producing favorable metabolic effects.
To measure water consumption (ml/24 hr/rat) and food intake (g/24 hr/rat), a certain amount of water (1000 ml in 2 water bottles) and food (500 g) was added to each cage (3 rats/cage). Then, the water remaining in the water bottles and food remaining in the cages at the end of each week were registered for 8 successive weeks using a graduated cylinder and a digital scale, respectively. In addition, levels of glucose and insulin were measured in the serum of fasted rats, and indices of IR [Homeostasis Model Assessment of IR (HOMA1-IR), updated HOMA-IR (HOMA2-IR)], and insulin sensitivity (quantitative insulin-sensitivity check index (QUICKI) were calculated as described previously [27]. Overnight (12 hr) fasted rats were anesthetized with sodium pentobarbital (60 mg/kg), and blood samples were collected by cutting the tail tips at week 0 (for the confirmation of diabetes in rats, which was concurrent with the start of nitrite administration) and week 8 (end of the nitrite administration). At week 8, pancreatic islets from all rats were separated by the Lacy & Kostianovsky method [7], GIIS, and mRNA of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase isoforms (Nox1, 2, 3, and 4), superoxide dismutase isoforms (SOD1, 2, and 3), glutathione peroxidase (GPX1 and 7), glutathione reductase (GR), catalase, thioredoxin (TXN1 and 2), and thioredoxin reductase (TXNRD1) were measured in isolated pancreatic islets using real-time PCR.
Induction of T2D T2D was induced in rats using a high-fat diet (HFD) and streptozotocin (STZ) [28]. Briefly, after 14 days of HFD feeding [29, 30], 25 mg/kg of STZ was intraperitoneally injected in all fasted rats in diabetic groups, and 7 days later, male rats with fasting serum glucose higher than or equal to 150 mg/dl were allocated in T2D groups, which continued to receive the HFD for the rest of the study [28].
For the preparation of the HFD, 1000 g of powdered regular pellet diet (Pars animal feed Company, Tehran, Iran), 531 g sheep butter as a source of fat, 125 g of casein (Iran Caseinate Company, Karaj, Iran) as a source of protein, 3 g of DL-methionine (Behparvar Company) to overcome low sulfur amino acids in the casein, 7 g of vitamin mix (Behroshd Company, Saveh, Iran), and 42 g mineral mix (Behroshd Company, Saveh, Iran) were thoroughly mixed to produce 1708 g of HFD. In the regular pellet diet, the total caloric value was ∼3160 kcal/kg, and calories received from fat, carbohydrate, and protein were 5.7, 72.2, and $22.1\%$, respectively (Table 1). In the prepared HFD, the total caloric value was ∼4900 kcal/kg, and calories received from fat, carbohydrate, and protein were 58.8, 27.0, and $14.2\%$, respectively. In the HFD/low-dose STZ model of T2D, rats consume HFD to induce IR, and after that, low-dose of STZ causes partial destruction of pancreatic β-cells. STZ dose (15–40 mg/kg), percent of calories received from fat (30–$67\%$), and duration of HFD before STZ injection (14–84 days) vary between studies [28]. However, most studies, including the initial ones that introduced the model [29, 30], used 14 days on HFD before STZ injection, as we did in the current study.
Serum glucose and insulin measurement *Fasting serum* glucose was measured using the glucose oxidase method (Pars Azmoon, Tehran, Iran, Cat. No.97001). Fasting serum insulin was measured using a rat-specific ELISA kit (Mercodia, Sweden, Cat. No.101113-10), respectively. The intra-assay coefficient of variations for glucose and insulin were $1.4\%$ and $8.4\%$, respectively.
Expression of target genes The sequence of primers is shown in Table 2. Total RNA from isolated pancreatic islets was extracted by the RNX-Plus solution kit (Cinagen Co., Tehran, Iran, Cat. No. EX6101). A NanoDrop-1000 spectrophotometer (Thermo Scientific, USA) was used to determine purity and quantity of the extracted total RNA. For the cDNA synthesis, extracted RNA was reverse transcribed with a cDNA synthesis kit (SMOBiO Technology, Taiwan). Related products in this kit are Reverse transcriptase (Cat. No. RP13002108402-1), RNase inhibitor (Cat. No. RL10002108111-8), dNTP Mix (Cat. No. CD10102108400-1), Oligo (dT) (Cat. No. CHRP032208402-1), Random Hexamers, (Cat. No. CHRP0342106600-1), and DEPC-Ttrated H2O (Cat. No. CHRP0521081101). Finally, 1 μl cDNA was amplificated in a real-time PCR machine (Rotor-Gene 6000, Corbett, Life science, Sydney, Australia) by a SYBR Green Master Mix (Thermo Fisher, USA Cat. No. 4309155). PCR reaction contained 1 µl cDNA, 1 μl of primers (forward and reverse), 7.5 μl Master Mix, and 5.5 μl DEPC-treated water, yielding a total volume of 15 µl. The thermal cycling settings included a 10 min (95 °C) initial denaturation followed by 40 cycles with 45, 45, and 60 sec at 94, 58, and 72 °C, respectively, and a five-min final extension at 72 °C.
Statistical analyses *Statistical analysis* was performed by GraphPad Prism software; all values are presented as mean±SEM. One-way analysis of variance followed by the Tukey post hoc test was used to compare water consumption and food intake, body weight, serum glucose and insulin, indices of insulin sensitivity/resistance, and insulin secretion at week 8. Relative expressions of genes were calculated based on their cycle thresholds versus β-actin as a reference gene with the REST software [31]. This software uses a randomization test to compare the difference between control and treated samples, which avoids any assumptions about data distribution and is therefore preferred over parametric tests [31]. The number of randomizations was set at 2000, which provides a reliable estimate of the P-value<0.05 [31].
## Results
Verification of model Consumption of HFD for two weeks before STZ injection produced IR in rats as indicated by higher HOMA1-IR (7.60±0.41 vs 2.86±0.27, $P \leq 0.001$) and HOMA2-IR (3.86±0.21 vs 1.88±0.24, $P \leq 0.01$) and lower QUICKI (0.287±0.002 vs 0.331±0.006, $P \leq 0.001$) compared with controls. The success rate for induction of the T2D model was $63\%$ ($\frac{12}{19}$); the range of glucose and insulin concentrations in the diabetic rats were 161–208 mg/dl and 113-181 pmol/L, respectively. At the end of the study, higher serum glucose ($101\%$, $P \leq 0.001$), serum insulin ($124\%$, $P \leq 0.001$), and lower GIIS from isolated islets ($45\%$, $P \leq 0.001$) were detected in diabetic rats. Moreover, rats with T2D had a higher HOMA1-IR ($334\%$) and HOMA2-IR ($167\%$), and lower QUICKI ($17.6\%$) at the end of the study.
Effect of nitrite on final body weight and intakes of water and food Rats with T2D had higher body weight by $12.7\%$ ($P \leq 0.001$), and water consumption by $46\%$ ($P \leq 0.001$) at week 8 while they had lower food intake by $27\%$ ($P \leq 0.001$) than controls. As shown in Table 3, nitrite administration in rats with T2D decreased body weight by $8.6\%$ ($P \leq 0.001$) but had no significant effect on water and food intake at week 8.
Effect of nitrite on serum glucose and insulin, insulin sensitivity/resistance, and insulin secretion As shown in Table 4, nitrite administration for 8 weeks to the rats with T2D decreased the concentration of serum glucose by $17.3\%$ ($P \leq 0.01$) and serum insulin by $19.6\%$ ($P \leq 0.05$), and increased islets GIIS by $39.1\%$ ($P \leq 0.001$). In addition, nitrite administration for 8 weeks to the rats with T2D resulted in decreased HOMA1-IR by $34.3\%$ ($P \leq 0.001$) and HOMA2-IR by $25.0\%$ ($P \leq 0.01$) and increased QUICKI by $7.5\%$ ($P \leq 0.05$).
Effect of nitrite administration on oxidative stress-related genes in islets of rats with T2D As shown in Figure 2, mRNA levels of Nox1, 2, and 4 were significantly higher in isolated islets of diabetic rats by 3.6, 2.3, and 3.8 folds, respectively; however, no change was observed in mRNA expression of Nox3 (Figure 2C). In addition, compared with the islets from T2D rats, nitrite administration decreased Nox1 (0.39-fold, $P \leq 0.01$) and Nox4 (0.23-fold, $P \leq 0.01$) gene expression (Figures 2A and 2D) in the diabetic rats. But, it had no significant effect on Nox2 and Nox3 expression (Figures 2B and 2C). Higher expression of Nox2 (1.9-fold. $P \leq 0.05$) was also observed in the T2D+nitrite group compared with the control group.
As shown in Figure 3, expressions of SOD1 and 2, and catalase were significantly lower in isolated islets of diabetic rats by 0.42, 0.37, and 0.26 folds, respectively. mRNA expression of SOD3 was comparable between groups (Figure 3C). Moreover, compared with non-treated diabetic rats, administration of nitrite to the diabetic rats increased mRNA expressions of SOD1 (2.2-fold, $P \leq 0.001$), SOD2 (2.8-fold, $P \leq 0.01$), and catalase (2.7-fold, $P \leq 0.01$) without affecting SOD3 expression (Figure 3C).
As shown in Figure 4, compared with controls, the following mRNA expressions were lower in isolated islets of rats with T2D: GPX1 (0.45-fold, $P \leq 0.05$), GPX7 (0.40-fold, $P \leq 0.05$), and GR (0.11-fold, $P \leq 0.001$). Nitrite administration increased the expression of the following genes: GPX1 (2.2-fold, $P \leq 0.05$), GPX7 (6.0-fold, $P \leq 0.001$), and GR (3.0-fold, $P \leq 0.01$). Higher expression of GPX7 (2.5-fold, $P \leq 0.05$) and lower expression of GR (0.34-fold, $P \leq 0.001$) were also observed in the T2D+nitrite group in comparison with the control group.
As shown in Figure 5, the expression of TXN1 was lower in isolated islets of diabetic rats by 0.48 ($P \leq 0.01$); however, no change was observed in the mRNA expression of TXN2 and TXNRD1 (Figure 5B and 5C). Moreover, administration of nitrite to the diabetic rats increased mRNA expressions of TXN1 (2.1-fold, $P \leq 0.01$) and TXNRD1 (2.3-fold, $P \leq 0.05$) in pancreatic islets of T2D rats, but it did not affect the expression of TXN2 (Figure 5C). Higher expressions of TXNRD1 (2.2-fold, $P \leq 0.05$) were also observed in the T2D+nitrite group compared with the control group.
## Discussion
The main result of the current study is that nitrite therapy attenuates T2D-induced increases in oxidative stress in isolated rat pancreatic islets. This finding indicates that the nitrite-induced increase in insulin secretion reported in our previous study [7] is at least in part due to blunting diabetes-induced oxidative stress in pancreatic islets.
In this study, serum glucose, serum insulin, and HOMA-IR were higher, and GIIS was lower in diabetic rats, indicating increased IR and reduced insulin secretion in the HFD-STZ model of T2D. Over an 8-week treatment by nitrite in diabetic rats, serum glucose and insulin concentrations were reduced, accompanied by decreased IR and increased GIIS; these findings are similar to others [5-7, 12]. The positive effect of nitrate/nitrite on glucose homeostasis in rats with T2D is due to reduced IR, increased insulin secretion, and pancreatic islet blood flow [11], increased glucose uptake in the skeletal muscle [6], increased expression of NO synthase enzymes in tissues sensitive to insulin effects [32], and increased insulin exocytosis [12]. In addition, nitrite decreased body weight without affecting food intake in diabetic rats, suggesting that the weight-reducing effect of nitrite was not associated with food intake in this study. The body weight-reducing effect of nitrate/nitrite in diabetic rats is due to WAT browning [10, 33].
In our study, mRNA levels of Nox1, 2, and 4 but not Nox3 were higher in the pancreatic islets of diabetic rats. Nitrite restored elevated expression of Nox1 and 4 but did not affect Nox2 and 3. The mitochondria, peroxisomes, endoplasmic reticulum, and cytosol are the primary sources that produce ROS in the pancreatic β-cells [14, 34]. Isolated rat islets express mRNAs of Nox1, 2, and 4 in a constitutive manner [35], where Nox1 and 2 are co-localized in the cell membrane and Nox4 is expressed in the ER, mitochondria, cell membrane, and the pancreatic β-cell nucleus [34]. In line with our results, expression of Nox (subunit p22phox, which is critical for Nox1-4 function [36]) is higher in the islets isolated from patients with T2D [37]. In addition, increased mRNA levels of Nox1, 2, and 4 but not Nox3 participate in the metabolic dysregulation of β-cells [36], whereas inhibition of Nox4 protects human islets against glucolipotoxicity [38]. Decreased activity or expression of Nox enzymes are among the possible mechanisms by which nitrite/nitrate can potentially reduce oxidative stress [39]. Nitrite inhibits Nox activity in the kidney [39], liver [40], macrophages [24], and vascular tissue [21]. Nitrite-induced decreased *Nox* gene expression, as observed in our study for Nox1 and Nox4 in the pancreatic islets, has been reported for Nox subunit p67 protein (part of in Nox2 and 3 holoenzymes) in the aorta [26] but not in the kidney [39]. This differential expression suggests that nitrite may exert its effects on Nox isoforms by different mechanisms in different tissues.
We showed that mRNA levels of SOD1 and SOD2 but not that of SOD3 were lower in the isolated islets of rats with T2D; nitrite increased SOD1 and SOD2 expressions to their natural values but did not affect SOD3 expression. In the pancreatic β-cells, Cu-Zn-SOD1 is expressed in the mitochondrial intermembrane space, cytosol, peroxisome, and the nucleus [34]. Mitochondrial Mn-SOD2 and Cu/Zn-SOD3 (EC-SOD) target the mitochondrial matrix and extracellular space, respectively [34]. Compared with the liver, SOD1 and SOD2 mRNA expressions in the pancreatic islets of male Wistar rats have been reported to be lower by $23\%$ and $55\%$, respectively [41]. In line with our results, lower expression of SOD1 [37, 42] and 2 [37] were observed in the pancreatic islets of patients with T2D [42]. Nitrite administration (50 mg/L) in drinking water for three weeks restores the decreased SOD activity in the aorta of aged (26 to 28-month-old) mice, but it did not affect SOD1 and SOD2 protein expression [26]. However, nitrite administration did increase SOD1 mRNA expression in the mesenteric arteries of hypertensive rats [23] and cardiac tissue of mice with congestive heart failure (CHF) [43].
In the current study, rats with T2D had lower islet expression of catalase, GPX (GPX1 and 7), and GR. Nitrite increased the transcription of these genes, particularly that of GPX7. Catalase and cytoplasmic GPX are hydrogen peroxide-inactivating enzymes [41], of which very low levels are found in the pancreatic islets [41]. Catalase and GPX activities in the pancreatic rat islets are 1-$3.5\%$ [18, 41] and $21\%$ [41] of those found in the liver. Catalase and GPX1 localize in the peroxisomes and cytosol, whereas GPX7 localizes in ER [34]. Thus, overexpression of GPX and catalase protect the β-cells against hydrogen peroxide toxicity [41]. In addition, GPX1 overexpression prevents, whereas buthionine sulfoximine, which depletes cellular glutathione levels, augments ribose-induced increases in peroxide levels, leading to decreased insulin mRNA, insulin content, and GIIS in rats islets [44]. In line with our results, nitrite increases mRNA levels of catalase and GPX1 in vascular tissue from hypertensive rats [23] and heart tissue of CHF mice [43]. In addition, in the heart, nitrite increases the reduced/oxidized-glutathione (GSH/GSSG) ratio in rats exposed to hypoxia [45]. Of significance, the SOD-to-catalase ratio is higher in insulin-producing cells than in other tissue, favoring the accumulation of hydrogen peroxide [41]. In our study, than controls, the ratio of SOD3-to-catalase was 3.17 in the diabetic group; this decreased to 1.31 in the diabetes+nitrite group reflecting a 2.4-fold change, which is more relevant in determining overall sensitivity to hydrogen peroxide toxicity [41].
In the current study, rats with T2D had lower expression of TXN1; nitrite administration increased this and that of TXNRD1. TXN-TXNRD1 plays a significant role in detoxifying hydrogen peroxide in the β-cells [46]. In line with our results, nitrite increases TXN1 gene expression in the vascular tissue of hypertensive rats [23], and NO donors increase TXNRD1 expression in pancreatic islets [47]. S-nitrosylation, the NO-dependent alteration of protein thiols to form S-nitrosothiols (SNO), is one of the principal ways that NO exerts its effects, including gene transcription [48]. GSH, thioredoxin, and their related redox systems are involved in SNO reduction [48]. TXNRD1 reduces oxidized TXN and keeps it active [48]. In addition, TXN1 can function as a denitrosylase to protect soluble guanylyl cyclase (sGC) sensitivity to NO [49].
Previously we reported that in T2D, nitrite increases islet insulin mRNA levels [12] and content [7], stimulates insulin secretion from islets [7], and increases mRNA expression of proteins involved in insulin exocytosis [12]. Other studies on the islets or β-cell lines show that high glucose concentrations increase intracellular ROS production [20, 44, 50], inhibiting insulin secretion. Ribose, which produces ROS more robustly than glucose, inhibits GIIS and decreases insulin content and mRNA levels; N-acetylcysteine prevents these effects [44]. Tert-butyl hydroperoxide, an agent which induces oxidative stress, lowers islet GSH content by $37\%$ and decreases GIIS by $67\%$ [18]. In addition, GIIS was potentiated by $38\%$ in Nox4-deficient islets [20], indicating stimulatory effects of Nox4 inhibition on insulin secretion, as observed in our study following nitrite administration. Collectively, hyperglycemia increases ROS production, and long-term ROS increase can cause dysfunction and death of β-cell [34]. Therefore, the anti-oxidant effects of nitrite on the pancreatic β-cells and its effect on increasing insulin secretion are relevant because T2D is frequently accompanied by oxidative stress. Dietary consumption of green leafy vegetables would be a viable means of providing adequate nitrate/nitrite supplementation in T2D patients. Drugs targeting oxidative stress may be advantageous in the future treatment of T2D [34], and enhancing β-cell anti-oxidant activity may preserve residual β-cell function after the onset of T2D [44].
As for strengths, first, we provided efficiency-corrected relative gene expression, which is highly recommended [31] and prevents any miscalculated differences in expression ratios [51]. Second, in the model of T2D in the current study, HFD induces IR, and STZ induces partial β-cell dysfunction; metabolic characteristics of this model, therefore, mimic the pathophysiology of T2D in humans [28]. Finally, the dose of nitrite (50 mg/l in drinking water) used in the current study is ~5.8 mg/kg (based on 38 ml/day of water consumption and 330 g of average body weight in nitrite-treated rats), which translates to a human equivalent dose of 0.93 mg/kg [52]. This nitrite dose is achievable through vegetable and fruit consumption, it is safe, and represents a low nitrite dose in humans [10, 53]. As a limitation, the expression of the protein of studied genes was not measured in the current study. However, anti-oxidant enzyme activities of the tissues are chiefly determined by their mRNA levels [41], and expression of oxidative stress-response genes at mRNA levels in islets of rats with T2D is used as an indicator of β-cell oxidative stress [34, 54].
**Figure 1:** *Study timeline* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 TABLE_PLACEHOLDER:Table 4 **Figure 2:** *Effect of nitrite administration on mRNA expression of NADPH oxidase (Nox) isoforms [Nox1 (A), Nox2 (B), Nox3 (C), and Nox4 (D)] in rats with type 2 diabetes (T2D). Symbols ** and *** show significant differences at P<0.01 and P<0.001, respectively, vs the control group. Symbol ## shows a significant difference at P<0.01 vs the diabetic group. Values are mean±SEM (n=6/group)* **Figure 3:** *Effect of nitrite administration on mRNA expression of superoxide dismutase (SOD) isoforms [SOD1 (A), SOD2 (B), and SOD3 (C)] and catalase (D) in rats with type 2 diabetes (T2D). Symbols ** and *** show significant differences at P<0.01 and P<0.001, respectively, vs the control group. Symbols ## and ### show significant differences at P<0.01 and P<0.001, respectively, vs the diabetic group. Values are mean±SEM (n=6/group)* **Figure 4:** *Effect of nitrite administration on mRNA expression of glutathione peroxidase (GPX) isoforms [GPX1 (A) and GPX7 (B)] and glutathione reductase (GR, C), in rats with Type 2 diabetes (T2D). Symbols * and *** show significant differences at P<0.05 and P<0.001, respectively, vs the control group. Symbols #, ##, and ### show significant differences at P<0.05, P<0.01, and P<0.001, respectively, vs the diabetic group. Values are mean±SEM (n=6/group)* **Figure 5:** *Effect of nitrite administration on mRNA expression of thioredoxin (TXN) isoforms [TXN1 (A) and TXN2 (C)], and thioredoxin reductase (TXNRD1, C), in rats with Type 2 diabetes (T2D). Symbols * and ** show significant differences at P<0.05 and P<0.001, respectively, vs the control group. Symbols # and ### show significant differences at P<0.05 and P<0.001, respectively, vs the diabetic group. Values are mean±SEM (n=6/group)*
## Conclusion
Nitrite administration in diabetic rats decreased oxidative stress in isolated pancreatic islets by suppressing oxidants and augmenting anti-oxidants. Therefore, nitrite-induced insulin secretion is at least in part due to decreased oxidative stress. These findings are relevant for potential translational nutritional-based intervention studies using nitrite.
## Authors’ Contributions
SJ, SG, KK, and AG designed the experiments; SJ, SG, and AG performed experiments and collected data; SJ and AG discussed the results and strategy; AG supervised, directed, and managed the study; SJ, SG, KK, and AG approved the final version to be published.
## Conflicts of Interest
None.
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|
---
title: Antihypertensive, antidyslipidemic, and renoprotective effects of Bursera simaruba
on metabolic syndrome
authors:
- Elizabeth Alejandrina Guzmán Hernández
- David Segura Cobos
- María del Rosario González Valle
- José del Carmen Benítez Flores
- Rubén San Miguel Chávez
- Leonardo del Valle Mondragón
- Gil Alfonso Magos Guerrero
- Pedro López Sánchez
journal: Iranian Journal of Basic Medical Sciences
year: 2023
pmcid: PMC10008388
doi: 10.22038/IJBMS.2023.66530.14601
license: CC BY 3.0
---
# Antihypertensive, antidyslipidemic, and renoprotective effects of Bursera simaruba on metabolic syndrome
## Abstract
### Objective(s):
Metabolic syndrome is associated with the development of chronic kidney disease. Bursera simaruba “chaca” is a medicinal plant used in Mexico for hypertension and empirical therapy. In this study, were examined the effects of ethanol extract of B. simaruba on metabolic syndrome.
### Materials and Methods:
For induction of metabolic syndrome, $20\%$ fructose was used, and it was administered in the water and food to male Wistar rats for 12 weeks, after administering ethanol extract of B. simaruba intragastrically (100 and 200 mg/kg/day) for 6 weeks, blood pressure was determined. In plasma, glucose, cholesterol, triglycerides, angiotensin II, oxide nitric, and angiotensin 1-7 were quantified. In the kidney was performed histological study and the activity of anti-oxidant enzymes was quantified.
### Results:
Rats with metabolic syndrome developed obesity, arterial hypertension, dyslipidemia, and kidney damage characterized by proliferative glomerulonephritis, necrosis, and reduced activity of anti-oxidant enzymes. These alterations were significantly ameliorated by ethanol extract of B. simaruba.
### Conclusion:
The ethanolic extract of B. simaruba showed antidyslipidemic, antihypertensive, anti-oxidant, and renoprotective effects.
## Introduction
Metabolic syndrome (MS) represents one of the main risk factors for the development of cardiovascular disease and diabetes mellitus; in Mexico, obesity represents one of the main public health problems, derived from changes in lifestyles, characterized by the consumption of foods rich in carbohydrates and fats, combined with a sedentary lifestyle. Despite the existence of effective drugs for the treatment of MS, their use is still limited, especially in rural areas, in Mexico the use of medicinal plants to treat various conditions continues to prevail [1-3].
Bursera simaruba is a tree of the family Burseraceae, it produces lignans, and is widely used by the Mexican native population for different health issues; traditionally B. simaruba is used for its medicinal properties including relief from pain, inflammation, and rheumatism, and can help treat illnesses such as colds, skin tumors, polyps, venereal diseases, and hypertension [4, 5].
The objective of this study was to evaluate the effect of ethanol extract of B. simaruba in MS.
## Materials and Methods
B. simaruba was collected in the municipality of Cerro Azul, Veracruz, Mexico (N21°11″, W97°44′00″), It was identified by botanists at the National Autonomous University of Mexico.
For in vivo evaluation, 3 kg of fresh leaves of B. simaruba were obtained, they were allowed to dry, later they were macerated in ethanol for 14 days, and an exhaustive extraction was carried out at reduced pressure using a rotary evaporator to eliminate the solvent until a volume of 50 ml was obtained, and dried in an oven at 40 °C.
Determination of phenolic acids, flavonoids, and terpenoids The HPLC analysis was carried out in the Phytochemistry Laboratory of the Postgraduate College under the supervision of Dr Marcos Soto Hernández and with the advice of M. en C. Rubén San Miguel Chávez. The determination of phenolic acids, flavonoids, and terpenoids present in the ethanolic extract of B. simaruba was carried out in a Hewlett Packard® 1100 series liquid chromatograph, equipped with an automatic injector (Agilent®, 1200 Series Mod. G1329A) and a Hewlett Packard® 1100 series diode array detector.
Induction of metabolic syndrome Male Wistar rats with an initial weight of 250 ± 15 g were used and were provided by the Bioterium of the Faculty of Higher Studies Iztacala, which was carried out under the Mexican Norm (NOM-062- ZOO-1999). For the induction of MS, following the methodology described by Merino et al., [ 2014], rats with MS were treated orally for 6 weeks, integrating the following groups: captopril (30 mg/kg) (MS + CAP) [7], atorvastatin (10 mg/kg) (MS+ ATOR) [8], and ethanolic extract of B. simaruba (100 and 200 mg/kg) (MS+ EtOH).
The biochemical analyses in plasma (total glucose, cholesterol, and triglyceride) were measured using an Accutrend Sensor glucometer (Roche); systolic arterial blood pressure (SBP) was measured noninvasively using a tail-cuff computer-aided monitoring device (Automatic Blood Pressure Computer, Model LE 5007; Letica Scientific Instruments, Barcelona, Spain) using the procedures described by Guzman et al. [ 2015]; it was carried out at the beginning (0 weeks), middle (12 weeks), and end (18 weeks).
The animals were placed in individual metabolic cages to determine the consumption of water, food, and urinary volume. Urinary protein was measured using the Bradford method [9]. Rats were later sacrificed, and blood was collected (3 ml) from the abdominal vein for biochemical analysis of high-density lipoprotein cholesterol (HDLc) (Spinreact, Cat. 1001097) and LDLc (Spinreact, Cat. 41023) levels were measured using commercially available kits following the manufacturer´s protocol. The cardiac and atherogenic index was determined according to what was described by Ajiboye et al. [ 2014].
For the determination of plasma concentration of angiotensin II, angiotensin [1-7], nitric oxide, and endothelin were measured using Capillary Electrophoresis (Tenorio et al., 2010).
Kidney cortex tissue samples were obtained for histological analysis and anti-oxidant activity of catalase and superoxide dismutase enzymes, as described by Ajiboye et al. [ 2014].
Statistical analysis *The data* are the mean ± SEM, statistical analyses were performed using GraphPad Software (USA), and their interactions by two-factor analysis of variance and means were compared using Tukey´s multiple comparisons post hoc test.
## Results
The analysis of high-performance liquid showed the presence of terpenoids: α-amyrin ($35\%$), oleanolic acid ($22.8\%$), and ursolic acid ($9.71\%$); Flavonoids: naringenin ($6.7\%$), phloridzin ($2.28\%$); Phenolic acids: ferulic acid ($5.15\%$), chlorogenic acid ($2.9\%$), caffeic ($0.5\%$), ferulic ($5.15\%$), gallic acid ($0.11\%$), and syringic acid ($0.22\%$) (Figure 1 and Table 1).
Effect of ethanolic extract of B. simaruba on metabolic syndrome In the present study we show that after the administration of $20\%$ fructose in the water and food for 12 weeks, the animals developed three of the 5 risk factors characteristic of MS: obesity, quantified through body mass index, abdominal circumference, and Lee’s index; dyslipidemia; and arterial hypertension (Table 2). As can be seen in Table 3, captopril, atorvastatin, and ethanolic extract of B. simaruba with the dose of 100 and 200 mg/kg decreased the three indicators of obesity, which suggests that ethanolic extract from B. simaruba has an anti-obesity effect.
The dyslipidemia was characterized by an increase in the total concentration of triglycerides, low-density lipoprotein, and very low-density lipoprotein (Figure 2a); treatment with ethanolic extract of B. simaruba, with the dose of 100 and 200 mg/kg, decreased the plasmatic concentration of total lipids and increased high-density lipoprotein (Figure 2b), which suggests a protective effect of cardiovascular and arteriogenic risks (Figures 2 d, 2e, and 2f).
Effect of ethanolic extract B. simaruba on hypertension To determine if the ethanolic extract of B. simaruba influences arterial hypertension, blood pressure was determined after six weeks of treatment as can be seen in Figure 3; animals with MS increased blood pressure (MS: 148 ± 3 mmHg vs 100 ± 5 mmHg control) (Figure 3). This increase was associated with an increase in plasma Ang II and a decrease in vasodilator mediators such as nitric oxide and angiotensin 1-7 (Figure 4b). EtOH showed a preventive effect on the increase in blood pressure induced after MS and was associated with increased nitric oxide and plasma angiotensin 1-7 (Figures 4a and 4c).
Effect of ethanolic extract B. simaruba on damaged kidney *In this* study it was found that animals with MS developed kidney damage characterized by hypertrophy that was determined by kidney weight/total body weight ratio, (Control: 2.0 ± 0.02, MS: 2.5 ± 0.02 g); urine protein excretion (MS: 120 ± 5.3 mg/24 hr vs 20.6 ± 2.6 mg/24 hr control), and oxidative stress characterized by a decrease in the activity of anti-oxidant enzymes catalase and superoxide dismutase (Figures 5a and 5b). Histologically it was observed as proliferative glomerulonephritis, mainly affecting the mesangial cells. The inflammatory phenomenon extends into the interstitium, in which the presence of protein deposits was observed. The most affected glomerular structure corresponds to the proximal contoured tubules (TCP), followed by distal contoured tubules (TCD) (Figure 4b), in which the presence of moderate and multifocal necrosis was observed. The ethanolic extract of B. simaruba (100 and 200 mg/kg) reversed renal hypertrophy (EtOH 100 and 200 mg/kg):2.0 ± 0.035 and 1.98 ± 0.025 g), decreased excretion of proteins and activity of anti-oxidant enzymes were restored (Figures 5a and 5b). Histologically it was observed that necrosis was reduced, mainly in the proximal contoured tubules, and the regeneration of the renal corpuscle and the proximal contoured tubules was promoted, mainly, regenerative phenomenon occurs simultaneously by regions; in renal corpuscle, glomerular capillaries have narrow light and space of small Bowman’s capsule, indicating poor circulation and absence of glomerular filtration (Figures 4e and 4f).
In the areas with regeneration, a little interstitium was observed, so there are no blood vessels and the proximal contoured tubules consist of cords of epithelial cells without a lumen or with very narrow light. The epithelial cells of these cords can be cubic and have no developed microvilli edges. Occasionally, cells in mitosis and nucleated cells can be identified (Figures 4e and 4f).
Regarding treatment with captopril, similar effects were found although the regeneration is lower compared with the ethanolic extract of B. simaruba. Atorvastatin treatment reduces tubular necrosis (Figure 4c).
**Figure 1:** *Chromatographic profile of ethanolic extract of Bursera simaruba* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 **Figure 2:** *Lipid profile: (a) LDL, (b) HDL, (c) VLDL, (d) atherogenic, (e) cardiac, and (f) coronary artery indexes in rat. Values are the mean ± SEM (n = 6), *P<0.05 control vs treatment; &P<0.05 metabolic syndrome (MS) vs treatment. captopril (CAP), atorvastatin (ATOR), ethanolic extract (EtOH)* **Figure 3:** *Effect of ethanol extract of Bursera simaruba on the plasma level of (a) nitric oxide, (b) angiotensin II, and (c) angiotensin 1-7. All values are represented as mean ± SEM. n = 6; *P<0.05 control vs treatment; &P<0.05 metabolic syndrome (MS) vs treatment. captopril (CAP), atorvastatin (ATOR), ethanolic extract (EtOH)* **Figure 4:** *Photomicrographs in which different regions of the renal cortex. (CR) renal tissue with degenerative and necrotic change (N). (R) regenerated renal tissue. a) control, b) metabolic syndrome (MS), c) MS + captopril, d) MS + atorvastatin, e) MS + ethanolic extract 100 mg/kg, and f) MS + ethanolic extract 200 mg/kg* **Figure 5:** *Effects of EtOH of Bursera simaruba on anti-oxidant enzymes, (a) catalase (CAT) and (b) superoxide dismutase (SOD). All values are represented as mean ± SEM. n = 6; *P<0.05 control vs treatment; &P<0.05 Metabolic syndrome (MS) vs treatment. captopril (CAP), atorvastatin (ATOR), ethanolic extract (EtOH)*
## Discussion
MS is due to a group of risk factors and increases the risk of cardiovascular diseases and type 2 diabetes mellitus; its prevalence has increased in recent years worldwide, representing a public health problem. Lifestyle changes can reverse the components of MS, but sometimes pharmacological intervention is necessary for stricter control of these risk factors. We investigated the antihypertensive, antidyslipidemic, and renoprotective effects of the ethanolic extract of B. simaruba on the MS model in rats.
The present study showed that after 12 weeks of ingesting $20\%$ fructose in water and food, three of the 5 risk factors that make up MS (arterial hypertension, obesity, and dyslipidemia) were consistent with the previous studies [6, 10, 12].
The accumulation of fat and high triglyceride levels observed in rats with MS in the present study explains the increase in body weight. Three indicators of obesity including the percentage of body weight gain, BMI, and abdominal circumference were measured; treatment with ethanolic extract showed antihyperlipidemic and anti-obesity effects; these effects may be due to the presence of caffeic acid, chlorogenic acid, α−amyrin, and naringenin in the extract; these secondary metabolites are inhibitors of fatty acid synthase and peroxisome proliferator-activated receptor alpha expression in the liver [13, 14].
Free fatty acids can bind to specific receptors on endothelial cells, causing damage to the proper functioning of these cells, so they can be damaged and stop the synthesis of compounds such as nitric oxide and increase the effect of vasoconstrictor mediators such as angiotensin II. We found that after 12 weeks of induction of MS, the activity of the renin-angiotensin system increases, since treatment with captopril blood pressure is reduced by up to $60\%$, as has been shown in other works [15,16]. The administration of the ethanolic extract of B. simaruba for 6 weeks showed a preventive effect on the increase in blood pressure induced by the MS, this effect can be attributed to the presence of caffeic and chlorogenic acids, that according to literature inhibit the activity of the angiotensin-converting enzyme [17, 18].
Excess free fatty acids can accumulate around kidneys, and this accumulation can cause lipotoxicity, loss of proper functioning, and adaptive changes such as hypertrophy and proteinuria [19, 20].
The ethanolic extract of B. simaruba slowed the progression of functional and structural damage to the kidney in MS, this effect was attributed to the presence of oleanolic, ursolic, and ferulic acids in other studies. The presence of these secondary metabolites has shown a renoprotective effect through decreasing apoptosis in the kidney [21-23].
Researchers demonstrated that high-fructose feeding increases oxidative stress resulting from an imbalance between the production of free radicals such as reactive oxygen species and reactive nitrogen species and the production of endogenous anti-oxidant defenses such as superoxide dismutase, catalase, and reduced glutathione; in MS rats they are decreased [23-25]. The ethanolic extract of B. simaruba restored the activities of anti-oxidant enzymes, which suggests that part of the mechanism of action by which B. simaruba has a renoprotective effect is its anti-oxidant effect, as has been shown in other studies. Flavonoids have phenolic hydroxyl groups and excellent iron chelation properties and other transition metals, or through their binding to transcription factors, one of the compounds present in EtOH is oleanolic acid, which has been shown to activate the redox transcription factors as nuclear factor erythroid 2 (Nrf2) [26, 27].
## Conclusion
The ethanolic extract of B. simaruba showed antidyslipidemic, antihypertensive, anti-oxidant, and renoprotective effects.
## Authors’ Contributions
DSC conceived the study. GAMG designed the study. DSC defined the intellectual content. DSC, MDR GV, LDC BF, R SMC, LDVM, GAMG, PLS, and EAGH performed the literature search. DSC, MDRGV, LDCBF, RSMC, LDVM, GAMG, PLS, and EAGH performed experimental studies. DSC, MDRGV, and LDCBF performed data acquisition. RSMC, LDVM, GAMG, PLS, and EAGH analyzed the data. GAMG, PLS, and EAGH performed statistical analysis. DSC, MDR GV, LDC BF, R SMC, LDV M, GA MG, PLS, and EAGH prepared the manuscript.
## Conflicts of Interest
The authors declare that no conflict of interest exists.
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|
---
title: Comparison of the anti-diabetic and nephroprotective activities of vitamin
E, metformin, and Nigella sativa oil on kidney in experimental diabetic rats
authors:
- Hayat Ayaz
- Seval Kaya
- Ugur Seker
- Yusuf Nergiz
journal: Iranian Journal of Basic Medical Sciences
year: 2023
pmcid: PMC10008389
doi: 10.22038/IJBMS.2023.68051.14876
license: CC BY 3.0
---
# Comparison of the anti-diabetic and nephroprotective activities of vitamin E, metformin, and Nigella sativa oil on kidney in experimental diabetic rats
## Abstract
### Objective(s):
In this study, we aimed to evaluate and compare the nephroprotective and possible anti-diabetic effects of vitamin E, metformin, and Nigella sativa.
### Materials and Methods:
Thirty male Wistar Albino rats were randomly divided into control, experimental diabetes (DM), vitamin E + DM, Metformin + DM, and N. sativa + DM. For experimental diabetes induction, IP 45 mg/kg streptozotocin was administered. Rats in vitamin E + DM, Metformin + DM, and N. sativa + DM received 100 mg/kg vitamin E, 100 mg/kg metformin, and 2.5 ml/kg N. sativa oil for 56 days. After the experiment, all animals were sacrificed, and blood and kidney samples were collected.
### Results:
The blood urea level of the DM group was significantly higher ($P \leq 0.01$) than the control group. Urea levels in vitamin E, metformin, and N. sativa groups were similar to the control group ($P \leq 0.05$) but significantly different from the DM group ($P \leq 0.01$). Bax, caspase-3, and caspase-9 immunopositivity intensity were quite low in the control group, and similar to the N. sativa group ($P \leq 0.05$). Bcl-2 immunopositivity density was highest in the N. sativa group, similar to the control group in terms of percentile area ($P \leq 0.05$).
### Conclusion:
When all three treatment methods were compared in terms of their effectiveness in alleviating DM and DN, it was determined that the most successful result was obtained with N. sativa oil.
## Introduction
DM is a complex disease that affects individuals with disruptions in insulin secretion and is characterized by chronic hyperglycemia [1, 2]. According to the International Diabetes Federation, it is predicted that 537 million adults are suffering from DM, and this number will reach 783 million by 2045 [3]. Hyperglycemia-related nephropathy, retinopathy, neuropathy, and hepatopathy are some of the important clinical outcomes and the most important reasons for mortality in DM [4]. Diabetic nephropathy (DN) is one of the most common and severe microvascular complications in DM [5]. Improved blood urea level is another parameter linked with renal failure in DN [6]. It is also believed that long-term and uncontrolled hyperglycemic conditions, as observed in DN, may increase the accumulation of reactive oxygen species (ROS) in renal tissue that triggers apoptotic signaling and programmed cell death [7]. For that reason, numerous researchers have performed experimental studies to find out the most effective, cheap, and safest treatment methods besides the only approved modality, exogenous insulin administration, in DM. Most of these experiments have focused on anti-oxidant substances due to oxidative stress being one of the most important key regulators in diabetic complications [8].
Vitamin E is a cheap and easily reachable anti-oxidant that has the potential to scavenge ROS end products of lipid peroxidation. Vitamin E also improves kidney function parameters and prevents podocyte loss in DN [9, 10]. However, the strength of vitamin E in diabetes complications has not been fully explored yet, and there are some controversial results reported in current knowledge [11-13]. Although metformin is widely used in type 2 DM or type 1 DM patients with insulin resistance, recent observations indicated that it is a reliable treatment way to control type 1 DM and its complications [14].
Nigella sativa (NS) has been used as an alternative treatment for various diseases due to bearing anti-diabetic, anticancer, antihypertensive, and anti-inflammatory properties [15-17]. For that reason, in this study, we aimed to investigate the nephroprotective effects of vitamin E, metformin, and NS in streptozocin (STZ)-induced diabetic rats comparatively.
## Materials and Methods
Study design This study was carried out with the approval of the dicle University Animal Experiments Local Ethics Committee (dated 28.03.2019 and 19-04). Thirty Wistar Albino male rats, 10-12 weeks old, weighing 240-350 g, were divided into 5 groups ($$n = 6$$): control, DM, DM + vitamin E, DM + Metformin, and DM + NS. The animals were housed under a 12-hr day and 12-hr light cycle at a room temperature of 24-26 °C with 55-$60\%$ humidity. During the experiment, animals were provided with standard pellet feed and tap water ad libitum. Animals in the control group were not exposed to any application except daily administration of 1 ml saline (P.O.). Experimental DM was established by dissolving a single dose of 45 mg/kg STZ (Sigma, USA) in sodium citrate solution (pH: 4.5, 0.1 M) and administering intraperitoneally to the animals in DM, DM + vitamin E, DM + Metformin and DM + NS groups [18]. Blood glucose levels of animals were measured after 72 hours, and animals with ≥250 mg/dl blood glucose levels were considered experimental diabetics [19]. Animals in DM + vitamin E, DM + Metformin, and DM + NS groups were administered 100 mg/kg vitamin E [20], 100 mg/kg metformin [21], 2,5 ml/kg NS oil [22] by oral gavage, respectively. At the end of the 56-day experiment, the animals were sacrificed by drawing blood from their heart.
Measurement of biochemical parameters Blood samples taken from the sacrificed animals were centrifuged at 4000 rpm for 15 minutes to determine the serum urea level. Biochemistry parameters were studied with Beckman Coulter (AU5800, Germany) device using the photometric method.
Tissue processing and staining The kidney tissues of the sacrificed animals were fixed in $10\%$ formalin and embedded into paraffin blocks after routine histopathological tissue processing protocol. After that, 5 µm thick sections were obtained from paraffin blocks and stained with hematoxylin and eosin (H&E), Periodic acid–Schiff (PAS), and Masson Trichrome. For that purpose, tissue sections were deparaffinized in xylene and dehydrated through a decreasing series of alcohol, and washed in distilled water. The samples were stained with PAS (Bio Optica, lot/cat#4117) and Masson Trichrome (Bio Optica, lot/cat# 3916) with ready-to-use kits. Staining steps were performed according to the manufacturer’s instructions.
Immunohistochemical staining Deparaffinized sections were passed through a decreasing series of alcohol, brought into distilled water, and washed in phosphate buffered saline (PBS). Antigen retrieval was performed in an ethylenediamine tetraacetic acid (EDTA) solution. Sections were washed in PBS and treated with $3\%$ hydrogen peroxide solution for 20 minutes. Ultra V Block solution was applied to the sections for 8 minutes. Bax (sc7480 Santa Cruz Biotechnology, Inc. Oregon, USA dilution: 1:200), Bcl-2 (sc7382 Santa Cruz Biotechnology, Inc. Oregon, USA dilution: 1:200) caspase-3 (sc56053 Santa Cruz Biotechnology, Inc.) Oregon, USA dilution: 1:150), and caspase-9 (sc56076 Santa Cruz Biotechnology, Inc. Oregon, USA dilution: 1:250) primary antibodies were applied onto the tissue sections and incubated at +4 °C overnight. The samples were incubated with secondary antibodies and enzymes at room temperature for 15 min each. After the washing process, diaminobenzidine (DAB) was applied as a chromogen, and the reactions of the sections were monitored under a light microscope. Then, the samples were counterstained with hematoxylin and mounted with Entellan™. The obtained sections were analyzed with the Image J program. For that purpose, the ratio of DAB positive within the total tissue area was automatically measured with the software. The obtained data were analyzed statistically.
Statistical analysis Obtained datasets were evaluated statistically. For that purpose, a normality test was performed and as a result of skewness and kurtosis, it was determined to use the parametric One-Way ANOVA test. Multiple comparisons were evaluated with Tukey’s post hoc test and the results were shown as mean ± SD. The $P \leq 0.05$ was considered significant.
## Results
Biochemical results The lowest blood urea level was observed in the control group (41.00±4.65 mg/dl), and there was a significant difference ($P \leq 0.01$) between the control and DM groups (73.20±8.32 mg/dl). Blood urea levels of the metformin (47.60±12.22 mg/dl) and vitamin E (49.20±13.01 mg/dl) groups were similar to the control group ($P \leq 0.05$), and there was a significant difference ($P \leq 0.01$) compared to DM group. It was concluded that the blood urea level of the NS (43.00±9.35 mg/dl) group was similar to the control group ($P \leq 0.05$), but it was significantly different from the DM group ($P \leq 0.01$). Graphical demonstrations of statistical analyses are shown in Figure 1.
Histopathological results Sections of the control group had normal histological structures. Atrophy and tubular necrosis were observed in the proximal convoluted tubules in the DM group. Pycnotic nuclei and cytoplasm with irregular vacuoles were detected in some tubular epithelial cells. In the sections stained with PAS, loss of the brush border, thickening, and perforation of the basement membranes were observed. We observed pycnosis in tubular epithelial cells and congestion in glomerular capillaries in the vitamin E group. Tubular atrophy was detected with irregularities in the brush margins and basement membranes in the sections stained with PAS. In the H&E stained sections of the metformin group, necrosis in tubule cells and congestion in glomerular capillaries were observed. Interstitial fibrosis was detected in Masson’s trichrome staining. Irregularities in the brush edges and basement membranes were evident in kidney sections stained with PAS. Kidney sections belonging to the NS group had a normal appearance of glomerular and tubular structures. There was no evidence of interstitial hemorrhage, tubular atrophy, and tubular necrosis. In the sections stained with PAS, glomerular basement membrane with normal histological structure, tubular basement membrane, and brush-like borders were observed (Figure 2).
Immunohistochemical results Varying distribution of Bax, Bcl-2, caspase-3, and caspase-9 was observed in all groups. Although the cytoplasmic distributions of the said apoptosis-related regulatory proteins were found in the glomeruli of the kidney sections and the cells forming the structures of the tubular system, some differences were found between the groups (Figure 3). As a result of our examination, it was determined that the immunopositivity density of Bax, caspase-3 and caspase-9 was quite low in the control group. It was determined that the expression level of all three pro-apoptotic proteins was similar to the NS group ($P \leq 0.05$), but both the control and NS groups were significantly different ($P \leq 0.05$) in terms of these proteins. It was observed that the Bcl-2 immunopositivity density was the highest in the NS group, and this group showed similar positivity with the control group in terms of percentile ($P \leq 0.05$). The Bcl-2 concentration was found to be the lowest in the DM group, while the Bcl-2 level of this group was found to be similar only to the vitamin E and metformin groups ($P \leq 0.05$). It was observed that Bax and caspase-3 levels were highest in the DM group. There was a positive difference in both Bax and caspase-3 immunopositivity intensity between the control, NS, vitamin E, and metformin groups ($P \leq 0.01$). It was determined that the caspase-9 level was highest in the DM group, and its expression level in this group was similar to the vitamin E group ($P \leq 0.05$). However, it was found that it was significantly different from the metformin group ($P \leq 0.01$), and caspase-9 levels were similar ($P \leq 0.05$) in the vitamin E and metformin groups (Table 1, Figure 4).
**Figure 1:** *Graphical demonstration of blood urea of animals in groups. Symbols between groups indicate statistical significance *$P \leq 0.01$* **Figure 2:** *Kidney section of the groups. Macula densa (m), glomerulus (G), proximal tubule (P), distal tubule (D), tubule basement membrane (→), brush border in proximal tubules (curved arrow), necrosis in proximal convoluted tubules (*), vacuolated cytoplasm (), pycnotic nuclei in tubule epithelial cells (►), atrophic tubule (↔), atrophic glomerular (#), loss of brush border (curvy arrow), corrugation in basement membranes (), pycnosis in tubule epithelial cell nuclei (►), congestion in glomerular capillaries, perforation of tubules (><), degenerative changes (), desquamated (q) epithelial cells in the tubule lumen* **Figure 3:** *Representative micrographs of Bax, Bcl-2, caspase 3, and caspase 9 in the control, DM, vitamin E, metformin, and *Nigella sativa* groups. Increased immuodensity of caspase 3 (arrow) and caspase 9 (curved arrow) in the irregular glomerulus of diabetic animals* **Figure 4:** *Graphical demonstration of statistical analyses. Different superscript texts on each column indicate significant differences between groups. The existence of similar characters indicates similarity. a-bP<0.05, a-cP<0.01, b-cP<0.01, d-eP<0.05, d-fP<0.01,* TABLE_PLACEHOLDER:Table 1
## Discussion
DN is clinically characterized by albuminuria followed by a decrease in glomerular filtration rate. [ 23]. In the experimental DM studies, it was reported that severe pathological changes occur in diabetic kidney tissue at the microscopic level [24]. In a previous study, it was reported that vitamin E reduces oxidative stress due to its anti-oxidant property and has positive effects on renal function parameters, thus providing a protective effect in DN [25]. Metformin is an antihyperglycemic agent frequently used in the treatment of DM [26]. Metformin has also been reported to inhibit the apoptosis mechanism associated with ROS production in diabetic complications [27]. It has been highlighted that NS reduces oxidative stress due to its anti-oxidant effects, provides a protective activity on renal function parameters, and reduces apoptosis in kidney tissue [28]. When compared with previous studies, our results are consistent, and all of the administered compounds successfully alleviated diabetes-related kidney alterations.
In another experimental study, it was reported that the blood urea levels increased in diabetic groups compared to the control group, and this was associated with renal failure [25]. On the other hand, Maheshwari et al. reported that serum urea levels could be controlled with the administration of metformin, regulating oxidative stress and the inflammatory cytokine release process in diabetic conditions [29]. In another experimental diabetes study, the ethanolic extract of NS successfully reduced serum urea levels, hyperglycemia, and oxidative stress [24]. Our results are consistent with previously published studies. During DN, tubular lining cells, renal vascular endothelial cells, and glomerular cells may suffer apoptotic cell death [30]. This process is believed to be activated through the accumulation of an excessive amount of ROS that suppresses anti-apoptotic proteins by triggering the expression of pro-apoptotic proteins. In a previously published study, Sha et al. reported that renal cellular apoptosis is upregulated in hyperglycemic conditions due to excessive ROS accumulation, but treatment can be reached through the administration of anti-oxidant chemical substances [31]. In another study, the authors reported that vitamin E has anti-apoptotic activity in experimental DM due to its anti-oxidant properties and may provide a protective effect on renal injury in DM [32]. Moreover, metformin has been reported to have protective activity on various organs due to bearing not only hyperglycemic control activity but also anti-oxidant properties [33, 34]. When we review the literature based on NS and DM, it is possible to comprehend that herbal treatment with NS could have promising results due to the anti-hyperglycemic and anti-oxidant effects of this plant extract [28]. Until today, numerous studies are performed to explore the anti-hyperglycemic and anti-diabetic strenght of NS. In one of these, the anti-diabetic potential of NS was linked with its effective activity on increased translocation of glucose transporter type 4 (GLUT4) in skeletal muscle, storage of free glucose from the blood, and a direct enhancement of insulin sensitivity [35]. Another study stated that NS decreased intestinal glucose absorption by inhibiting sodium-glucose linked transporter 1 (SGLT1) [36]. Although NS was reported with its multifunctional activity on diabetes-related complications, some studies indicated that excessive anti-oxidants such as thymoquinone, carvacrol, t-anethole, 4-terpineo in NS also support treatment in DM [37]. Although vitamin E does not have an insulin sensitivity-enhancing feature, research results are indicating that it can be a protective agent against the formation of DM-related nephropathy due to its intense anti-oxidant property [25, 38]. Metformin inhibits glucose production by the liver by activating the AMPK signaling pathway, thereby improving insulin sensitivity and glucose uptake by striated muscles. In addition, metformin has positive effects on oxidative stress parameters in the kidney tissue of experimental diabetic rats. In a previous study, it was reported that both metformin and NS could successfully regulate oxidative stress, and our results are consistent with this study as well [17].
## Conclusion
In our current study, in which we comparatively evaluated the anti-apoptotic and nephroprotective potentials of vitamin E, metformin, and NS, we reached findings consistent with the literature regarding that all three agents are protective at varying levels. However, as a result of our comparative analysis, we obtained strong data that the most effective nephroprotective and anti-apoptotic properties can be obtained with NS. Our findings were obtained as a result of experimental research, and we think that more research is needed to investigate the clinical usability and anti-hyperglycemic, anti-diabetic, anti-apoptotic, and nephroprotective properties of NS and to examine the molecular signal communications underlying these protective properties.
## Authors’ Contributions
HA, SK, UŞ, and YN designed the experiments; HA and SK performed experiments and collected data; HA, SK, UŞ, and YN discussed the results and strategy; YN Supervised, directed, and managed the study; HA, SK, UŞ, and Misexpresse YU Final approved of the version to be published.
## Ethical Consent
Our study was approved by dicle University Animal Experiments Ethics Committee with protocol number 19-04 and decision number 35582840-604.01.01-.
## Conflicts of Interest
The authors declare no conflict of interest.
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|
---
title: Chlorogenic acid attenuates liver apoptosis and inflammation in endoplasmic
reticulum stress-induced mice
authors:
- Azam Moslehi
- Tahereh Komeili-Movahhed
- Mostafa Ahmadian
- Mahdieh Ghoddoosi
- Fatemeh Heidari
journal: Iranian Journal of Basic Medical Sciences
year: 2023
pmcid: PMC10008392
doi: 10.22038/IJBMS.2023.66827.14659
license: CC BY 3.0
---
# Chlorogenic acid attenuates liver apoptosis and inflammation in endoplasmic reticulum stress-induced mice
## Abstract
### Objective(s):
The accumulation of unfolded or misfolded proteins in the endoplasmic reticulum (ER)results in a state known as “ER stress”. It can affect the fate of proteins and play a crucial role in the pathogenesis of several diseases. In this study, we investigated the protective effect of chlorogenic acid (CA) on the inflammation and apoptosis of tunicamycin-induced ER stress in mice.
### Materials and Methods:
We categorized mice into six groups: Saline, Vehicle, CA, TM, CA 20-TM, and CA 50-TM. The mice received CA (20 or 50 mg/kg) before intraperitoneal tunicamycin injection. After 72 hr of treatment, serum biochemical analysis, histopathological alterations, protein and/or mRNA levels of steatosis, and inflammatory and apoptotic markers were investigated by ELISA and/or RT-PCR.
### Results:
We found that 20 mg/kg CA decreased mRNA levels of Grp78, Ire-1, and Perk. Moreover, CA supplementation prevented TM-induced liver injury through changes in lipid accumulation and lipogenesis markers of steatosis (Srebp-1c, Ppar-α, and Fas), and exerted an inhibitory effect on inflammatory (NF-κB, Tnf-α, and Il-6) and apoptotic markers (caspase 3, p53, Bax, and Bcl2), of liver tissue in ER stress mice.
### Conclusion:
These data suggest that CA ameliorates hepatic apoptosis and inflammation by reducing NF-κB and Caspase 3 as related key factors between inflammation and apoptosis.
## Introduction
The endoplasmic reticulum (ER) is a multifunctional organelle in which the folding of newly synthesized secretory and membrane proteins, lipid biosynthesis, and calcium storage occur [1].
Misfolding of some proteins occurs during biosynthesis in the ER lumen. The accumulation of unfolded or misfolded proteins in the ER results in a state known as “ER stress”. It can affect the fate of proteins, lipids, and carbohydrates, lead to inflammatory signaling in the stressed cells, cause cellular apoptosis, and promote disease processes [2]. Cross-talk between ER stress and inflammation has been demonstrated in many pathological conditions. Increasing evidence has demonstrated that ER stress is associated with chronic diseases, including diabetes and obesity, multiple forms of respiratory inflammation, neuromuscular and neurodegenerative inflammatory diseases, arthritis, autoimmune disease, inflammatory bowel diseases, cancer, ischemia, and liver fat accumulation disease (hepatic steatosis) [3-6].
Some prevalent factors such as oxidative stress, viral infections, dietary demands, and pharmacologic stimuli [7] induce ER stress by altering the redox state, calcium levels, or failure to modify secretory proteins post-translationally. Pharmacologically, toxins such as tunicamycin (TM) and thapsigargin inhibit protein glycosylation and disrupt ER Ca2+ levels leading to ER stress [8]. Since sustained or massive ER stress leads to apoptosis, if the stress cannot be resolved, it will be lethal to cells, and signaling switches to a pro-apoptotic response by influencing unfolded protein response mediated signals [9].
As mentioned above, inflammation plays an important role in creating ER stress. On the other hand, the evidence from available literature shows that a variety of plants and their derived bioactive combinations have distinctive properties that permit them to act as potent anti-inflammatory compounds [10], such as chlorogenic acid (CA) [11].
CA (5-O-caffeoylquinic acid), a polyphenolic compound widely distributed in foods and herbs, is one of the main polyphenols in the human diet, and it takes many health-promoting properties [12]. Foods and herbs such as tomatoes [13], potatoes, pears, tobacco leaves, apples, eggplants, coffee beans, honeysuckle [13, 14], artichoke [15], grapes [16], plums [13, 17], kiwi fruit [18], and tea [16, 19] contain a significant amount of CA. Recent studies demonstrated that CA has anti-inflammatory [11], anti-oxidant [11], anti-diabetic [20], anti-cancer [21], anti-neurodegenerative [22], anti-lipidemic [20] and anti-hypertensive activities [23].
According to the beneficial effects mentioned above, we evaluated the effects of CA administration on liver steatosis, inflammation, and apoptosis in TM-induced ER stress.
## Materials and Methods
Chemicals and reagents CA (C3878, purity ≥ $95\%$) was purchased from Sigma-Aldrich (St. Louis, MO, USA). TM was purchased from Cayman Chemical (Ann Arbor, MI, USA). TM was prepared in dimethyl sulfoxide (DMSO; Calbiochem, EMD Bioscience Inc. La Jolla, CA, USA).
Animals In the present study, 36 male C57/BL6 mice weighing 22-25 g were purchased from the Pasteur Institute (Tehran, Iran). The study commenced after obtaining the approval of the experimental animal ethics committee (Ethics code: IR.MUQ.REC.1400.056). Mice were provided with fresh drinking water daily and were kept at 21 ˚C with 12 hr light (08:00–20:00 hr) and 12 hr darkness [24] with free access to standard laboratory chow (Pars animal feed Co, Tehran, Iran).
Experimental design Before each analysis, animals were randomly divided into 6 equal groups ($$n = 6$$), including Saline (0.2 mL sterile normal saline injection), Vehicle (0.2 mL DMSO injection), CA (50 mg/Kg CA injection) [25, 26], TM (a single dose of 2 µg/g TM injection to induce ER stress) [27], CA 20-TM (a single dose of 20 mg/kg CA 60 minutes before TM administration) [28], CA 50-TM (a single dose of 50 mg/kg CA 60 minutes before TM administration). Intraperitoneal administration was used for all groups.
Thirty hours post-TM injection [29], the animals were anesthetized with ketamine (100 mg/kg) and xylazine (10 mg/kg). The blood samples were taken from the heart for liver biochemical assays. Then, the abdomen was excised via a midline incision, and the liver was removed. Apart from the median lobe, the liver was dissected and half of it was fixed in $10\%$ formalin for histopathology assessment and the other half was stored in a freezer (-80˚C) for molecular assessment.
Evaluation of total cholesterol (TC), triglyceride (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) Blood was directly collected from the heart by a 2 mL syringe insertion. Serum was obtained from the centrifuged blood samples (3500 rpm for 20 min). Then, TC, TG, ALT, and AST levels were determined according to the protocol provided by the colorimetric Kit (Pars Azmun, Iran) using a spectrophotometer (UNICO Instruments C., Model 1200, USA) [24]. ALP level was determined in liver tissue samples according to the ALP colorimetric activity assay Kit (Cayman chemical, No. 701710, USA).
Tissue preparation and histopathology examination For the histological examination, the liver tissues were fixed in $10\%$ formalin, dehydrated in ethanol series, cleaned in xylene series, embedded in paraffin wax, cut into 5 µm sections, mounted on glass slides, and stained with hematoxylin and eosin dye (H&E stain) according to Bancroft and Layton [30]. Photomicrographs and histology examinations were taken using a light microscope (Leica DM750, Leica Microsystems, India). In all samples, the histological findings were scored based on the Kleiner et al. scoring system as follows: steatosis (0–3), lobular inflammation (0–3), and hepatocellular ballooning (0–2)[31]. Then the histological findings as well as serum biomarker levels were interpreted by an expert pathologist blind to the experiment. The inflammatory cell infiltration, hepatocyte ballooning, and steatosis were evaluated using image analysis software (Image J, National Institute of Health, Bethesda, MD).
Real-time reverse transcription polymerase chain reaction (Real-time RT-PCR) The total RNA of frozen tissue samples was isolated using the Trizole (Yekta Tajhiz, Iran) according to the manufacturer’s instructions. The quantity and purity of the RNA samples were measured by a Nanodrop spectrophotometer. Complementary DNAs (cDNA) were prepared from mRNA templates for RT-PCR using the RocketScript™ RT PreMix (BioNeer, Alameda, CA, USA). Real-time PCR analysis was performed with AccuPower® 2X GreenStar™ qPCR Master Mix (Biofact, Korea) using glyceraldehyde-3-phosphate dehydrogenase (Gapdh) as an internal control [3]. The quantitation of data was performed using the comparative CT (∆∆CT) method (Table 1).
Enzyme-linked immunosorbent assay (ELISA) of inflammatory cytokines in the liver tissue The presence of Caspase 3 and NF-κB in the tissue supernatant was assayed with a mouse standard ELISA kit. Briefly, 100 mg of the liver tissue was weighed, homogenized, and added to 1 ml phosphate buffer. It was then centrifuged (3000-4000 rpm for 20 min), and the supernatant was collected, aliquoted, and kept at -80 oC. For NF-κB, 40 µl sample, 10 µl NF-κB- Ab, 50 µl of each standard, and 50 µl streptavidin-HRP were added and incubated at 37 oC for 60 min. For Caspase 3, 40 µl samples, 10 µl caspase 3-Ab, 50 µl standards and 50 µl streptavidin-HRP were added and incubated at 37 oC for 60 min. The following steps were similar for both assays. The plates were then washed five times with 300 µl wash buffer, then 100 µl chromogen was added and incubated for 10-20 min. Afterward, the stop solution was added and read at 450 nm within 10 min. The results were calculated based on the absorbance levels of complex cytokine-antibodies, and the units of cytokines were described as pg/ml [32].
Statistics analysis *All data* are presented as mean ± SEM. Statistical analysis was performed using one-way analysis of variance (ANOVA) and Tukey’s post hoc test for multiple comparisons using the statistical software package SPSS Version 18.0 for Windows. In all analyses, the significance level was accepted as 0.05.
## Results
CA decreased TM-induced ER stress in mice *In this* study, a single dose of TM resulted in ER stress and increased *Grp78* gene expression in the TM group compared to the saline group; but *Grp78* gene expression significantly decreased in the CA 20- TM and CA 50- TM groups compared to the TM group ($P \leq 0.05$) (Figure 1A).
Our finding showed that the TM injection increased the expression of Ire-1 and Perk in the TM group compared to the saline group ($P \leq 0.05$) (Figure 1B, 1C). The pretreatment with 20 mg/kg CA significantly decreased gene expression of Ire-1 and Perk, while receive of 50 mg/kg CA increased gene expression compared to the TM group ($P \leq 0.05$) (Figures 1B and 1C).
CA ameliorated TC, TG, ALT, AST, and ALP levels on TM-induced ER stress in mice Blood sample analysis revealed that serum TC and TG levels in the TM group were significantly decreased compared to the saline group. Treatment with CA (both 20 and 50 mg/kg) did not increase serum TC and TG levels ($P \leq 0.05$) (Table 2). The ALT and AST levels in serum and ALP levels in the liver tissue were measured to explore damage to the liver following TM administration. As depicted in Table 2, compared to the saline group, the TM group showed a clear increase in the levels of ALT, AST, and ALP ($P \leq 0.05$). Mice treated with CA (20 mg/kg) experienced a significant decrease in the level of AST compared to the TM group ($P \leq 0.05$), but there were no significant differences in the ALT and ALP levels with the saline group ($P \leq 0.05$). The ALT and ALP levels decreased significantly in the high concentrations of CA (50 mg/kg); however, the AST level increased significantly compared to the saline group ($P \leq 0.05$). Interestingly both groups treated with different concentrations of CA showed lowered ALT, AST, and ALP levels compared to the TM group ($P \leq 0.05$).
CA reduces steatosis and fat accumulation in the liver tissue *In this* experiment, H&E staining was performed to evaluate histopathological alterations in the liver tissue. As noted in Figure 2, no sign of steatosis or inflammation was seen in the Saline group; however, severe steatosis (excessive lipid droplet accumulation with empty spaces), severe cellular ballooning, and lobular inflammation were observed in the TM group compared to the Saline group. Nevertheless, administering 50 mg/kg CA did not decrease steatosis compared to the Saline group (Figure 2). However, in the CA 20-TM group, a clear decrease was seen in steatosis, inflammation, and cellular ballooning compared to the TM group ($P \leq 0.05$) (Figure 2). In fact, the administration of CA (20 mg/kg) improved liver steatosis and inflammation in the ER-stress-induced mice.
CA pretreatment alleviates steatosis in liver tissue The expression of the Srebp-1c gene in the TM group increased compared to the Saline group ($P \leq 0.05$) (Figure 3A). This suggests that the administration of the TM caused ER stress conditions in mice. Additionally, our results showed that the level of the Srebp-1c transcription factor gene in groups treated with CA (20 or 50 mg/kg) was significantly downregulated compared to the TM group ($P \leq 0.05$).
The level of Ppar-α gene expression in the TM group decreased significantly compared to the saline group ($P \leq 0.05$). While the administration of CA (20 or 50 mg/kg) increased the level of the Ppar-α gene compared to the TM group ($P \leq 0.05$) (Figure 3B).
Mice treated with the TM experienced a significant increase in the levels of Fas compared to the saline group, but it significantly decreased in CA (20 and 50 mg/kg) treated mice compared to the TM group ($P \leq 0.05$) (Figure 3C).
CA pretreatment changes pro-inflammatory cytokines *The* gene expression of NF-κB and two pro-inflammatory cytokines, Tnf-α and Il-6, in the TM group were markedly increased compared to the Saline group ($P \leq 0.05$) (Figure 4A-C). However, pretreatment with CA lowered the level of inflammatory cytokines compared to the TM group ($P \leq 0.05$). In addition, the treatment with different concentrations of CA has various outcomes in Tnf-α, and Il-6 levels as CA (20 mg/kg) significantly downregulated the expression of these pro-inflammatory cytokines compared to the high concentrations of CA ($P \leq 0.05$).
CA pretreatment affects the apoptosis in liver tissue Real-time RT-PCR results depicted that TM markedly increased hepatocyte mRNA expression of p53 compared to the Saline group, while this marker was significantly reduced in CA (20 or 50 mg/kg) compared to the TM group ($P \leq 0.05$) (Figure 5A). *Bax* gene expression was also significantly upregulated in the TM-induced mice compared to the saline group ($P \leq 0.05$), while the CA administration markedly downregulated it compared to the TM group (Figure 5B).
*Bcl2* gene expression slightly increased in the TM-induced mice compared to the Saline group, and the CA administration markedly raised it compared to the TM group ($P \leq 0.05$) (Figure 5C). Moreover, the expression of apoptotic indexes (Bax and Bcl2) in the CA 50-TM group increased significantly compared to the Saline group ($P \leq 0.05$). The ratio of Bax/Bcl-2 mRNA expression significantly increased in the TM group in comparison with the Saline group and strikingly decreased in the CA pretreated groups. The ELISA results also showed a marked increase of Caspase 3 level in the TM group compared to the Saline group ($P \leq 0.05$). Nevertheless, CA pretreatment significantly decreased the Caspase 3 level in the CA 20-TM group compared to the TM group ($P \leq 0.05$) (Figure 5D).
## Discussion
This study showed that CA could reduce liver steatosis and inflammation, plasma ALT and AST, and liver ALP levels. It also decreased gene expression of apoptosis pathways such as p53, Caspase 3, Bax, and Bcl-2 in TM-challenged mice. Additionally, CA attenuated pro-inflammatory cytokines, including Tnf-α and Il-6, and inhibited the nuclear translocation of NF-κB. It also lowered the expression of Srebp-1c and *Fas* genes and increased Ppar-α gene expression in the TM-induced mice. To the best of our knowledge, this is the first study evaluating the effects of CA on hepatic ER stress-induced steatosis, inflammation, and apoptosis in an animal model.
Recent studies have shown that TM, a common pharmacological ER stressor, can induce ER stress in the hepatocytes and lead to hepatic steatosis [33-35]. In this study, we first evaluated the expression of representative ER stress markers. As expected, the mRNA expressions of Grp78, Ire-1, and Perk significantly increased in the TM group. The present study showed that the administration of 20 mg/kg CA provided a marked decrease in the expression of genes related to ER stress. Wang et al. [ 36] have stated that CA decreased Grp78, Perk, and Ire-1 expressions and improved pulmonary fibrosis after bleomycin administration. CA also downregulated ER stress markers in the palmitic acid-induced hepatocytes [37]. These reports are consistent with our results, and it seems that CA administration could decrease Grp78, Ire-1, and Perk and thereby ameliorates hepatic ER stress. Surprisingly, in our study, 50 mg CA increased ER stress indexes and upregulated Grp78, Perk, and Ire-1 expressions. In this regard, another study showed that intravenous injection of a high dose of CA (49mg/kg/day) increased the number of adherent leukocytes, generation of peroxides in the venular walls, and induced albumin leakage from mesentery venules in the small intestine. Upregulation of inflammatory cytokines and inflammation was also observed in this dose of CA [38]. Here, it seems that 50 mg CA increased ER stress and was toxic to liver tissue.
Our histological results showed severe steatosis (excessive lipid droplet accumulation in hepatocytes), cellular ballooning, and lobular inflammation after thirty hours of TM challenge, based on many previously published [34, 39] and 20 mg/kg CA supplementation prevented TM-induced lipid accumulation, cellular ballooning, lobular inflammation. However, administration of a high dose of CA (50 mg/kg) cannot improve liver steatosis and inflammation in ER-stress-induced mice. The histological results were compatible with those obtained from the expression of genes related to ER stress and confirmed the positive effect of CA in preventing ER stress-induced liver injuries.
In accordance with our histopathological investigation, Shi et al. suggested that CA could alleviate the cadmium-exposed chicken livers. They showed that the liver histopathology and ultrastructure of hepatocytes were improved after the poisoned chickens were treated with α-lipoic or CA [40].
It is notable that the hepatic function and histology were significantly improved after the suppression of oxidative stress by CA treatment in ischemia/reperfusion injury in rat liver, as designated by hepatic structure improvement [41].
This study showed that TM administration induced TG accumulation in the hepatocytes and led to steatosis by upregulating the Srebp-1c and *Fas* gene expression and downregulating the *Ppar* gene expression in the mice. However, CA alleviates steatosis in liver tissue by downregulating the expression of Srebp-1c and *Fas* genes and upregulating the *Ppar* gene expression in TM-induced ER stress. In accordance with our results, Li and his colleagues showed that in bovine hepatocytes, SREBP-1C overexpression could induce TG accumulation by increasing lipid synthesis and decreasing lipid oxidation. Moreover, SREBP-1c overexpression upregulated the expression of other genes involved in TG synthesis, including FAS [54].
The anti-obesity effect of Nostoc commune ethanol extract could downregulate the mRNA expression of adipogenesis, including PPAR-γ and SREBP-1c and lipid lysis-related genes in epididymal adipose tissue [55]. It seems that CA administration could alleviate liver steatosis through Srebp-1c and Fas downregulation, and Ppar-a upregulation. It seems that CA administration through stress attenuation downregulated Srebp-1c, and following that, the Fas enzyme also upregulated Ppar-α, and afterward, liver steatosis was alleviated.
In this study, NF-κB protein levels, together with TNF-α and IL-6, increased in mice with ER stress. Previous studies have shown that NF-κB protein levels and the expression of inflammatory genes were upregulated in ER stress model [42, 43]. Aslan et al. [ 35] established that serum levels of ALT, AST, and ALP markedly rose in TM-induced ER stress in male rats. Compatible with previous reports, 20 mg/kg CA effectively declined NF-κB levels and IL-6 and TNF-α expression and attenuated TM-induced hepatic inflammation.
In several documents, it has been shown that some concentrations of CA can attenuate inflammation [28, 44] while some other concentrations can provide stimulatory effects on proinflammatory cytokines such as interleukins and TNF-α [41, 45]. Our results showed that 50 mg/kg CA increased NF-ĸB, Il-6, and Tnf-a. Anqi et al. [ 46] have reported that 40 mg/kg CA increased IkB-α and induced apoptosis in breast cancer tumors. Herein, it seems that a high dose of CA could not decrease inflammatory markers due to the inability of ER stress attenuation.
It has been reported that ER stress induces apoptosis in many diseases [47, 48]. In this study, CA decreased gene expression of tumor suppressor p53, Caspase 3, and pro-apoptotic Bax and increased anti-apoptotic Bcl-2 in the liver tissue of TM-challenged mice. A study demonstrated that CA decreased caspase 3, 9, and 12 in RLE-6TN cells and pulmonary tissue of mice with ER stress [49]. Another study proved that CA induced cell apoptosis in ER stress provoked by palmitic acid [37]. Moreover, the administration of CA had anti-apoptotic and antifibrogenic effects showing that it can be used as a beneficial treatment for various liver diseases [50]. Although the present study demonstrated that the use of 20 mg/kg CA inhibits apoptosis, a higher concentration of CA has a contrary effect and induces the expression of apoptosis-related genes; So, the effect of CA on the expression of genes involved in the apoptosis mechanism is probably dose-dependent [50]. In our previous study, a high concentration of CA upregulated the expression of apoptotic genes such as p53, Bax, and caspase‐3 in mice with breast cancer tumors [51]. Therefore, CA in low concentrations possibly shows a protective effect against TM-induced ER stress in the liver tissue, and in high concentrations, it could be useful for cancer treatment by induction of apoptosis. In recent years, several studies have revealed that CA plays an important role in tumor prevention. CA can decrease the proliferation of A549 human lung cancer cells [52] and suppress glioma growth by repolarizing the phenotype of macrophages [53]. It also induces apoptosis in MCF-7 and MDA-MB-231 breast cancer cell lines in a dose-dependent manner and disrupts the cell cycle [54].
## Conclusion
Our study demonstrated that TM treatment resulted in liver ER stress and low concentration of CA can improve steatosis, and hepatic inflammation and plays an important role in inhibiting ER stress symptoms. Also, 20 mg/kg of CA decreased lipid metabolism-related transcriptional factors, enzyme expression, and apoptosis agents in ER stress-induced mice. However, high concentrations of CA had negative effects on steatosis, inflammation, and apoptosis. Therefore, choosing a suitable concentration of CA is critical for the treatment of liver injuries.
## Authors’ Contributions
AM designed the experiments; TKM performed experiments and collected data; MA discussed the results and strategy; FH Supervised, directed, and managed the study; AM, TKM, MA, and MG approved the final version to be published.
## Conflicts of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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|
---
title: Exosomes from adipose-derived stem cells promote angiogenesis and reduce necrotic
grade in hindlimb ischemia mouse models
authors:
- Trinh Hoang-Nhat Nguyen
- Phuc Van Pham
- Ngoc Bich Vu
journal: Iranian Journal of Basic Medical Sciences
year: 2023
pmcid: PMC10008393
doi: 10.22038/IJBMS.2023.67936.14857
license: CC BY 3.0
---
# Exosomes from adipose-derived stem cells promote angiogenesis and reduce necrotic grade in hindlimb ischemia mouse models
## Abstract
### Objective(s):
Acute hindlimb ischemia is a peripheral arterial disease that severely affects the patient’s health. Injection of stem cells-derived exosomes that promote angiogenesis is a promising therapeutic strategy to increase perfusion and repair ischemic tissues. This study aimed to evaluate the efficacy of adipose stem cell-derived exosomes injection (ADSC-Exos) in treating acute mouse hindlimb ischemia.
### Materials and Methods:
ADSC-Exos were collected via ultracentrifugation. Exosome-specific markers were analyzed via flow cytometry. The morphology of exosomes was detected by TEM. A dose of 100 ug exosomes/100 ul PBS was locally injected into acute mice ischemic hindlimb. The treatment efficacy was evaluated based on the oxygen saturation level, limb function, new blood vessel formation, muscle structure recovery, and limb necrosis grade.
### Results:
ADSC-exosomes expressed high positivity for markers CD9 ($76.0\%$), CD63 ($91.2\%$), and CD81 ($99.6\%$), and have a cup shape. After being injected into the muscle, in the treatment group, many small and short blood vessels formed around the first ligation and grew down toward the second ligation. The SpO2 level, reperfusion, and recovery of the limb function are more positively improved in the treatment group. On day 28, the muscle’s histological structure in the treatment group is similar to normal tissue. Approximately $33.33\%$ of the mice had grade I and II lesions and there were no grade III and IV observed in the treatment group. Meanwhile, in the placebo group, $60\%$ had grade I to IV lesions.
### Conclusion:
ADSC-Exos showed the ability to stimulate angiogenesis and significantly reduce the rate of limb necrosis.
## Introduction
Acute limb ischemia (ALI) is defined as a condition in which limb perfusion is suddenly decreased, causing a potential threat to the surviviAdipose-derivedects 1.5 patients per 10,000 people each year. ALI is common in the lower extremities in 9 to 16 new patients and the upper extremities in 1 to 3 new patients per 100,000 individuals per year [1, 2]. This disease can be caused by a variety of conditions, such as trauma ($5\%$), arterial embolism ($30\%$), and thrombosis-related conditions ($65\%$) [3]. Its main clinical manifestation occurs within 2 weeks after the beginning of symptoms [4]. The symptoms of ALI develop within several minutes to hr or days. They range from new or intermittent claudication to pain at rest, paranesthesia, muscle weakness, and paralysis. In severe cases, limb gangrene may lead to limb amputation or death [1]. Moreover, patients with ALI are at high risk for complications, including pulmonary problems, renal function worsening, myocardial infarction, and heart failure [5]. Within 30 days after presentation, 15-25 % of patients with ALI die from associated diseases [4].
Revascularization to restore blood flow is the primary aim of the current treatment options [1]. The majority of patients with ALI require endovascular intervention (cryoplasty or implantation of stent grafts or drug-eluting balloons or stents), bypass surgery, or plasminogen activator medication [1, 4]. Despite all medical advances, 20-$45\%$ of patients are not eligible for surgery or experience treatment failure. This subgroup of patients can experience serious complications, such as amputation and even death [6, 7]. Therefore, novel treatment methods to improve limb perfusion among patients with ALI, especially those considered to have “no treatment option,” are imperative. Targeting microvascular regeneration using therapeutic stem cells is a potential strategy for patients with ALI [8].
In recent years, many studies have applied different stem cell sources, such as bone marrow-derived stem cells, adipose-derived stem cells (ADSCs) [9], menstrual blood cells [10], and placenta-derived mesenchymal stem cells (MSCs), in the treatment of ALI [11]. Stem cell therapy has shown beneficial results in the treatment of this disease. As the scientific basis underlying the effectiveness of stem cell therapy, extracellular secretion from stem cells is evaluated as the main mechanism for regenerating ischemic injury. ADSCs have been proven to be superior in the secretion of bioactive factors related to angiogenesis and tissue regeneration [12].
Accumulating evidence supports that extracellular vesicles (EVs), especially exosomes, mediate the regenerative capacity of MSCs [13, 14]. EVs are nanosized cell-derived vesicles that exist in most body fluids, such as blood and urine, and cell culture media. Exosomes, microvesicles, and apoptotic bodies are the three primary subtypes of EVs [15]. Exosomes are 30–100 nm in diameter and derived from the budding of multivesicular bodies [16]. Exosomes derived from MSCs contain a variety of cargo categories, including bioactive lipids, functional proteins, and RNAs [17, 18]. Moreover, EVs and exosomes have the capacity to transfer bioactive factors and regulate tissue regeneration by reprogramming recipient cells [19, 20].
Exosomes can promote angiogenesis by transporting proangiogenic miRs from MSCs, such as miR-125a [21], miR-126 [22], and miR-30b [20], to endothelial cells and by repressing DLL4. In a previous study, miR532-5p, miR148a, let-7f, and miR378 were enriched in MSC-EVs and regulated many biological processes, including angiogenesis, apoptosis, proteolysis, and transcription, in recipient cells [23]. In vitro, endothelial cells treated with MSC-exosomes increased the expression of molecules involved in angiogenesis, such as angiogenin, HIF-1a, VEGFA, VEGFR-2, ANG-1, PDGFA, PGF, bFGF, TGFB1, bFGFR, and IL-8 [24, 25]. Exosomes derived from MSCs were used to treat many diseases, while those derived from EPCs, iMSCs, and placenta-derived MSCs enhanced the density of microvessels and blood perfusion in mouse models of ischemia [19, 25, 26]. Umbilical cord MSC-exosomes promoted angiogenesis in a model of cutaneous burns by activating Wnt/β-catenin [27]. Generally, MSC-exosomes contain cytokines and growth factors related to muscle repairs, such as VEGF, IL-6, FGF-2, GCSF, and PDGF-BB, which encourage muscle repair by promoting angiogenesis and myogenesis [24].
Therefore, the purpose of this research was to assess the treatment efficacy of exosomes from ADSCs (ADSC-Exos) in mouse models of acute hindlimb ischemia.
## Materials and Methods
Characteristics of the ADSCs Frozen human ADSCs were provided by SCI Biobank (Stem Cell Institute, HCMC, VN). Cryovials were thawed in a thermostatic bath at 37 °C. The cell cryopreservation medium was removed via centrifugation at 1,500 rpm for 5 min. The cells were cultured in MSCCult I medium (Regenmedlab, VN) at 37 °C and $5\%$ CO2. The medium was refreshed every 2-3 days depending on the proliferation of the cells until they reached the desired density.
Three characteristics of the designated MSCs, including the expression of MSC markers, ability to differentiate into functional cells, and the ability to adhere to plastic surfaces, were then evaluated. First, 10,000 cells were stained with antibodies against CD14 FITC, CD34 FITC, CD19 PerCD, CD45 APC, CD90 PE, CD73 PE, CD105 PerCD, and HLA-DR FITC (Miltenyi Biotec, Germany) for 15 min in the dark. The antibodies were washed two times using phosphate buffer solution (PBS) via centrifugation at 3,500 rpm for 5 min. The cells were resuspended in 300 µl of PBS and analyzed using the BD FACSCalibur system. Second, the ADSC candidates were cultured in StemPro® Adipogenesis Differentiation Media, StemPro® Osteogenesis Differentiation Media, and StemPro® Chondrogenesis Differentiation Media (Gibco/Thermo Fisher Scientific, MA, USA) to detect their ability to differentiate into adipocytes, osteoblasts, and chondroblasts, respectively. On day (D) 14, the cells were stained with Oil Red O (Sigma-Aldrich, MO) to observe the appearance of the adipocytes. On D21, the cells were stained with Alizarin Red (Sigma-Aldrich, MO) to determine the accumulation of Ca2+ and Mg2+ in the osteoblasts as well as with Alcian Blue (Sigma-Aldrich, MO) to determine the synthesis and storage of proteoglycans in the chondroblasts. Finally, the ADSC candidates were observed under a microscope to check the cell adherence on the plastic surface.
Collection and identification of the stem cells-derived exosomes The human ADSCs from pass 3 to pass 7 were cultured in MSCCult I medium to collect the supernatant. The supernatant was centrifuged at 300×g for 10 min to remove the remaining cells. Extracellular secretion of the human ADSCs was induced via ultracentrifugation at 4 °C [28]. Briefly, the supernatant was transferred to a new centrifuge tube and further centrifuged at 2,000 × g for 20 min to remove dead cells and large debris. Thereafter, the supernatant was transferred to a new ultracentrifuge tube and centrifuged at 10,000×g for 30 min to remove small debris and large bags. The collected fluid was then transferred to a new ultracentrifuge tube and ultracentrifuged at 100,000×g for 70 min. After this step, the supernatant was removed, leaving approximately 2 mm of the supernatant above the pellet. The pellet was redissolved in PBS and ultracentrifuged at 100,000 × g for 70 min. The pellet was kept in 100 μl of PBS. The ADSC-Exos candidates were then stored at -80 °C for further experiments.
The Bradford assay was used to evaluate the total protein concentration of the ADSC-Exos [28]. The BSA standards and ADSC-Exos were thawed at 4 °C. In a flat-bottomed 96-well plate, 10 µl of each PBS was loaded into a blank well. Thereafter, 10 µl of each BSA standard (Thermo Fisher Scientific, MA) was added to the wells. Five microliters of PBS were loaded in the well containing the ADSC-Exos sample, and 5 µl of the sample was then added to each well. Each solution was repeated in three wells, and 300 µl of Coomassie blue solution was then added to each well. The OD at 595 nm was read within 10 min.
The ADSC-Exos were identified based on the expression of specific markers, including CD63, CD9, and CD81, and via flow cytometry [28]. Briefly, 50 µg of the ADSC-Exos was incubated with 1 µl of latex aldehyde/sulfate beads for 15 min at room temperature (RT). PBS was then added to a final volume of 1 ml and incubated overnight at 4 °C. Thereafter, 110 µl of 1 M glycine was added to the abovementioned solution, mixed gently, and incubated for 30 min, followed by centrifugation at 4,000 rpm at RT for 3 min. In the next step, the bead pellet was resuspended in 1 ml PBS/$0.5\%$ BSA and centrifuged at 4,000 rpm for 3 min to discard the supernatant. The bead pellet was resuspended in 0.7 ml PBS/$0.5\%$ BSA and divided into 2 ml tubes. One microliter of antibody was added to the tube and incubated for 30 min at 4 °C in the dark. Next, the tubes were centrifuged at 4,000 rpm for 3 min to remove the supernatant. Finally, the bead pellet was suspended in 300 µl of PBS and analyzed on a BD FACSMelody cell sorter (BD BioSciences, NJ).
Mouse model of acute hindlimb ischemia All animal experiments were approved by the Institutional Animal Care and Use Committee, Stem Cell Institute (HCMC, VN). Mice over 6 months old were used to create the acute ischemic hindlimb model according to the protocols published by Vu et al. [ 29]. Briefly, the mice were anesthetized via intramuscular injection of 4 mg/kg xylazine (VIME-LAZIN, Vemedim, Vietnam) and 5 mg/kg Zoletil (Virbac, France). Hairy thighs were shaved and disinfected with a $10\%$ povidone-iodine solution (Pharmedic, Vietnam). An incision approximately 1.5 cm long was created in the skin. The femoral artery and vein near the abdomen were separated from the muscle and nerves and ligated at two locations-above and below the superficial caudal epigastric artery. Next, the two ligated blood vessels were cut using scissors. Finally, the skin was stitched, and the wound area was covered in a povidone-iodine solution.
ADSC-Exos injection in the acute hindlimb ischemic mice The acute hindlimb ischemic mice were divided into two groups. The placebo group was injected with 150 µl PBS, while the treatment group was injected with 100 µg ADSC-Exos suspended in 150 µl PBS. ADSC-Exos or PBS was injected directly into the muscle at the burn sites immediately after the models were established. All mice were followed up for 28 days after the injection.
Evaluation of limb recovery after ADSC-Exos injection The severity of the ischemic injury was evaluated based on the grade of limb necrosis according to the guidelines by Goto et al. [ 28]: grade 0, normal limb without necrosis; grade I, black toenails with necrosis limited to the toes; grade II, necrosis extending to the foot; grade III, necrosis extending to the knee; and grade IV, necrosis extending to the hip or loss of the entire hindlimb.
The improvement in perfusion was assessed using the trypan blue flow assay. One hundred microliters of $0.4\%$ trypan blue solution were injected into the tail vein of the mice. The time to staining of the toes and footpad was recorded on D3, D7, D14, and D28 and compared with that in normal mice.
Blood flow up to the limb was indirectly evaluated based on the blood oxygen level. The peripheral oxygen saturation (SpO2) level was monitored using Contec08A+ SpO2 Probe on D3, D7, D14, D21, and D28 and compared with that in normal mice.
Limb function recovery was assessed by observing the movements of the mouse limbs while moving on a plane and by counting the pedal frequency of the hindlimbs over a 10-sec period.
The changes in tissue structure were analyzed through histological experiments on D3 and D28 after injection. The mice were euthanized, and the hindlimb muscle between the two ligated blood vessels was biopsied and fixed in a $4\%$ paraformaldehyde solution overnight. Thereafter, the tissue samples were cut into 3-µm sections and stained with hematoxylin and eosin (H&E) at Cho Ray Hospital (Ho Chi Minh City, Vietnam). All slides were observed using an inverted microscope (Carl Zeiss, Germany).
Neovascularization was assessed on D7 and D28 after surgery. The mice were euthanized, and the formation of new blood vessels was observed via stereoscopic microscopy (Carl Zeiss).
Statistical analysis Data were presented as means±standard deviations. All statistical analyses were performed using GraphPad Prism 8.0. Differences were considered significant at $P \leq 0.05.$
## Results
Characterization of the ADSCs After thawing, the human ADSCs were reconfirmed to be MSCs. The ADSCs were able to adhere and spread on the culture vessel surface and exhibited classic fibroblast-like cell morphology (Figure 1A). After 21 days of culture in the osteogenic induction medium, extracellular matrix Ca2+ accumulation was observed (Figure 1B). The formation of lipid droplets in the cells was observed after 7-14 days of culture in the adipogenic induction medium. The lipid droplets were confirmed based on positive staining with Oil Red O (Figure 1C). After chondrogenic induction, the cells aggregated and increased the production of the extracellular matrix. The induced cells were positive for Alcian Blue staining 21 days after induction (Figure 1D). These results showed that the ADSC candidates were able to differentiate into functional mesoderm cells. On flow cytometry, the cells expressed MSC-specific markers. Specifically, these cells were negative for CD14 ($0.12\%$), CD19 ($0.15\%$), CD34 ($0.17\%$), CD45 ($0.13\%$), and HLA-DR ($0.08\%$) surface expression but were positive for CD73 ($100\%$), CD90 ($100\%$), and CD105 ($99.3\%$) surface expression (Figure 1E). Thus, according to the standards of MSCs of the International Society for Cell and Gene Therapy [29], the obtained human ADSCs were confirmed to be MSCs.
Characterization of the human ADSC-Exos The protein concentration of the ADSC-Exos per milliliter was 1.42±0.2 µg. Flow cytometry showed that the isolated ADSC-Exos expressed all three markers (CD9, CD63, and CD81). Among the three analyzed markers, CD81 had the highest expression, while CD9 had the lowest expression. The percentages of exosomes positive for CD63 (Figure 2A), CD81 (Figure 2B), and CD9 (Figure 2C) were $91.2\%$, $99.6\%$, and $76.0\%$, respectively. ADSC-Exos also showed the morphology like a disk or cup (Figure 2D).
Recovery of the peripheral capillary SpO 2 level after ADSC-Exos injection Two to three hr after acute disturbance of blood flow, signs of peripheral cyanosis in the limb were observed in both groups (Figures 3A and B). The SpO2 level recovered more rapidly in the treatment group than in the placebo group. The average SpO2 level in the normal mice was $97.73\%$±$0.46\%$. After 3 hr of ischemia, the SpO2 levels in the placebo and treatment groups were $74.93\%$±$1.61\%$ and $74.33\%$±$1.74\%$, respectively. The level in both groups significantly decreased compared with that in the normal mice ($P \leq 0.05$). From D7 to D21, the SpO2 level in the treatment group increased from $85.31\%$±$1.97\%$ to $91.53\%$±$2.16\%$ ($P \leq 0.05$), while that in the placebo group increased from $81.91\%$±$1.15\%$ to $87.49\%$±$1.77\%$ ($P \leq 0.05$). On D28 after injection, the SpO2 level in both groups was over $94\%$, and there was no significant difference found (Figure 3C).
Recovery of the blood flow to the feet In the normal mice, the paw pads and toes in both hindlimbs simultaneously appeared blue 71.00±1.87 sec after trypan blue injection into the tail vein (Figure 4). On D3, the average time to blue staining of the ischemic hindlimbs was 1089±131.95 sec in the placebo group and 880.40±142.27 sec in the treatment group ($P \leq 0.05$). The average time to trypan blue staining of the ischemic hindlimbs in the treatment group decreased considerably from D7 to D28. On D28, the paw pads and toes stained blue after 181.90±22.42 sec in the treatment group. The time did not significantly differ between the treatment group and normal mice ($P \leq 0.05$). However, it significantly differed between the treatment and placebo groups ($P \leq 0.05$). The restoration of blood flow was evaluated using the trypan blue assay in the mice, which revealed that the blood flow in the treatment group was more improved than that in the placebo group.
Recovery of the limb function Three hours after surgery, the mobility of the ischemic hindlimbs in both groups was dramatically reduced, and the hindlimbs were dragged when the mouse moved. The mice with normal limb function were used as a reference to evaluate mobility. In the normal mice, the pedal frequency was 45.03±4.45 times/10 sec; in the two groups, almost all ischemic hindlimbs lost mobility in water after 1 day. In the treatment group, the hindlimb function was restored to normal after 14 days (42.8±4.07 times/10 sec), and there was no significant difference from the normal mice ($P \leq 0.05$). Meanwhile, in the placebo group, it took up to 21 days for the limb function to return to normal (39.53±4.64 times/10 sec) (Figure 5).
Neovascularization Stereoscopic microscopy showed that neovascularization improved in both groups. Vascular growth was stronger in the treatment group than in the placebo group. After 7 days, blood vessels under the second ligation in both groups developed in response to ischemia compared with those in the normal mice. Multiple vascular branches arising from the saphenous artery developed laterally (Figures 6B2 and B3, black arrow). Remarkably, many small and short blood vessels formed around the first ligation and grew down toward the second ligation in the treatment group (Figure 6A3, blue arrow); however, these were faintly expressed in the placebo group. After 28 days, the new vasculature developed wavy tortuous shapes connecting the two ligations. Vascular development under the second ligation and posterior thigh was also stronger in the treatment group than in the placebo group.
Histological structure of the muscle tissue Muscle tissue structure restoration and blood vessel formation were assessed via histology. H&E staining showed that the skeletal muscle of the normal mice had an orderly arrangement of muscle cells in bundles. In cross-section, the muscle cells were polygonal in shape and relatively uniform in size. Each muscle cell had multiple nuclei distributed peripherally and stained blue‒black with hematoxylin. The cytoplasm was stained pink with eosin. However, the histological muscle structure of the treatment and placebo groups exhibited microscopic changes related to degeneration at 7 days after ischemic induction. In the placebo group, the tissue structures were destroyed and lost muscle bundles (Figure 7A2, blue arrow). The muscle cells were disorderly arranged and expressed abnormal shapes (Figure 7B2, blue head arrow). The cytoplasm exhibited fragmentation and shrinkage (Figure 7B2, blue arrow), while the nuclei were concentrated near or in the cytoplasm (Figure 7B2, yellow arrow). The muscle lesions were milder in the treatment group than in the placebo group. In the treatment group, the cells retained their normal shape and were arranged in an orderly manner (Figure 7B3). The small blood vessels were also observed to be interspersed between the muscle cells. After 28 days, the muscle structure of the mice without hindlimb loss in both groups significantly improved. However, the muscle structure at the postinjury sites appeared in tissue areas without recovery in the placebo group (Figure 7A4, blue arrow). In contrast, the muscle structures were similar to normal tissue structures in the treatment group (Figure 7A5). The density of the blood vessels was also higher in the treatment group than in the placebo group and normal mice (Figure 7C4 and C5, green arrow).
Reduction of the hindlimb necrosis grade The effect of the ADSC-Exos on the recuperation of acute ischemic injury in both groups was observed for 28 days. The limb necrosis grade was divided according to the classification described by Goto et al. [ 28] (Figure 8A). The ADSC-Exos considerably reduced hindlimb necrosis after ischemic injury. In the placebo group, the rate of high-grade limb necrosis rapidly increased until 13 days after ischemia induction. Meanwhile, in the treatment group, the change in the limb necrosis grade stopped on D7 (Figure 8B). In the placebo group, $40.00\%$ of the mice fully recovered, and $60.00\%$ had hindlimb necrosis, mainly grade I and II. The percentages of grade I, II, III, and IV necroses were $30.00\%$, $13.33\%$, $6.66\%$, and $10.00\%$, respectively. Meanwhile, in the treatment group, up to $66.67\%$ of the mice completely recovered. Remarkably, the treatment group did not have grade III or IV necrosis, although $26.67\%$ and $6.67\%$ of the mice had grade I and II necroses, respectively (Figure 8C).
## Discussion
In mammals, the tetraspanin family of proteins includes more than 30 members. Tetraspanins have been found on the plasma membrane or in the endothelial compartments or lysosomes of most cell types [30]. This family consists of distinct proteins that are characterized by their common specific molecular structure. Owing to the high content of tetraspanins, such as CD9, CD81, CD63, and CD82, in exosomes, they are commonly used as exosome recognition markers.
Exosomes are essential paracrine components produced by ADSCs that have various biological functions [31]. In our research, exosomes were isolated from human ADSCs, and the expression of endosome-specific tetraspanins was evaluated. The analysis showed that the ADSC-Exos expressed CD9, CD63, and CD81, which is consistent with previous findings [32]. However, researchers reported that the percentages of ADSC-Exos markers positive for CD9, CD63, and CD81 were $89.8\%$, $52.15\%$, and $81.18\%$, respectively, which were higher than those in our research [33]. According to the extensive proteomic analysis of exosomes conducted by Jankovičová et al., exosomes derived from different cell types and even the same cells can have significant differences in their specific tetraspanin profile both qualitatively and quantitatively [34]. As another example of alteration in the expression levels of these markers, ADSC-Exos isolated in the study by Mitchell expressed only CD63 without the presence of CD9 and CD81 [35]. Moreover, based on the findings of the coexpression of tetraspanins, the exosomes isolated in this study included many subpopulations, which is similar to a previous report [36]. For example, exosome populations either coexpressed all three markers or expressed only CD81.
In ischemic mice, the hindlimbs show significant signs of acute ischemia. The hindlimbs of mice are perfused by the external iliac artery. This artery gives rise to branches that further divide into accessory vessels that enter the muscles [37]. Broken femoral blood vessels disrupt the perfusion of peripheral tissues, leading to a lack of nutrients and oxygen supply to the tissues. In ischemic tissues, the oxygen supply is significantly reduced, causing peripheral cyanosis. Peripheral cyanosis, a condition in which peripheral tissue color turns blue-violet or purple-black, is a sign of significant hypoxia. The clinical presentation of cyanosis usually occurs at an SpO2 level of ≤$85\%$ [37]. During the treatment period, the SpO2 level of our treatment group increased significantly and recovered to above $85\%$ from D7 after exosome injection, while this phenomenon occurred from D14 in the placebo group. These results show that the ability of ADSC-Exos to improve hypoxia is better than that of PBS.
The time to trypan blue staining in the paw pad also confirmed the effect of the ADSC-Exos on the blood circulation of the hindlimb ischemic mice with grade I and 0 necroses. Meanwhile, the time for blood circulation to the extremities was much shorter in the treatment group than in the placebo group.
Along with the significant physiological improvements, limb function was also more effectively restored in the treatment group. In this group, the water pedal frequency in 10 sec increased significantly and recovered to normal from D14 after transplantation, while in the placebo group, this phenomenon took 21 days to occur. Our results are consistent with previous reports. In one study, treatment with EPC-derived exosomes improved the blood oxygen concentration and shortened the recirculation time compared with PBS injection [133]. Meanwhile, Hu et al. reported that iMSC exosome-treated anemic mice showed a restored perfusion level on D14 and had motility better than that of PBS-injected mice [25]. The time to trypan blue staining in the paw pad of the mice also confirmed the efficacy of extracellular secretion in restoring blood circulation in mice with mild injury (acute I and 0). The time for blood circulation to the extremities was much shorter in our treatment group than in our placebo group.
Collateral circulation is a network of blood vessels found in most tissues. These blood vessels connect adjacent arteries, thereby limiting tissue damage caused by sudden ischemia. Collateral circulation plays an important role in maintaining blood flow and limiting damage to ischemic tissue [38]. After 7 days of ischemia, the majority of the mice in both groups in our study developed collateral circulation in the lower thigh. The branch vessels tended to develop bilaterally. However, in the treatment group, small vascular structures formed from the superior knot site, which was not observed in the placebo group. The histological structure also showed that the blood vessel density in the treatment group was higher than that in the placebo group. However, the new blood vessels between the muscle fibers were small, so the blood flow to the lower extremities was very slow compared with that before vascular ablation.
In addition, the muscle tissue showed less damage and inflammation in the treatment group than in the placebo group. For up to 28 days, regeneration of the new vessels and muscles was stronger in the treatment group than in the placebo group. The vascular density was higher, and the tissue structure was more similar to normal in the treatment group than in the placebo group. As previously reported, the capillaries in mice treated with exosomes had a higher density than those in untreated mice, which was evident on D7 after transplantation [25]. These results suggest that treatment with ADSC-Exos can stimulate angiogenesis and repair acute ischemic muscle injuries.
Neovascularization therapy is an effective therapeutic strategy for blood flow recovery after arterial occlusion [37, 39]. The primary mechanisms are arteriogenesis and angiogenesis [39]. The formation of new blood vessels as well as the development of preexisting collateral vessels could be an underlying mechanism for the improvement of blood delivery to peripheral tissues, thereby increasing oxygen supply to these tissues. Herein, the ADSC-Exos not only restored perfusion and SpO2 but also promoted muscle tissue structure regeneration based on the histological findings. Previous studies have also reported that exosomes derived from ADSCs could enhance the growth of skeletal muscle [40]. Accordingly, these improvements led to significant decreases in limb necrosis and recovery of limb function in mice treated with ADSC-Exos.
Many studies have been conducted and have shown the important role of exocrine factors from MSCs in the regeneration of damaged tissue. These factors are capable of stimulating endogenous repair, regulating inflammation, and protecting tissues at risk [41]. In culture, ADSCs contain components involved in these processes, especially signaling factors that help regenerate blood vessels and repair tissue after injury as exosomes [42]. Exosomes obtained from ADSC culture have potent activities in ischemic models [43]. Exosomes are highly valued for their effectiveness in the treatment of lesions. They participate in the regeneration process through the transport of biomolecules from cells to target cells in the injured tissue. These molecules activate different signaling pathways that lead to alterations in biological activities in recipient cells [19]. Exosomes also show potential applications in the treatment of various pathologies: MSC-EVs promote angiogenesis in ischemic injury [44], restore poststroke function by promoting endothelial cell proliferation and capillary network expansion [45], promote collagen synthesis and angiogenesis [27], inhibit fibrosis and inflammation in the heart, and improve cardiac function in a myocardial infarction rat model [46].
The results of this study demonstrate that ADSC-Exos have the ability to significantly improve ischemia of the hindlimbs in a mouse model. ADSC-Exos promote rapid and powerful vascular regeneration, causing blood flow to circulate to the tissue injury due to ischemia. This reperfusion supports the restoration of peripheral SpO2 and regenerates muscle tissue, resulting in the rehabilitation of limb function and reduction in the rate of limb necrosis.
**Figure 1:** *Human adipose-derived stem cells expressed the MSC phenotype proposed by ISCT* **Figure 2:** *Flow cytometry characteristics of isolated exosomes from human adipose-derived stem cells. They expressed markers of CD63 (A), CD81 (B), and CD9 (C), and have a morphology like disk or cup (D)* **Figure 3:** *Recovery of the peripheral capillary SpO2 level after ADSC-Exos injection* **Figure 4:** *Recovery of the blood flow to the feet* **Figure 5:** *Recovery of the limb function. The mobility of mice climbs sharply decreased immediately after induced ischemia in both groups. The treatment group completely recovered after 14 days, while the untreated group took up to 21 days* **Figure 6:** *Formation of new blood vessels. Vascular structure in normal mice (A-C). After 7 days, in comparison to normal mice, the blood vessel under second ligation developed in both the placebo group (D-F) and the treatment group (G-H). After 28 days, the development of new blood vessels connected between the two ligation sites in both the untreated group (K-M) and treated mice (N-M) was stronger. However, the vascular development under second ligation and posterior thigh in treated mice were also stronger than in untreated mice groups. The arrows point to new blood vessels* **Figure 7:** *Histological muscle tissue structure at 7 days and 28 days after injection* **Figure 8:** *Ratio of hindlimb necrosis caused by acute ischemia in both groups. The degree of limb necrosis was assessed and categorized according to the recommendations of Goto et al. [28] (A). The damage process of untreated mice lasted longer than treated mice (B). The data revealed that injection of AD-EXOs dramatically reduced hindlimb necrosis compared with a placebo group (C)*
## Conclusion
Exosomes derived from human ADSCs significantly improve acute ischemic symptoms in a mouse model. ADSC-Exos accelerate the recovery of physiological states, such as peripheral SpO2, blood circulation, and limb motion function. Injection of ADSC-Exos enhances vascular remodeling and repair of damaged muscle tissue, thereby reducing the limb necrosis grade in treated mice compared with that in untreated mice. ADSC-Exos are confirmed to be a potential treatment for hindlimb ischemic diseases.
## Authors’ Contributions
THNN, PVP, and NBV designed the experiments; THNN and NBV performed experiments and collected data; THNN, PVP, and NBV discussed the results and strategy; NBV supervised, directed, and managed the study; THNN, PVP, and NBV approved the final version to be published.
## Conflicts of Interest
The authors declare that they have no competing interests.
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|
---
title: Benefits of bone marrow-derived mesenchymal stem cells primed with estradiol
in alleviating collagen-induced arthritis
authors:
- Monireh Jahantigh
- Seyyed Meysam Abtahi Froushani
- Nahideh Afzale Ahangaran
journal: Iranian Journal of Basic Medical Sciences
year: 2023
pmcid: PMC10008397
doi: 10.22038/IJBMS.2023.68112.14882
license: CC BY 3.0
---
# Benefits of bone marrow-derived mesenchymal stem cells primed with estradiol in alleviating collagen-induced arthritis
## Abstract
### Objective(s):
To investigate the effects of the oestradiol (ES) pulsed bone marrow-derived mesenchymal stem cells (BM-MSC) to treat adjuvant-induced arthritis in Wistar rats.
### Materials and Methods:
BM-MSCs were pulsed with ES (0, 10,100, and 1000 nM) for 24 hr. RA was induced by collagen and Freund’s Complete Adjuvant into the base of the tail of Wistar rats.
### Results:
The least effective concentration of ES that can promote potent anti-inflammatory properties in the MSC population is 100 nM. At this concentration, ES increases the inhibition of the polyclonal T lymphocyte proliferation, production of IDO, IL-10, Nitric oxide, and TGF-β, and expression of CXCR4 and CCR2 mRNA in the MSC population. Accordingly, the RA rats were treated with 2×106 MSCs or ES-pulsed MSCs (100 nM) on day 10 when all animals had developed signs of RA. ES-pulsed BM-MSCs reduced the severity of RA more profoundly than treatment with BM-MScs alone. The ability of ES-pulsed BM-MSCs to reduce symptoms and RA markers like CRP, RF, and nitric oxide was comparable to that of prednisolone. Prednisolone was more successful in reducing inflammatory cytokines than treatment with ES-pulsed BM-MSCs. ES-pulsed BM-MSCs were more successful in increasing anti-inflammatory cytokines than treatment with Prednisolone. The ability of ES-pulsed BM-MSCs to decrease the level of nitric oxide was comparable to that of prednisolone.
### Conclusion:
ES-pulsed BM-MSCs may be a helpful strategy in RA control.
## Introduction
Rheumatoid arthritis (RA) is an inflammatory disease characterized by joint inflammation and synovitis, swelling, autoantibody production, bone dysfunction, and cartilage degradation [1, 2]. The main features of RA are inflammatory cell infiltration in the joints, increased concentration of inflammatory factors, and invasion of adjacent cartilage, all of which cause bone erosion and cartilage tissue damage [2]. The etiology of the disease is not known. However, it might be an autoimmune disease owing to the production of autoantibodies against citrulline proteins [3, 4].
Several conventional agents are administrated to alleviate RA progression, such as steroidal and nonsteroidal anti-inflammatory drugs, disease-modifying anti-rheumatic drugs (DMARDs), and novel biological therapeutic agents. Despite advances in RA control, no drug can completely cure the disease. In addition, most of these drugs have dangerous side effects [3, 5, 6]. In this regard, much attention has been paid to the therapeutic potential of mesenchymal stem cells (MSCs). MSCs, like bone marrow-derived mesenchymal stem cells (BM-MSCs), are found in the bone marrow and differentiated into adipocytes, osteoblasts, and chondrocytes. Due to their high immunomodulatory and regenerative potentials, these cells could be considered a practical approach to controlling autoimmune diseases such as rheumatoid arthritis [7]. Administration of the BM-MSCs helped restore damaged cartilage and decreased synovial inflammation [8]. MSC therapy is a unique strategy for migrating transplanted cells toward injured targets. However, a small percentage of the transplanted cells reach the target tissues [9-11]. The increased number of MSCs in the injury sites may improve the efficacy of MSC transplantation [9]. The presence of estrogen receptors (ERα and ERβ) and their responsive elements within MSCs suggest that estrogen is involved in modulating the function of these cells. The studies have reported that the differentiation, migration, and immune regulatory function of MSCs depend on multiple nuclear receptors such as nuclear steroid receptors [12, 13]. Many studies have shown that estrogen derivatives, as well as pregnancy, play a protective role in rheumatoid arthritis by directly modulating immune responses [14]. On the other hand, the use of estradiol (17β-estradiol) has a pivotal role in proliferation, differentiation, and maturation of hematopoietic progenitor cells expressing estrogen receptor α (ERα) [15, 16]. It has been reported that treating MSCs with 17β-estradiol improved the viability and function of neutrophils [7]. Some studies have investigated the treatment of BM-MSCs with estradiol for the treatment of diabetes [9], experimental autoimmune encephalomyelitis [17], and ulcerative colitis. However, the efficiency of ES-pulsed MSCs for the treatment of RA has not been reported. Thus, this study investigates the role of the BM-MSCs primed with estradiol (ES) in alleviating collagen-induced arthritis in Wistar rats.
## Materials and Methods
Materials RPMI 1640, phosphate-buffered saline, fetal bovine serum, and penicillin-streptomycin were prepared by Biowest Company. Dimethyl sulfoxide, 3,3′,5,5′-Tetramethylbenzidine, Freund’s Complete Adjuvant (FCA), estradiol, and collagen II were procured from Sigma Aldrich Company (USA). Prednisolone was purchased from Aburaihan company (Tehran-Iran). Cytokines were assessed by Commercial kits prepared by Peprotech company (USA), and other kits were purchased from Sigma Aldrich company (USA).
Isolation and proliferation of MSCs The BM-MSCs were isolated and proliferated, as reported previously [18]. Briefly, BM-MSCs were obtained from tibias and femurs of Wistar rats (6 weeks old). They were washed twice and centrifuged at 1200 rpm for 5 min in PBS, and cells were plated in tissue-culture flasks at the concentrations of 0.3 to 0.4×106 cells/cm2. DMEM medium was supplemented with $15\%$ fetal bovine serum, and cells were incubated in a humidified incubator with $5\%$ CO2 at 37 °C. After four days, the culture mediums were collected, centrifuged, and cells were seeded. Cells were trypsinized using Trypsin/EDTA, counted, and passed at 1:3 ratios (about 1.5 × 106 cell/75-cm2 flask). The MSCs were incubated upon $70\%$ confluency, trypsinized, collected, and used for the following experiments. Cells in the sediment were soaked in 1 ml sterilized PBS and formaldehyde (40 µl) was added to the mixture and incubated at 37 ºC for 10 min. The investigation of phenotyping of the cultured cells was conducted as reported by Jones et al. [ 19].
Immunophenotype of MSCs To characterize MSCs, these cells at passage three were stained with a fluorescently labeled monoclonal antibody (anti-rat CD29 (Integrin b chain; Ha$\frac{2}{5}$; FITC), CD90-PCY5 (Thy-1/Thy-1.1-FITC), and CD45-FITC) as described formerly [20]. The stained cells were monitored immediately on a DAKO flow cytometer (Partec, Germany).
Estradiol treatment of MSCS Cells at the third passage were treated with 0, 10, 100, and 1000 nM 17β-estradiol for 24 hr. The medium was gathered, and the cells were washed three times with PBS.
Evaluation of the immunoregulatory potential of MSCs: To evaluate the potency of MSCs in inhibiting lymphocyte proliferation, spleens were aseptically isolated from 3 Wistar rats. MSCs were plated at 4×103 cells/cm2. After a short adherence period, splenocytes were incubated with the plated MCs (10 splenocytes to 1 MSC) in a trans-well system (0.4-mm pore size membrane, eight-well strip, Nunc). For mitogenic stimulation, splenocytes were stimulated with phytohemagglutinin (5 μg/ml). After five days, the solenocytes were pulsed with 20 μl of the MTT solution (final concentration: 5 mg/ml). To dissolve the formazan crystal, 150 ml DMSO was added to plates after four hours. The plates were shaken vigorously. The optical density (OD) at 550 nm was monitored by a microplate reader (Dynatec, Dockendorf, Germany). The results were expressed as the proliferation index (PI) on the MTT assay calculated according to the ratio of OD550 of stimulated cells with PHA to OD550 of non-stimulated cells.
For in vitro examination, the isolated conditioned media were filtered through a 0.22 mm membrane, and the levels of TGF-β and IL-10 in conditioned media were monitored using commercial ELISA kits (Peprotech Company, USA). The levels of nitric oxide (NO) in conditioned media were determined by the Griess method [4]. The biological activity of Indoleamine 2, 3-dioxygenase (IDO) was evaluated by monitoring the level of kynurenine in the isolated conditioned media [20].
In original research, the pivotal importance of CCR2 and CXCR4 chemokine receptors has been shown in migrating mesenchymal stem cells toward damaged tissues [9]. To analyze the mRNA expression of CXCR4 and CCR2, total RNA was extracted from the MScs using the Trizol reagent. Then, the complementary DNA was synthesized by isolated mRNA. PCR amplification was run in triplicate by the SYBR-Green kit (Parstous, Iran) according to the manufacturer’s guidelines. The sequence of primers was selected based on a previous article [9]. The primers are shown in Table 1. Cyclic conditions were conducted in an Eppendorf Master cycler (Hamburg, Germany). In the case of CXCR4, each cycle is composed of 5 min at 94 °C, followed by 40 cycles of 30 sec at 94 ºC, 1 min at 52 ºC, and a final step of 5 min at 72 ºC. In the case of CCR2, each cycle is composed of 5 min at 94 °C, 30 sec at 94 °C, 1 min at 55 °C, and 5 min at 72 °C. Each cycle of β-actin consisted of 5 min at 94 °C, 1 min at 56 °C, and 1 min at 72 ºC. PCR progressed up to 35 cycles. All target genes’ findings were expressed as relative fold change (RFC) from the control group (estradiol concentration = 0) values.
Induction and evaluation of the RA Animal studies were conducted under the ethical code “IR, UU.AEC. 476/PD3” issued by the Ethics Committee for Laboratory Animals of Urmia University. RA was induced as described by Brand et al. [ 21]. Male Wistar rats (6–8 weeks old) were bought from the Pasteur Institute of Tehran, Iran. Animals were adapted to the environment for seven days. To induce RA, collagen II (2 mg/ml) was dissolved in acetic acid, homogenized overnight, and stored at -20 °C. Before administration, the collagen solution was mixed with 2.5 ml FCA and stored on ice. The emulsion was intradermally administrated using a syringe with 27–25 gauge at the base of the tail. The experimental rats were divided into five groups, each group comprising ten rats designated as: 1) Healthy rats included animals in which RA was not induced, 2) Negative control included the RA induced rats without the treatment, 3) Positive control included the RA induced rats that received daily treatment with 2 mg/kg of prednisolone, 4) BM-MSCs included the RA induced rats and treated with 2×106 BM-MSCs (BM-MSCs), and finally 5) BM-MSCs+ES include rats induced with RA and treated with 2×106 ES-pulsed BM-MSCs.
The intensity of RA was monitored by the following scoring system for each limb: 0=Normal paw; 1=Erythema of the toe; 2=Erythema and swelling of paws; 3=Swelling of the ankle; 4=Complete swelling of the whole leg and incapacity to bend it. The maximum arthritis score can be 16. The observations were done every morning during the investigation by three independent observers. The therapy was started on day 10 when all animals had developed a sign of RA. Treatments were performed intraperitoneally. The signs included inflammation, redness, and stiffness in joints. Animals were monitored until day 35 after induction.
Serological evaluation At the end of the survey, blood samples were isolated from the hearts of experimental rats under deep anesthesia. Nitric oxide concentration was investigated as reported by Bryan et al. [ 22] and based on the Griess reaction method. Also, the serum concentration of MPO was assessed by an ELISA reader at 450 nm wavelength, as reported earlier [23]. The serum concentrations of rheumatoid factor (RF) and C-Reactive Protein (CRP) were assessed as reported by the kit producer company.
Ex vivo investigation of lymphocytes proliferation and cytokine profile At the end of the study, the rats were euthanized, and the spleens were isolated, aseptically. The spleen-to-body weight ratios were reported using the formula: Spleen Index= (spleen weight/body weight) ×100.
Next, spleens were aseptically ground in a five milliliter media culture of RPMI containing $10\%$ FBS and then passed from a network with a size of 0.2 mm. To remove red blood cells, the samples were adjacent to ACK-RBC lysis buffer for 5 min. A splenocyte suspension (2×106 cells/ml) was cultured in 6-well plates and primed with collagen II (50 µg/ml) for 72 hr. The culture supernatants were used to determine the levels of IL-6, IL-1β, IL-10, IL-17, TNF-α, and TGF-β using ELISA kits according to the manufacturer’s guidelines.
Furthermore, splenocytes were cultured in 96-well flat-bottomed plates (105 cells/100 µl/well) and were primed with collagen II (50 µg/ml) for 72 hr or the medium alone, as control, for 72 hr. Afterward, each well was pulsed with 20 µl of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium) solution (5 mg/ml) for four hours. Then, the plates were centrifuged, and the supernatant was isolated. To dissolve formazan crystals, 150 µl of dimethyl sulfoxide was mixed into each well, and the plates were shaken vigorously. The absorbance of wells was monitored at 492 nm. The proliferation index was determined according to the ratio of the absorbance of the splenocytes pulsed with collagen II to the absorbance of non-pulsed cells.
Data analysis *The data* were investigated for normality using the Kolmogorov-Smirnov test. Since the data have a normal distribution, One-Way-analysis was used. The data were reported as mean ± standard deviation.
## Results
Immunophenotype analysis of BM-MSCs The flow cytometry results showed that the BM-MSCs were positive for CD29 and CD90, two mesenchymal stem cell markers, and negative for CD45, an indicator of hematopoietic cells (Figure 1).
Evaluation of the immunoregulatory potential of BM-MSCs in vitro In the presence of estradiol, the MSCs, ability to inhibit lymphocyte proliferation was strengthened. By increasing the estradiol concentration from 10 nM to 100 nM, the potential of MSCs to inhibit lymphocyte proliferation enhanced (Figure 2A). Nevertheless, there was no significant difference between the anti-proliferation potential of MSCs treated with 100 or 1000 nM of estradiol (Figure 2A). The levels of TGF-β, IL-10, NO, and IDO activity were significantly increased in conditioned media gathered from all ES-primed MSCs compared with conditioned media gathered from MSCs without treatment (Figure 2B-E). At the same time as the concentration of ES increased from 10 nM to 100 nM, the secretion of IL-10, TGF-β, and NO into the conditioned medium of MSCs significantly increased (Figure 2B-E). Regarding the production of IL-10 and TGF-β in the conditioned medium, there was no statistically significant difference between the MSCs pulsed with 100 nM and 1000 nM of ES (Figures 2B and C). Moreover, the NO level was significantly higher in the conditioned media isolated from estradiol-primed MSCs at 100 nM compared with the conditioned media gathered from MSCs pulsed with 0.1 mM of estradiol (Figure 2D). Although treatment with ES increased the IDO activity in the supernatant of MSCs, there was no significant difference between different groups treated with various concentrations of estradiol (Figure 2E).
To monitor the effects of estradiol on CCR2 and CXCR4 mRNA, reverse transcription PCR was performed. As depicted in Figure 3, all ES concentrations increased relative fold change (RFC) of both chemokine receptors in the MSCs population compared with un-pulsed MSCs. Increasing the estradiol concentration from 10 nM to 100 nM increased the expression of CCR2 and CXCR4 mRNA (Figure 3). However, the level of CCR2 and CXCR4 mRNA expression decreased significantly when the estradiol concentration increased from 100 to 1000 nM. The mRNA level of CCR2 was even lower than the mRNA expression of CCR2 induced by the concentration of 10 nM (Figure 3).
Based on in vitro findings, the least effective concentration of estradiol that can induce a potent anti-inflammatory phenotype in the MSCs was 100 nM. Therefore, this concatenation was chosen for in vivo studies.
Results of using mesenchymal stem cells in rats with RA Treatments of RA rats were initiated on day 10 post-induction when individual animals had an arthritis index of ≥1. The peak of the arthritis index was recorded on the 15th-day post-RA induction (Figure 4A). All therapies could significantly repress the arthritis index of RA rats 20 days post-induction, compared with untreated RA animals (Figure 4A). The average mean arthritis index was significantly regressed in the RA rats receiving prednisolone or BM-MSC+ES compared with RA rats receiving un-pulsed MSCs during the study period (Figure 4B). Albite, RA rats receiving prednisolone (positive control) showed a faster decrease in the arthritis index compared with RA rats receiving BM-MSC+ES. However, there was no significant difference in the average mean arthritis index between the two groups (Figure 4C).
Ex vivo results Figure 5 illustrates the concentration of pro-inflammatory and anti-inflammatory cytokines in spleen supernatants. The concentrations of TNF-α, IL-1β, IL-6, IFN-γ, and IL-17 were significantly higher in the negative control compared with the healthy rats. The rats in BM-MSCs and BM-MSCs+ES groups showed lower concentrations of these pro-inflammatory cytokines compared with the negative control group (Figure 5). There was no significant difference in interferon-gamma levels between the two groups treated with BM-MSCs or BM-MSCs+ES. Statistically, in reducing the levels of other inflammatory cytokines, treatment with BM-MSCs+ES showed better performance than treatment with BM-MSCs. The RA rats in the positive (prednisolone) group showed the lowest concentration compared with other RA rats for IL-1β, IL-6, and IFN-γ (Figure 5).
The concentrations of anti-inflammatory cytokines of IL-10 and TGF-β were significantly lower in the normal rats compared with other groups (Figure 5). The data showed that RA rats treated with BM-MSCs+ES could significantly increase the levels of IL-10 and TGF-β compared with the levels of this cytokine gathered from the splenocytes culture of RA rats treated BM-MSCs (Figure 5). Statistically, ES-pulsed BM-MSCs were more successful in increasing anti-inflammatory cytokines than treatment with prednisolone (Figure 5).
The splenocyte proliferation and the splenic index increased significantly in the rats with RA groups compared with the healthy control group. The results also showed that the treatment of the rats with BM-MSCs, BM-MSCs+ES, and prednisolone decreased the splenic index compared with the healthy rats (Figure 6). As shown in Figure 6, cell therapy or prednisolone use significantly regressed the proliferative response of the splenic lymphocytes compared with the untreated RA group. Treatment with BM-MSCs+ES or prednisolone resulted in a further reduction in lymphocyte proliferation compared with therapy with BM-MSCs (Figure 6). However, there was no significant difference between the two groups treated with BM-MSCs+ES and prednisolone.
As shown in Figure 7, the induction of RA increases the serum concentrations of rheumatoid factor and CRP. In RA rats treated with ES-pulsed MSC or MSC, both parameters were reduced significantly. However, the BM-MSCs+ES led to a more significant decline in the serum levels of CRP compared with treatment with un-pulsed BM-MSCs (Figure 7). The ability of ES-pulsed BM-MSCs to reduce CRP and RF was comparable to that of prednisolone (Figure 7). Also, RA induction could significantly mount the levels of MPO and nitric oxide in the sera of rats. The findings showed that RA rats treated with BM-MSCs+ES could significantly reduce the serum levels of nitric oxide more than this factor in RA rats treated BM-MSCs (Figure 7). The ability of ES-pulsed BM-MSCs to decrease the level of nitric oxide was comparable to that of prednisolone (Figure 7). Finally, prednisolone significantly reduced the MPO level more than the other treated groups (Figure 7).
## Discussion
This survey was conducted to investigate the effects of the estradiol (ES) pulsed bone marrow-derived mesenchymal stem cell (BM-MSC) to ameliorate adjuvant-induced arthritis in Wistar rats. In the first step, the effect of different concentrations of estradiol ES on the immunoregulatory function of MSCs was evaluated. In vitro results showed that the least effective concentration of ES that can promote potent anti-inflammatory properties in the MSC population is 100 nM. At this concentration, ES increases the inhibition of the polyclonal T lymphocyte proliferation, production of IDO, IL-10, nitric oxide, TGF-β, and expression of CXCR4 and CCR2 mRNA in the MSC population. Based on in vitro results, the RA rats were treated with 2×106 MSCs or ES-pulsed MSCs (100 nM) on day 10 when all animals had developed signs of RA. Results showed that the treatment with BM-MSC pulsed with estradiol led to more relevant and comparable results with prednisolone to reduce the symptoms of RA compared with the treatment with un-pulsed BM-MSC.
Low delivery of mesenchymal stem cells to inflamed tissues is one of the main challenges of using these cells, despite their beneficial modulating power [17]. A recent study suggested that treating MSCs with estradiol promotes the migration of cells in cultured MSCs and a cell therapy model of diabetes via adjustment of critical mediators of cell trafficking like hypoxia-inducible factor-1a (HIF-1a) [9]. Interestingly, gender could affect the performance of MSCs. Female MSCs stressed by one h hypoxia or LPS (200 ng/ml) significantly produced lower TNF and IL-6 and significantly greater VEGF release than MSCs isolated from male mice [24]. Estradiol also improves the differentiation ability and bone regeneration potential of implanted BM-MSCs in a rabbit model of the radial non-union segmental defect [25].
One of the most critical chemokines and their receptors that affect the migration of BM-MSCs to inflamed areas are stromal cells derived-factor 1 (SDF-1)/ CXCR4 and monocyte chemo-attractant protein (MCP)-1/ CCR2 [9]. Estrogens have long been known to exert their functions by turning genes on and off through a multi-step process. Estrogens primarily use two classical nuclear receptors, estrogen receptor α (ERα, Esr1) and ERβ (Esr2), to regulate gene expression. In addition, estrogen receptors change the transcription of genes by interacting with various histone-modifying enzymes and chromatin-remodeling complexes [17, 25]. Our results showed that estradiol treatment increased the mRNA expression of CXCR4 and CCR2. However, increasing the concatenation of estradiol from 100 nM to 1000 nM was associated with a decrease in the mRNA expression of these two chemokine receptors in MSCs. Therefore, in vitro, conditioning MSCs with 100 nM of estradiol is more effective in increasing the migration potential of stem cells in vivo. In 2015, Mirzamahmoudi et al. showed that treatment of stem cells with estradiol increased the expression of CXCR4 and CCR2 through induction of hypoxia-inducible factor 1α (HIF-1α) [9].
The main factors responsible for the immunosuppressive and anti-inflammatory benefits of BM-MSCs include surface molecules (like Galectins, PDL1, and HLA-G), anti-inflammatory cytokines (like IL-10 and TGF-β), and secrete some enzymes and molecules (like indole aminepyrrole 2,3-dioxygenase (IDO) and nitric oxide) [20]. The current study indicated that increasing the concatenation of estradiol from 100 nM to 1000 nM did not cause a further increase in the anti-proliferative potential of the lymphocytes and other immunoregulatory mediators. Hereupon, the least impressive concentration of estradiol that can induce potent anti-inflammatory phenotype in the MSCs was 100 nM. All this caused MSCs pulsed with 100 nM of estradiol to be used for the following in vivo investigations.
The main objectives of this survey were to check out the efficacy of ES-pulsed BM-MSCs to decrease the clinical signs and reset the immune system of RA rats compared with MSc-alone or prednisolone. Despite the potent anti-inflammatory effects of glucocorticoids, they have many side effects. Therefore, using alternative therapies such as mesenchymal stem cells is a logical decision [20]. Obtained data in this study revealed that treatment with ES-pulsed BM-MSCs led to a more desirable improvement in the RA severity than using the un-primed BM-MSCs. More importantly, the results of the evaluation of the arthritis index after treatment with ES-pulsed BM-MSCs were similar to those with prednisolone treatment.
Several serum biomarkers have been considered in connection with rheumatoid arthritis or animal models of the disease [20, 26]. CRP is an inflammatory factor for RA. The results showed that BM-MSCs with estradiol decreased CRP concentration, confirming the efficiency of BM-MSCs with estradiol in decreasing inflammation. Rheumatoid Factor (RF) belongs to the immunoglobulins family with different isotypes and affinities directed to the Fc portion of IgG. However, it is not specific to RA and found in rheumatic, non-rheumatic conditions, and even healthy adults [27]. Regarding RF, the results of this study did not show a significant difference in reducing the level of this factor between the groups treated with BM-MSCs+ES and BM-MSCs. Albite, the BM-MSCs+ES promoted a more significant decrease in the CRP levels compared with therapy with BM-MSCs alone. Furthermore, the potential of BM-MSCs+ES to decline CRP and RF was comparable with that of prednisolone. At the molecular level, CRP is synthesized by the liver in response to pro-inflammatory cytokines like IL-6, IL-1β, and TNF-α [20]. As our results showed, treatment with MSCs decreased the level of these pro-inflammatory cytokines. Therefore, reducing the level of CRP is not impossible.
The induction of RA increased the concentration of nitric oxide (NO). Nitrative tissue damage by NO has a close relationship with RA disease. NO induces apoptosis in cartilage and destroys it [4]. A study showed a positive correlation between the serum and synovial fluid of patients with RA and NO concentration [28]. One of the tip-top biomarkers of inflammatory and oxidative stress in autoimmune diseases like RA is the serum level of MPO [29]. Our results also showed that the concentration of MPO was higher in RA rats compared with healthy rats. Similarly, other studies have reported that RA increases the plasma concentration of MPO [30]. Our results indicated that the ability of ES-pulsed BM-MSCs to decline the level of nitric oxide was comparable to that of prednisolone. Albite, prednisolone did better than other groups in reducing the MPO level.
The splenic index was significantly higher in the negative control. It means that the spleen increased its size against RA. The spleen has a pivotal role as a reservoir of monocytes/macrophages and lymphocytes activated during inflammation and produces cytokines and chemokines [31]. The results of the spleen index confirm the relevant results of the treatment protocols. The capability of medication to reduce lymphocyte proliferation restricts the number of potentially pathologic T cells in RA. Both prednisolone and MSCs therapy possess anti-proliferative effects [20]. According to the results of this study, the treatment of BM-MSCs with estradiol increased their immunosuppressive properties, so that the strength will be comparable to prednisolone.
The induction of RA increased the concentration of pro-inflammatory cytokines while decreasing the concentration of anti-inflammatory cytokines. Pro-inflammatory cytokines are potential therapeutic targets for RA, and cytokines promote inflammatory responses in arthritic joints and synovial tissues [3]. TNF-α plays a pivotal role in the inflammatory and immunological responses to RA development and it is generally known as a promising target for an anti-RA drug. IL-1β and IL-6 are critical pro-inflammatory cytokines involved in the development of RA [1, 3, 26]. The Th17 and Th1 cells are critical players in RA disease. IL-17 and IFN-γ possess potent pro-inflammatory properties and are the main factors for Th17 and Th1-mediated immunopathology, respectively [32]. The inflammation process is significantly controlled and balanced by mediators that induce and sustain inflammation and mediators that shut down the process and are called anti-inflammatory cytokines such as IL-10 and TGF-β [1]. Based on our results, prednisolone was more successful in reducing inflammatory cytokines than treatment with ES-pulsed BM-MSCs. Conversely, ES-pulsed BM-MSCs were more successful in increasing anti-inflammatory cytokines than pharmacotherapy with prednisolone. Therefore, it can be assumed that prednisolone acts by paralyzing immune responses, while BM-MSCs act more by amplifying anti-inflammatory responses. This may be another advantage of using estradiol-treated BM-MSCs to control RA.
## Conclusion
Treatment of MSCs with estradiol increased the regulatory potential of these cells compared with untreated MSCs. The results of the in vivo study showed a better improvement in RA signs of rats that received ES-pulsed MSCs compared with the symptoms of RA rats which received MSCs. The clinical results of treatment with ES-pulsed MSCs were comparable to treatment with prednisolone. Due to the excellent potential of ES-pulsed BM-MSCs in reducing the symptoms of the disease, this approach may be a helpful strategy for controlling RA. The main limitation of the current study is conducting a study on rats, and the results cannot be used for other animals and humans. Further studies are required to show the efficiency of BM-MSCs treated with estradiol in humans.
## Authors’ Contributions
SMAF designed the experiments; MJ performed experiments and collected data; SMAF and NAA supervised, directed, and managed the study; MJ, SMAF, and NAA finally approved the version to be published.
## Conflicts of Interest
None.
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|
---
title: Effect of Haematological Parameters in the Development of Urethrocutaneous
Fistula After Hypospadias Surgery
journal: Cureus
year: 2023
pmcid: PMC10008427
doi: 10.7759/cureus.36033
license: CC BY 3.0
---
# Effect of Haematological Parameters in the Development of Urethrocutaneous Fistula After Hypospadias Surgery
## Abstract
Investigation of the relationship between urethrocutaneous fistula (UCF) development and haematological parameters after hypospadias surgery was aimed for in this study. Patients who underwent tubularized incised plate urethroplasty between January 2015 and June 2021 with the diagnosis of distal hypospadias were included in the study. We divided the participants into two groups based on UCF development. We compared haematological parameters, including neutrophil, lymphocyte, and platelet counts; neutrophil-lymphocyte ratio (NLR); platelet-lymphocyte ratio (PLR); and systemic immune inflammation index (SII) values between the two groups. A total of 78 patients were included in the study. Of the patients, 11 developed UCF. The mean age of the patients was 74.9 ± 42.8 months. Catheter diameter, operation time, neutrophil counts, NLR, and SII values were similar between those with and without UCF ($p \leq 0.05$). However, the UCF group had significantly higher lymphocyte and platelet counts than those without UCF ($p \leq 0.05$). Moreover, the PLR value was significantly lower in the UCF group ($p \leq 0.05$). Patients who developed UCF post hypospadias surgery had a significant association with altered blood cell counts, including increased lymphocytes and decreased PLR rate. The PLR can be used as a biological marker for UCF development.
## Introduction
Hypospadias is a common congenital anomaly that affects approximately 1 in 300 male births. It occurs due to incomplete fusion of the urethral folds on the ventral side of the penis. Multifactorial etiology, including genetic, hormonal, vascular anomalies, and environmental factors, were suggested to play a role in hypospadias development [1].
Hypospadias is treated surgically, and the surgery aims to repair the urination and sexual function and appearance of the penis. Despite significant advances in the surgical treatment of hypospadias, complications occur in approximately one-quarter of cases [2]. The common complications include urethrocutaneous fistula (UCF), penile chord correction and glanular dehiscence, and meatal stenosis. Factors such as age at repair, hypospadias type, surgical technique, surgeon's experience, and the urethral tissue's healing capacity determine the operation's success [3].
The clinical significance of haematological parameters in wound healing remains unknown [4]. Post-surgery wound healing consists of hemostasis, inflammation, proliferation, and maturation stages. Platelets, leukocytes, macrophages, fibroblasts, endothelial cells, and molecules such as interferon, proteoglycans, integrins, matrix metalloproteinases, glycosaminoglycans, and other regulatory cytokines play critical roles during these stages [5]. A disruption in the wound healing stages might cause complications. Blood cell abnormality was implied to play a role in post-surgical complications. Therefore, we hypothesised that an abnormality in blood cell count might be linked to the hypospadias surgery complication of UCF.
The relationship between UCF and haematological parameters has not been studied before. We aimed to study post-surgical UCF development and neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune inflammation index (SII) relationship.
## Materials and methods
Patients with distal hypospadias who underwent hypospadias surgery by a single specialist surgeon from January 2015 to June 2021 were included in the study. The institutional ethics committee of Harran University approved the study (HRU$\frac{.22}{11}$/11). The patients who underwent single hypospadias surgery and were followed up for at least six months were included in the study. The medical charts of participants were reviewed retrospectively. Those with more than one surgery for hypospadias, post-surgery infection, insufficient data on the chart, were excluded. Seventy-eight patients met the eligibility criteria.
The tubularized incised plate urethroplasty technique was used in the surgery. This method consisted of the following stages. *After* general anaesthesia, the meatus was re-evaluated and the surgical procedure was planned. A traction suture was placed on the glans penis. The penile skin was degloved up to the penoscrotal region. Artificial erection was induced by injection of sterile saline solution into the corpora cavernosa through a butterfly needle to determine the degree of deviation. The urethral plate was incised in the midline from the desired meatus point to the hypospadias meatus. After preparing the urethral plate, creating a tube over the catheter, the glandular wings were laterally dissected to cover the tension-free neourethra in the midline. Both sides of the urethral plate were sutured using $\frac{7}{0}$ polydioxanone continuous sutures over a 6-8 Fr silicone urethral catheter according to the urethral calibration. The neourethra was covered with a vascularised dartos flap prepared from the subcutaneous tissue of the dorsal preputial skin or penile shaft. The dartos fascial flap was sufficiently mobilised to prevent penile torsion. The glandular wings and ventral skin defect were closed using $\frac{5}{0}$ vicryl sutures. A standard midline closure was applied to the skin. These procedures were the same for all patients and performed by the same specialist surgeon. The patients were kept in the hospital for wound care until their catheters were removed and their regular dressings were applied. Catheters were removed on postoperative days five to seven. The patients were followed up on postoperative months one, three, and six.
The patients with and without UCF complications composed groups I and II, respectively. Blood samples were collected during anaesthesia preparation, and neutrophil, platelet and lymphocyte counts were analysed from the pre-operative hemogram results. NLR was calculated by dividing the neutrophil count by the lymphocyte count and PLR was calculated by dividing the platelet count by the lymphocyte count. SII was calculated by multiplying the neutrophil count by the platelet count and dividing the result by the lymphocyte count. Lymphocyte, neutrophil, and platelet counts, as well as NLR, PLR and SII were compared between the two groups.
Statistical analysis Statistical Package for Social Sciences (SPSS) Version 28.0 (IBM Corp., Armonk, NY, USA) was used for data analysis. Mean, standard deviation, median (min-max), frequency, and ratio values were used in the descriptive statistical analysis of the data. Conformity to normal distribution was checked with the Kolmogorov-Smirnov test. The independent sample t-test and Mann-Whitney U test were used to analyse quantitative independent data. The chi-square test was used to analyse qualitative independent data, and the Fischer test was used when the chi-square test conditions were not met. The effect size and cut-off value were investigated using the receiver operating characteristic (ROC) curve.
## Results
A total of 78 patients were included in the study. The mean age of the patients was 74.9 ± 42.8 months. Of the patients, 11 developed UCF during the six-month follow-up (Table 1).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Min - Max | Min - Max.1 | Min - Max.2 | Mean ± SD/n% | Mean ± SD/n%.1 | Mean ± SD/n%.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Age (months) | Age (months) | 14.0 | - | 151.0 | 74.9 | ± | 42.8 |
| Catheter size | 6 fr | | | | 14.0 | | 17.9% |
| Catheter size | 8 fr | | | | 64.0 | | 82.1% |
| Duration of operation (min) | Duration of operation (min) | 45.0 | - | 130.0 | 82.2 | ± | 18.0 |
| Neutrophil (×103 /µL) | Neutrophil (×103 /µL) | 1.4 | - | 5.5 | 3.3 | ± | 1.1 |
| Lymphocyte (×103 /µL) | Lymphocyte (×103 /µL) | 1.7 | - | 9.3 | 4.0 | ± | 1.8 |
| Platelet (×103/µL) | Platelet (×103/µL) | 179.0 | - | 894.0 | 361.6 | ± | 99.0 |
| NLR | NLR | 0.4 | - | 60.7 | 1.7 | ± | 6.8 |
| PLR | PLR | 38.6 | - | 253.6 | 106.8 | ± | 54.3 |
| SII | SII | 112.0 | - | 1067.6 | 352.7 | ± | 230.4 |
| Urethrocutaneous fistula | No | | | | 67.0 | | 85.9% |
| Urethrocutaneous fistula | Yes | | | | 11.0 | | 14.1% |
None of the patients in our study needed plication. Catheter diameter, duration of operation, neutrophil count, NLR and SII values were similar between the groups ($p \leq 0.05$). Patients with UCF had statistically significantly higher lymphocyte and platelet counts than patients without UCF ($p \leq 0.05$). PLR was significantly lower in patients with UCF ($p \leq 0.05$) (Table 2).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Urethrocutaneous fistula (−) | Urethrocutaneous fistula (−).1 | Urethrocutaneous fistula (−).2 | Urethrocutaneous fistula (+) | Urethrocutaneous fistula (+).1 | Urethrocutaneous fistula (+).2 | p | p.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Mean ± SD/n% | Mean ± SD/n% | Mean ± SD/n% | Mean ± SD/n% | Mean ± SD/n% | Mean ± SD/n% | p | p |
| Age (months) | Age (months) | 70.1 | ± | 42.6 | 104.3 | ± | 31.9 | 0.009 | m |
| Hypospadias level | Midpenile | 25 | | 37.30% | 9 | | 81.80% | 0.006 | X² |
| Hypospadias level | Subcoronal | 42 | | 62.70% | 2 | | 18.20% | 0.006 | X² |
| Catheter size | 6 fr | 14 | | 20.90% | 0 | | 0.00% | 0.198 | X² |
| Catheter size | 8 fr | 53 | | 79.10% | 11 | | 100.00% | 0.198 | X² |
| Duration of operation (min) | Duration of operation (min) | 82.3 | ± | 18.6 | 80 | ± | 8.9 | 0.72 | m |
| Neutrophil (×103 /µL) | Neutrophil (×103 /µL) | 3.3 | ± | 1.1 | 3.5 | ± | 0.7 | 0.568 | t |
| Lymphocyte (×103 /µL) | Lymphocyte (×103 /µL) | 3.8 | ± | 1.6 | 6.8 | ± | 1 | 0 | m |
| Platelet (×103/µL) | Platelet (×103/µL) | 356 | ± | 100.7 | 429.2 | ± | 31.7 | 0.012 | m |
| Hemoglobin (g/dl) | Hemoglobin (g/dl) | 12.45 | ± | 3.25 | 14.15 | ± | 0.63 | 0.508 | m |
| NLR | NLR | 1.01 | ± | 0.6 | 10.54 | ± | 24.57 | 0.069 | m |
| PLR | PLR | 110.4 | ± | 55 | 63.6 | ± | 6.5 | 0.002 | m |
| SII | SII | 363.6 | ± | 236.5 | 221.7 | ± | 30.1 | 0.177 | m |
The UCF group had a significantly high lymphocyte number [area under the curve (AUC), 0.940 (0.886-0.993)]. The platelet counts successfully recognized patients with UCF [AUC, 0.810 (0.718-0.902)]. The PLR also had a significant impact on distinguishing between patients with and without UCF [AUC, 0.882 (0.800-0.964)] (Table 3). A PLR cut-off value of 75 had a sensitivity of $100\%$, a positive predictive value of $25\%$, a specificity of $75\%$, and a negative predictive value of $100\%$ for the differentiation of patients with and without UCF (Figure 1).
## Discussion
Complications after surgeries may occur regardless of the surgeon’s experience. Many surgical techniques have been developed to reduce complications and increase success rates. One of the most common complications of hypospadias surgery is UCF. The reported factors affecting UCF incidence include age, type of hypospadias, the tension in the suture lines, poor approximation of the neourethra, failure of the urethral catheter and infections [6]. However, UCF can develop for no particular reason. This has led researchers to investigate other factors. In recent years, inflammatory markers have played an important role in predicting the prognosis of malignancy, cardiovascular diseases, scar development in vesicoureteral reflux and rheumatic diseases [7,8]. In the present study, we focused on whether pre-operative haematological parameters affected UCF development.
Wound healing consists of hemostasis, inflammation, proliferation, maturation and remodelling stages. During the inflammatory stage, pathogens and foreign substances are removed from the injury, and the damage is brought under control. This phase begins immediately after the platelets achieve hemostasis. Vasodilation occurs, increasing vascular permeability and allowing neutrophils and monocytes to settle at the wound site. Cytokines and monocytes transform into macrophages in this phase [9]. In the proliferative phase, fibroblasts and collagen are formed. Angiogenesis occurs and nourishes the wound site. This is followed by a remodelling or maturation phase where the collagen type returns to normal [10]. These events show that neutrophils, lymphocytes and platelets play an active role in the wound healing. Following hypospadias surgery, an inflammatory response may lead to inadequate wound healing and UCF formation. Although nearly the same surgical methods are used in every patient and patients have the same risk factors, some patients develop UCF while others do not. It is also not always possible to pre-operatively predict which patients may develop a fistula. According to the hypothesis proposed in the present study, haematological parameters that play an active role in wound healing may also play an active role in fistula development after hypospadias surgery.
Coelho et al. examined pre-operative hemogram values in patients with acute extremity ischaemia and observed an increase in amputation and mortality in patients with high NLR values [11]. In a similar study, Vatankhah et al. reported better wound healing in diabetic wound ulcers in patients with low NLR [12]. Another study reported that the probability of spontaneous fistula closure increased as the NLR value decreased in patients who developed pancreatic fistula after distal pancreatectomy [13]. Topaktas et al. investigated the effect of NLR in predicting the recurrence of urethral stenosis after urethroplasty and reported that NLR was ineffective in predicting the recurrence of urethral stenosis [14]. In the present study, no difference was found in the NLR values of patients who developed UCF.
SII is a new inflammatory marker calculated from NLR and platelet count and used in cancer diagnosis and prognosis [15]. SII is proposed to be a better biomarker than NLR [16]. It has been widely used to reflect the immune status and determine some diseases prognosis and risk classification [17,18]. In the present study, although the SII value was lower in those with UCF, the difference was not statistically significant.
Lymphocytes and platelets are produced from the same haematopoietic stem cells. PLR should remain constant for hemostasis [19]. In the case of abnormal hematopoiesis, platelet count decreases more rapidly than lymphocyte count because the lifespan of the platelet is shorter, resulting in a decrease in PLR. Conditions that affect platelet production or lifespan can also affect wound healing. Therefore, PLR is critical in wound healing [20]. Maruyama et al. reported that surgical wound healing was poor when PLR values were lower [4]. Similarly, the present study found that patients with low PLR values developed UCF at a higher rate. In the present study, the probability of developing UCF increased as the lymphocyte and platelet counts increased, and PLR decreased. A PLR cut-off value of 75 had a sensitivity, specificity, positive predictive value and negative predictive value of $100\%$, $75\%$, $25\%$ and $100\%$, respectively. This suggests that inflammatory cells operate in a balance and that a disruption in this balance impairs wound healing and leads to UCF.
One of the common complications requiring reoperation is UCF. These reoperations not only create a financial burden, but also cause anesthesia-related complications and long-term psychosexual problems [21]. For this reason, many treatment methods are tried to prevent the development of UCF. The use of intraoperative tissue adhesives has been shown to reduce the risk of UCF [22,23]. In another study, it was reported that additional coating of the neo-urethra with a double layer of dartos significantly reduced the rate of fistula after hypospadias [24]. On the other hand, there are studies reporting that the use of inlay grafts reduces the development of UCF and the risk of meatal/neourethral stenosis [25,26].
If the data in our study is supported by other studies and similar findings are reached, pre-op hematological parameters can be added to the risk factors for UCF. In patients with a high risk of developing UCF, additional measures such as tissue adhesives, inlay grafts, and double-layer grafts can be taken.
This study has some limitations. Due to the nature of the retrospective study, the collected data and the number of patients were limited.
Other complications of hypospadias such as wound infection, meatal stenosis, glanular dehisence, penile chord may also be associated with hematological parameters. These complications were not evaluated in this study due to the small number of patients and the lack of long-term follow-up.
Various parameters such as chordee grade, urethral plaque status and glans depth are effective in the formation of UCF, but these were not evaluated in our study.
The result only shows an overview of the association between hematological alteration and UCF, and we are unaware of any causal relationship. It is known that the complication rates of hypospadias surgery decrease as experience and the number of cases increase. In the present study, patients at the beginning and end of the case series were not compared among themselves.
## Conclusions
A simple blood count can help in determining the risk of UCF complications. Platelet and lymphocyte counts and PLR are practical, easily accessible, low-cost parameters already checked as part of the routine workup. In addition to the other UCF risk factors, checking these haematological parameters can give us a better idea of UCF development. In patients with a high risk of developing UCF, additional measures such as tissue adhesives, inlay grafts, and double-layer grafts can be taken. Further prospective studies involving multiple centres and large patient series examining long-term results are needed to validate these results.
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25. Omran M, Sakr A, Elgalaly H, Fawzy A, Abdalla M. **Narrow urethral plate augmentation in anterior and middle hypospadias repair: onlay flap vs. inlay graft. A prospective randomized comparative study**. *J Pediatr Urol* (2021) **17** 216-218
26. Silay MS, 't Hoen L, Bhatt N. **Are there any benefits of using an inlay graft in the treatment of primary hypospadias in children? A systematic review and metanalysis**. *J Pediatr Urol* (2021) **17** 303-315. PMID: 33691984
|
---
title: Diabetes and infectious disease mortality in Mexico City
authors:
- Fiona Bragg
- Pablo Kuri-Morales
- Jaime Berumen
- Adrián Garcilazo-Ávila
- Carlos Gonzáles-Carballo
- Raúl Ramírez-Reyes
- Rogelio Santacruz-Benitez
- Diego Aguilar-Ramirez
- Louisa Gnatiuc Friedrichs
- William G Herrington
- Michael Hill
- Eirini Trichia
- Rachel Wade
- Rory Collins
- Richard Peto
- Jonathan R Emberson
- Jesus Alegre-Diaz
- Roberto Tapia-Conyer
journal: BMJ Open Diabetes Research & Care
year: 2023
pmcid: PMC10008442
doi: 10.1136/bmjdrc-2022-003199
license: CC BY 4.0
---
# Diabetes and infectious disease mortality in Mexico City
## Abstract
### Introduction
Although higher risks of infectious diseases among individuals with diabetes have long been recognized, the magnitude of these risks is poorly described, particularly in lower income settings. This study sought to assess the risk of death from infection associated with diabetes in Mexico.
### Research design and methods
Between 1998 and 2004, a total of 159 755 adults ≥35 years were recruited from Mexico City and followed up until January 2021 for cause-specific mortality. Cox regression yielded adjusted rate ratios (RR) for death due to infection associated with previously diagnosed and undiagnosed (HbA1c ≥$6.5\%$) diabetes and, among participants with previously diagnosed diabetes, with duration of diabetes and with HbA1c.
### Results
Among 130 997 participants aged 35–74 and without other prior chronic diseases at recruitment, $12.3\%$ had previously diagnosed diabetes, with a mean (SD) HbA1c of $9.1\%$ ($2.5\%$), and $4.9\%$ had undiagnosed diabetes. During 2.1 million person-years of follow-up, 2030 deaths due to infectious causes were recorded at ages 35–74. Previously diagnosed diabetes was associated with an RR for death from infection of 4.48 ($95\%$ CI 4.05–4.95), compared with participants without diabetes, with notably strong associations with death from urinary tract (9.68 (7.07–13.3)) and skin, bone and connective tissue (9.19 (5.92–14.3)) infections and septicemia (8.37 (5.97–11.7)). In those with previously diagnosed diabetes, longer diabetes duration (1.03 (1.02–1.05) per 1 year) and higher HbA1c (1.12 (1.08–1.15) per $1.0\%$) were independently associated with higher risk of death due to infection. Even among participants with undiagnosed diabetes, the risk of death due to infection was nearly treble the risk of those without diabetes (2.69 (2.31–3.13)).
### Conclusions
In this study of Mexican adults, diabetes was common, frequently poorly controlled, and associated with much higher risks of death due to infection than observed previously, accounting for approximately one-third of all premature mortality due to infection.
## Introduction
Despite the classic dichotomy of communicable and non-communicable diseases, clear inter-relationships exist between them, as demonstrated by the COVID-19 pandemic.1 Greater susceptibility to infections has long been recognized in diabetes. However, infection has recently been described as an ‘emerging’ complication of the condition,2 and has received comparably little attention in clinical guidelines and practice, with prevention efforts consisting of, at best, vaccination (eg, influenza and pneumococcal vaccinations3) and foot care.4 The limited focus on infectious complications of diabetes likely in part reflects the availability of only limited, and frequently inconsistent, population-based evidence on the relevance of diabetes for infectious disease risks. Studies in high-income countries suggest an approximate doubling of the risk of infection-related death in diabetes.5–8 However, findings from lower income settings are more varied. Risks among East Asian populations appear similar to those in high-income countries,9 10 while previous findings from Latin America suggest up to sixfold higher risks of death from infectious causes in diabetes.11 12 These differences may reflect differences in the typical characteristics of diabetes; for example, in levels of glycemic control, which may drive the risk of infectious diseases.13 However, inconsistent findings from frequently underpowered observational studies with inadequate control for confounding limit our understanding of this,13 with scarce evidence available from trials.14 The relevance of diabetes-related comorbidities is similarly unclear; although early studies suggested macrovascular complications mediate the association of diabetes with mortality from infectious causes,15 subsequent studies have varied in their assessment of the relevance of vascular diseases.16 17 Furthermore, few studies have examined the full range of infectious diseases, with many, particularly in lower income settings, focusing only on one or a subset of infections. Thus, considerable uncertainty persists regarding the relevance of diabetes for infectious disease mortality and factors influencing this, particularly in low and middle-income country settings.
Using data from the Mexico City Prospective Study of approximately 150 000 adults, we report on the associations of diabetes with risk of death from infectious causes, exploring how duration of diabetes diagnosis and glycemic control influence these risks.
## Study population
Details of the Mexico City Prospective Study design, methods and population have been reported previously.18 Briefly, between 1998 and 2004, households within two urban districts of Mexico City (Coyoacán and Iztapalapa) were visited and all residents aged 35 years or older were invited to participate.
## Data collection
Trained nurses administered electronic questionnaires in participants’ houses, collecting information on sociodemographic status, lifestyle factors (eg, smoking and alcohol consumption) and personal medical history, including current medication. Physical measurements were undertaken using calibrated instruments, including height, weight, hip and waist circumferences and sitting blood pressure. A 10 mL non-fasting venous blood sample was collected into an EDTA vacutainer and separated into two plasma and one buffy coat aliquots for long-term storage at −150°C. HbA1c levels were measured in buffy coat samples using a validated high-performance liquid chromatography method11 on HA-8180 analyzers with calibrators traceable to International Federation of Clinical Chemistry standards.19
## Assessment of glycemic status
Participants who reported at recruitment to have been previously diagnosed with diabetes by a doctor, or who reported taking one or more medications for diabetes were defined as having previously diagnosed diabetes. These participants provided information on their approximate date of diagnosis. Among those without previously diagnosed diabetes, undiagnosed diabetes was defined as an HbA1c level of $6.5\%$ (equivalent to 48 mmol/mol) or higher, and pre-diabetes was defined as an HbA1c level between $6.0\%$ and $6.4\%$ (equivalent to 42–47 mmol/mol).20 Normoglycemia was defined as HbA1c <$6.0\%$. Participants with diabetes diagnosed before 35 years and taking insulin at recruitment were considered to have likely type 1 diabetes.
## Follow-up for mortality
Information on cause of death is obtained through probabilistic linkage (based on name, age and sex) to the Mexican electronic death registry. All diseases recorded on death certificates are coded using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Deaths are reviewed by study clinicians who, where necessary, recode the underlying cause of death.11 Participant deaths for the present study were tracked until 1 January 2021. The primary endpoints in this paper are deaths for which any infection was recorded as the underlying cause, and six main subcategories of respiratory, urinary tract, gastrointestinal, and skin, bone and connective tissue infections, septicemia, and other infections (online supplemental table S1).
## Statistical analysis
Analyses excluded participants aged 85 years or older, with previously diagnosed chronic diseases other than diabetes (ischemic heart disease, stroke, chronic kidney disease, cirrhosis, cancer, emphysema) or with likely type 1 diabetes. Those with missing or extreme exposure or covariate (see below) data, or who had an uncertain cause of death were also excluded.
Cox proportional hazards models, with time since entry into the study as the underlying timescale, were used to determine the relevance of previously diagnosed and undiagnosed diabetes and, among participants with previously diagnosed diabetes, of diabetes duration (<5, 5 to <10, or ≥10 years) and glycemic control (HbA1c <$9.0\%$, $9.0\%$ to <$11.0\%$, or ≥$11.0\%$) for infectious disease mortality, through estimation of cause-specific mortality rate ratios (RRs, estimated by the Cox hazard ratios). Among individuals with previously diagnosed diabetes, diabetes duration and HbA1c were subsequently examined as continuous variables once it was confirmed that there was no evidence against these associations being ‘log-linear’.21 Mortality RRs were stratified by age at risk (5-year groups) and sex, and adjusted for district (Coyoacán and Iztapalapa), educational level (university or college, high school, elementary school, other), smoking status (never, former, occasional, <10 cigarettes/day, ≥10 cigarettes/day), alcohol drinking (never, former, current), height (four equal groups), weight (four equal groups), waist circumference (four equal groups) and hip circumference (four equal groups). Analyses examining the relevance of diabetes duration were additionally standardized for glycemic control, and those examining glycemic control for diabetes duration (to the average levels of those with previously diagnosed diabetes). Group-specific variances were estimated (reflecting the amount of data in each glycemic status category), such that the RR for each category, including the reference category, is associated with a group-specific ('floated') $95\%$ CI, enabling comparisons between any two categories and not only with the reference group.22 Participants who did not die due to the infectious disease of interest were censored at the earliest of death from any other cause, the end of the age-at-risk period of interest, or 31 December 2020. The main analyses examined premature mortality (ie, deaths before 75 years), but the relevance of previously diagnosed diabetes for infectious disease mortality was also examined at 75–84 years. Adjusted RRs were compared across strata of other covariates (sex, region, education, smoking, alcohol drinking and body mass index (BMI) quartiles). Additional analyses explored the relevance of pre-diabetes (HbA1c $6.0\%$ to <$6.5\%$) and of HbA1c levels within the normoglycemic range (<$6.0\%$) for infectious disease mortality. Sensitivity analyses included participants with previously diagnosed chronic diseases other than diabetes at recruitment.
Assuming a causal relationship, the proportions of infectious disease deaths attributable to undiagnosed diabetes, previously diagnosed diabetes with HbA1c <$9.0\%$, previously diagnosed diabetes with HbA1c ≥$9.0\%$, and total diabetes (diagnosed and undiagnosed combined) were calculated for each group by (RR-1)/RR, where RR is the adjusted RR for infectious disease death for each group relative to participants without diabetes.
All analyses used SAS V.9.4. Figures were produced using R V.4.1.3.
## Participant characteristics
Of the 159 755 participants recruited, 20 379 were excluded from the present analyses. These comprised 2959 participants aged ≥85 years at recruitment, a further 7800 with prior chronic diseases other than diabetes, 229 with likely type 1 diabetes, 1869 with uncertain mortality linkage, and 7317 with missing or outlying data, and a further 205 participants who were recruited twice (data from the first visit at which a blood sample was collected were used for these participants). Among the remaining 139 376 participants, 130 997 were aged 35–74 at recruitment, and 8379 were aged 75–84 (online supplemental table S2).
Table 1 shows the baseline characteristics of the 130 997 participants aged 35–74 years at recruitment. Their mean (SD) age was 51 [10] years, $32\%$ were men, and their mean BMI was 29.1 (4.8) kg/m2. Overall, $17.2\%$ of participants had diabetes, including $12.3\%$ ($$n = 16$$ 112) with previously diagnosed diabetes and $4.9\%$ ($$n = 6381$$) with undiagnosed diabetes. A further $5.3\%$ ($$n = 7008$$) of participants had an HbA1c level in the pre-diabetes range. Participants with diabetes at recruitment were older, less highly educated and less likely to be current smokers or alcohol drinkers than those without diabetes. The prevalence of previously diagnosed diabetes increased markedly with age from about $2\%$ at 35–39 to over $25\%$ at 70–74 (online supplemental figure S1). Diagnosis was, on average, 9 years prior to recruitment. Longer time since diagnosis was associated with a lower frequency of current smoking, and lower BMI and waist circumference, as well as with younger age at diagnosis. Most participants with previously diagnosed diabetes ($79\%$) reported taking glucose-lowering medication, most commonly sulfonylureas ($69\%$). However, the mean (SD) baseline HbA1c among participants with previously diagnosed diabetes was $9.1\%$ ($2.5\%$), and was higher among participants with a longer duration of diabetes, despite reportedly more frequent use of glucose-lowering medication.
**Table 1**
| Unnamed: 0 | No diabetes | Diabetes | Diabetes.1 | Previously diagnosed diabetes by duration (years) | Previously diagnosed diabetes by duration (years).1 | Previously diagnosed diabetes by duration (years).2 | Previously diagnosed diabetes by HbA1c (%) | Previously diagnosed diabetes by HbA1c (%).1 | Previously diagnosed diabetes by HbA1c (%).2 | Overall |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | No diabetes | Undiagnosed | Previously diagnosed | <5 | 5 to <10 | ≥10 | <9 | 9 to <11 | ≥11 | Overall |
| Participants (n) | 108 504 | 6381 | 16 112 | 4151 | 7809 | 4152 | 8021 | 4120 | 3971 | 130 997 |
| Age, sex and socioeconomic factors | | | | | | | | | | |
| Age (years) | 49 (10) | 54 (10) | 58 (10) | 55 (10) | 57 (9) | 62 (8) | 59 (10) | 57 (9) | 55 (9) | 51 (10) |
| Men, % | 32 | 34 | 33 | 32 | 33 | 33 | 33 | 33 | 30 | 32 |
| Resident of Coyoacán, % | 41 | 33 | 34 | 27 | 35 | 41 | 35 | 34 | 33 | 40 |
| Resident of Iztapalapa, % | 59 | 67 | 66 | 73 | 65 | 59 | 65 | 66 | 67 | 60 |
| University/college educated, % | 18 | 10 | 8 | 8 | 9 | 6 | 8 | 8 | 6 | 16 |
| Lifestyle factors, % | | | | | | | | | | |
| Current smoker | 30 | 27 | 24 | 27 | 25 | 19 | 22 | 25 | 25 | 29 |
| Current alcohol drinker | 70 | 66 | 59 | 60 | 61 | 53 | 56 | 60 | 62 | 68 |
| Physical activity 1+ times/week | 23 | 16 | 21 | 18 | 21 | 22 | 23 | 20 | 17 | 22 |
| Anthropometry | | | | | | | | | | |
| Height (cm) | 156 (9) | 155 (9) | 154 (9) | 155 (9) | 155 (9) | 154 (9) | 155 (9) | 155 (9) | 154 (9) | 156 (9) |
| Weight (kg) | 71 (13) | 76 (14) | 70 (13) | 73 (14) | 70 (13) | 66 (12) | 71 (13) | 70 (70) | 67 (13) | 71 (13) |
| BMI (kg/m2) | 29.0 (4.7) | 31.5 (5.2) | 29.1 (5.0) | 30.4 (5.2) | 29.3 (4.9) | 27.7 (4.6) | 29.8 (4.9) | 29.0 (4.9) | 28.0 (5.1) | 29.1 (4.8) |
| Waist circumference (cm) | 93 (11) | 101 (11) | 97 (11) | 98 (11) | 97 (11) | 94 (11) | 98 (11) | 97 (11) | 94 (11) | 94 (11) |
| Waist-to-hip ratio | 0.89 (0.07) | 0.93 (0.07) | 0.92 (0.07) | 0.93 (0.07) | 0.92 (0.07) | 0.93 (0.07) | 0.93 (0.07) | 0.93 (0.07) | 0.92 (0.06) | 0.90 (0.07) |
| HbA1c (%) | 5.5 (0.4) | 8.6 (2.1) | 9.1 (2.5) | 8.5 (2.5) | 9.3 (2.4) | 9.4 (2.4) | 7.0 (1.1) | 10.0 (0.6) | 12.4 (1.2) | 6.1 (1.7) |
| Duration of diabetes diagnosis (years) | | | 9 (7) | 3 (1) | 8 (1) | 19 (5) | 9 (7) | 10 (7) | 10 (6) | |
| Age at diabetes diagnosis (years) | | | 48 (10) | 52 (10) | 49 (9) | 43 (9) | 50 (10) | 47 (10) | 46 (9) | |
| Glucose-lowering medication, % | | | | | | | | | | |
| Sulfonylurea | | | 69 | 63 | 71 | 71 | 65 | 74 | 72 | |
| Biguanide | | | 18 | 13 | 19 | 22 | 17 | 20 | 20 | |
| Insulin | | | 6 | 2 | 5 | 11 | 4 | 8 | 7 | |
| Other | | | 1 | 2 | 1 | 1 | 1 | 1 | 2 | |
| Any | | | 79 | 70 | 81 | 86 | 75 | 85 | 82 | |
## Diabetes and death from any infectious cause
During approximately 2.1 million person-years (median 18 years) of follow-up, 2030 deaths due to infectious causes occurred at ages 35–74 (online supplemental table S1). There were 692 deaths (350 per 100 000 person-years) among participants with previously diagnosed diabetes, 202 (216 per 100 000 person-years) among participants with undiagnosed diabetes, and 1136 (63 per 100 000 person-years) among those without diabetes. These included 1198 deaths from respiratory infection, 211 from urinary tract infection, 176 from septicemia, 201 from gastrointestinal infection, and 109 from skin, bone or connective tissue infection. Among all 139 376 participants aged 35–84 at recruitment, there were 1292 deaths (491 per 100 000 person-years) due to any infectious cause at ages 75–84. Figure 1A shows age-specific and sex-specific RRs for death from any infectious cause among participants with previously diagnosed diabetes versus those without diabetes at recruitment. RRs were stronger at younger ages: 7.00 ($95\%$ CI 5.44–8.99) at 35–59, 3.48 ($95\%$ CI 3.11–3.91) at 60–74, and 2.03 ($95\%$ CI 1.79–2.30) at 75–84. At all ages, RRs did not differ much between men and women.
**Figure 1:** *Relevance of previously diagnosed and undiagnosed diabetes to mortality from any infectious cause by age and sex (A), duration of diabetes (B), and glycemic control (C). (A) Mortality rate ratios (RRs) by age and sex for death from any infectious cause at ages 35–84 years for patients with previously diagnosed diabetes compared with those with no diabetes. Diamonds show values for men and women combined. The RRs for participants with undiagnosed diabetes compared with participants without diabetes were 4.21 (95% CI 3.10–5.72) at 35–59 years, 2.11 (95% CI 1.75–2.54) at 60–74 years, and 1.43 (95% CI 1.16–1.76) at 75–84 years. (B) Mortality RRs for death from any infectious cause by duration of diabetes at ages 35–74 years. (C) Mortality RRs for death from any infectious cause by glycemic control at ages 35–74 years. RRs in all panels are stratified by age at risk and sex (as appropriate), and adjusted for district, educational level, smoking status, alcohol drinking, height, weight, waist circumference and hip circumference. In (B) and (C), the RR estimates for those with previously diagnosed diabetes are also adjusted, respectively, for any HbA1c or diabetes duration differences between the groups (to the average HbA1c or duration seen for all those with previously diagnosed diabetes) in such a way that their information-weighted average equals the overall RR estimate for all those with previously diagnosed diabetes versus those with no diabetes. The numbers above the squares are the RRs and the numbers below the squares are the number of deaths in that group. In all panels, the size of each square is proportional to the amount of statistical information. Horizontal lines represent 95% CIs.*
Previously diagnosed diabetes of less than 5 years’ duration was associated with an RR for death from any infectious cause at ages 35–74 years of 3.34 ($95\%$ CI 2.80–3.98), while participants with 5–10 years and ≥10 years’ duration had RRs of 4.45 ($95\%$ CI 3.93–5.05) and 6.87 ($95\%$ CI 5.86–8.06), respectively (figure 1B). Among participants with previously diagnosed diabetes, each additional year of diagnosed diabetes was associated with $3\%$ (RR 1.03 ($95\%$ CI 1.02–1.05)) higher risk of death. Compared with participants without diabetes, the RR for death from any infectious cause at ages 35–74 years among those with undiagnosed diabetes was 2.69 ($95\%$ CI 2.31–3.13). Higher baseline HbA1c among participants with previously diagnosed diabetes was associated with higher infection-related death RRs (figure 1C). Compared with participants without diabetes, those with previously diagnosed diabetes with a baseline HbA1c <$9.0\%$ had an RR for death from any infectious cause at ages 35–74 years of 3.53 ($95\%$ CI 3.06–4.07), for those with a baseline HbA1c of $9.0\%$ to <$11.0\%$ the death RR was 4.52 ($95\%$ CI 3.86–5.30), and for those with a baseline HbA1c of ≥$11.0\%$ it was 6.40 ($95\%$ CI 5.56–7.38). In those with previously diagnosed diabetes, each $1.0\%$ higher baseline HbA1c was associated with $12\%$ (RR 1.12 ($95\%$ CI 1.08–1.15)) higher risk of death from any infectious cause. For deaths at ages 35–74 years, previously diagnosed diabetes was more strongly associated with death due to any infectious cause among those with a lower BMI level (who had a higher HbA1c level), but RRs otherwise differed little by baseline characteristics (online supplemental figure S2).
## Diabetes and death from specific infectious causes
Figure 2 presents RRs for death at ages 35–74 years from specific infectious causes among participants with previously diagnosed diabetes compared with those without diabetes. There were notably strong associations with death from urinary tract infection (RR 9.68 ($95\%$ CI 7.07–13.3)), skin, bone and connective tissue infection (9.19 ($95\%$ CI 5.92–14.3)) and septicemia (8.37 ($95\%$ CI 5.97–11.7)). More modest but still substantial associations were observed for death from gastrointestinal (RR 3.92 ($95\%$ CI 2.86–5.39)) and respiratory (3.65 ($95\%$ CI 3.19–4.18)) infections. Deaths from respiratory infections largely comprised deaths from pneumonia ($55\%$) and COVID-19 ($36\%$), for which the mortality RRs were 5.32 ($95\%$ CI 4.47–6.35) and 1.76 ($95\%$ CI 1.30–2.37), respectively (4.86 ($95\%$ CI 2.87–8.23) and 1.69 ($95\%$ CI 1.25–2.27), respectively, when follow-up was limited to the period from 1 January to 31 December 2020). RRs for death from specific infectious diseases associated with previously diagnosed diabetes were similar in men and women (online supplemental figure S3), but were stronger at younger than at older ages (online supplemental figure S4). Undiagnosed diabetes was associated with more modest RRs for death from all specific infectious causes (figure 3). Analyses including participants with previously diagnosed chronic diseases at recruitment did not materially alter diabetes-associated RRs (data not shown).
**Figure 2:** *Relevance of previously diagnosed diabetes to mortality from infectious causes at ages 35–74 years. Mortality rate ratios (RRs) for deaths due to infectious causes at ages 35–74 years for patients with previously diagnosed diabetes versus those with no diabetes. RRs are stratified by age at risk and sex, and adjusted for district, educational level, smoking status, alcohol drinking, height, weight, waist circumference and hip circumference. The RR for death due to pneumonia was 5.32 (95% CI 4.47–6.35), and for death due to COVID-19 was 1.76 (95% CI 1.30–2.37). The RRs for participants with undiagnosed diabetes compared with participants without diabetes were 5.67 (95% CI 3.63–8.85) for urinary tract infection deaths, 5.43 (95% CI 2.98–9.90) for skin, bone and connective tissue infection deaths, 4.26 (95% CI 2.53–7.17) for septicemia deaths, 2.36 (95% CI 1.45–3.83) for gastrointestinal infection deaths, 2.35 (95% CI 1.92–2.87) for respiratory infection deaths, 0.99 (95% CI 0.43–2.28) for deaths due to other infections, and 2.69 (95% CI 2.31–3.13) for all infectious disease deaths. The size of each square is proportional to the amount of data available, and the unshaded diamond represents the values for mortality from any infectious cause. Horizontal lines represent 95% CIs.* **Figure 3:** *Relevance of previously diagnosed and undiagnosed diabetes to mortality from infectious causes at ages 35–74 years by glycemic control. Rate ratios (RR) are stratified by age at risk and sex, and adjusted for district, educational level, smoking status, alcohol drinking, height, weight, waist circumference and hip circumference. Analyses are additionally adjusted for duration of diabetes diagnosis among participants with previously diagnosed diabetes. Unfilled squares represent no diabetes. Gray squares represent undiagnosed diabetes. Black squares represent previously diagnosed diabetes. The numbers above the squares are the RRs and the numbers below the squares are the number of deaths in that group. The size of each square is proportional to the amount of data available. The error bars represent 95% CIs.*
Diabetes diagnosis duration was strongly positively associated with death from skin, bone and connective tissue infection and with death from septicemia (online supplemental figure S5). Participants with diagnosed diabetes of ≥10 years’ duration had an almost fourfold higher death rate due to skin, bone and connective tissue infection than participants with diagnosed diabetes of <5 years’ duration (RR 20.4 (floated $95\%$ CI 12.9–32.4) vs 5.45 (floated $95\%$ CI 2.83–10.5)). For death from septicemia, the RR (floated $95\%$ CI) was approximately threefold higher after ≥10 years of diagnosed diabetes (RR 15.0 (10.4-21.8)) compared with <5 years (RR 5.38 (3.24–8.94)). By contrast, the increase in RR with longer diabetes durations was more modest for death from urinary tract infection (RR 15.2 (10.5–21.8) for diagnosed diabetes of ≥10 years vs 7.09 (4.57–11.0) for diagnosed diabetes <5 years’ duration) and with death from respiratory infection (5.28 (4.27–6.54) and 2.88 (2.32–3.58), respectively). Diagnosed diabetes duration was not clearly associated with risk of death from gastrointestinal infection.
Glycemic control among participants with previously diagnosed diabetes was strongly associated with death from skin, bone and connective tissue infection, with an almost fourfold higher rate of death among participants with baseline HbA1c ≥$11.0\%$ than among participants with baseline HbA1c <$9.0\%$, reflecting death RRs of 18.1 (floated $95\%$ CI 12.3–26.7) and 4.58 (floated $95\%$ CI 2.59–8.10), respectively (figure 3). Moderately weaker (though still substantial) increases in the RR with higher HbA1c were observed for death from gastrointestinal infection (HbA1c <$9.0\%$: RR 2.55 (1.68–3.89); HbA1c ≥$11.0\%$: RR 6.89 (4.76–9.97)) and from urinary tract infection (HbA1c <$9.0\%$: RR 6.33 (4.43–9.04); HbA1c ≥$11.0\%$: RR 16.7 (12.5–22.3)). There were apparently positive, but more modest, associations between baseline HbA1c and death from septicemia and respiratory infection.
Assuming a causal relationship, approximately one-third of all infection deaths at ages 35–74 years were due to diabetes, including $18\%$ attributable to uncontrolled previously diagnosed diabetes (HbA1c ≥$9.0\%$), $9\%$ to controlled previously diagnosed diabetes (HbA1c <$9.0\%$) and $6\%$ to undiagnosed diabetes (online supplemental table S3). This included more than half of deaths due to urinary tract infection, skin, bone and connective tissue infection and septicemia, with the greatest proportions accounted for by uncontrolled diagnosed diabetes.
## Non-diabetic glycemia and death from infectious causes
Pre-diabetes was associated with a modestly elevated mortality RR (1.38 ($95\%$ CI 1.18–1.58)) for death from any infectious cause when compared with baseline HbA1c levels <$6.0\%$. However, there was no apparent association of HbA1c levels with risk of infectious disease mortality below this threshold (online supplemental figure S6). Higher HbA1c levels among participants without diabetes were associated with higher risks of death from septicemia, with RRs of 1.51 ($95\%$ CI 0.78–2.92), 1.65 ($95\%$ CI 0.78–3.47) and 2.31 ($95\%$ CI 0.94–5.66) at baseline HbA1c levels of $5.4\%$ to <$5.7\%$, $5.7\%$ to <$6.0\%$ and $6.0\%$ to <$6.5\%$, respectively, compared with HbA1c <$5.4\%$ (online supplemental figure S7). A more modest positive association was observed with risk of death from respiratory infection (RRs of 1.11, 1.18 and 1.21, respectively). HbA1c levels among participants without diabetes were not related to mortality from other specific infectious causes studied.
## Conclusions
In this large prospective cohort of adults from Mexico City, previously diagnosed diabetes was associated with greater than fourfold higher risk of premature death from any infectious cause between 35 and 74 years of age. Death rates in diabetes were highest for urinary tract infection, skin, bone and connective tissue infection and septicemia, with up to 10-fold higher risks than participants without diabetes. Moreover, infectious disease mortality risks were higher among those with longer duration of diabetes or with higher baseline HbA1c levels. In this population with a high prevalence of frequently poorly controlled diabetes, the condition accounted for approximately one-third of all premature deaths due to infectious diseases.
The diabetes-associated risks of mortality due to infectious diseases observed in the present study are generally more extreme than those reported previously. Although infection-related mortality rates among patients with type 2 diabetes recruited from an outpatient clinic in Brazil were found to be six times higher than in the general adult population, this was based on a very small study population ($$n = 471$$) and residual confounding may have explained some of the excess risk, since rates were standardized only for age and sex.12 In contrast, large-scale studies comprising populations from predominantly high-income countries have typically reported a more modest doubling of the risk of death due to infectious causes.5 7 15 23 For example, in the Emerging Risk Factors Collaboration’s individual participant data meta-analysis of 40 000 participants with previously or newly diagnosed diabetes and 675 000 participants without diabetes followed for an average of 14 years, diabetes was associated with a 2.4-fold higher risk of death due to all infections excluding pneumonia ($$n = 1081$$) and a 1.7-fold higher risk of death due to pneumonia ($$n = 2893$$).5 Differences between studies in the definitions of diabetes used may have contributed to differences in reported risks, as illustrated by the lower risks in undiagnosed, than diagnosed, diabetes in the present study. However, these would not explain the magnitude of differences observed, which could also reflect the influence of glycemic control or differing age distributions of study populations. Moreover, disparities between countries in access to healthcare interventions (eg, newer generation antibiotics, intensive care) with potential to differentially impact on population subgroups with greater susceptibility to severe infections, including individuals with diabetes,13 may have contributed to the comparably high diabetes-associated infectious disease mortality risks in this Mexican study population.
There is limited evidence available on the relevance of glycemic control for infectious disease mortality.13 24 Studies examining infectious disease outcomes more generally (ie, both fatal and non-fatal infections) have shown mixed findings, but many have found J-shaped or U-shaped associations of HbA1c levels with risk of both infectious disease incidence and mortality.10 24–26 For example, apparent J-shaped associations were observed between HbA1c levels and risks of both hospitalization for, and death from, infection among 85 000 patients with diabetes recorded in English primary care data, with lowest risks observed at levels of $6\%$–$7\%$.24 In contrast, we found a strong positive association of baseline HbA1c levels with risk of death due to infectious causes among participants with previously diagnosed diabetes, with no apparent threshold in the association. The absence of higher risks at the lowest HbA1c levels in the present study likely reflects exclusion of participants with prior chronic diseases (in contrast with the other studies described), reducing potential for reverse causality and residual confounding, while the comparably clear positive association at higher HbA1c levels may reflect relatively poor glycemic control (mean HbA1c was $9.1\%$ in the Mexico City study population, but $7.4\%$ and $8.3\%$ among participants with type 2 and type 1 diabetes, respectively, in the English study population24). Higher glucose concentrations may increase the risk of infectious diseases, as well as contributing to adverse outcomes following infection, through multiple mechanisms. These include impaired immune function, adversely impacting both humoral and cell-mediated immunity, and promotion of the growth of some microorganisms.27 However, trials of intensive glycemic control in diabetes have generally not investigated the impact on infections,14 and better evidence is needed to understand whether the association is causal. Evidence is also limited on the relevance of non-diabetic glycemia for infectious disease risk.28 Although we observed a higher risk of death due to any infectious cause among participants with pre-diabetes than among those with HbA1c levels in the truly ‘normoglycaemic’ range, this may simply reflect subsequent development of diabetes and associated infectious disease mortality risks in this group.
Duration of diagnosed diabetes showed a strong positive association with the risk of mortality due to infectious causes in the Mexico City population, independent of its association with glycemic control. This is consistent with the observed lower risks among individuals with undiagnosed, than with diagnosed, diabetes, despite similar mean HbA1c levels. Given the average 9-year duration of diabetes diagnosis, which is higher than in several previous studies,10 29 this may have contributed to the higher diabetes-associated risks of mortality due to infectious causes in this study population. Both longer diabetes duration11 and higher levels of glycemia30 31 are established risk factors for vascular complications of diabetes, which previous studies suggest may play a role in determining infectious disease risks and prognosis.15 16 In contrast, the associations presented herein differed little according to participants’ history of cardiovascular (ie, macrovascular) diseases. However, we were unable to account for macrovascular complications developed during follow-up in the study, or for microvascular complications. Few studies have simultaneously examined the association of diabetes with risks of death due to the full range of site-specific infections. However, this has been explored for infection-related outcomes more generally (including both primary care recorded diagnoses and hospitalizations),10 16 27 29 in many instances showing the highest diabetes-associated risks for the same, or closely related, infections as in the present study, although more modest than those observed in this Mexico City population.16 27 29 The notably strong associations of diabetes, glycemic control and diabetes duration with risk of death due to skin, bone and connective tissue infection may, at least in part, reflect the influence of chronic microvascular and macrovascular complications of diabetes, as well as the predominance of bacterial infections at these sites,29 and similar factors may explain the comparatively strong associations with death due to urinary tract infections and septicemia.
In the present study, we estimated that one-third of infectious disease deaths before age 75 could be attributed to diabetes. This clearly highlights the need for efforts to prevent infectious diseases among this Mexican population, particularly given the high prevalence of diabetes and poor glycemic control. Despite lower diabetes-associated relative mortality risks, respiratory infections accounted for the greatest proportion of infectious disease deaths among individuals with diabetes in the present study, highlighting the potential value of vaccination against respiratory pathogens in this population. Although pneumococcal and influenza vaccinations are recommended for all adults with diabetes in Mexico,32 uptake is reported to be low,33 but there is clear value in ensuring effective implementation of these existing vaccination guidelines.
Our study has certain limitations. First, diabetes may influence both the onset of infectious diseases and their course, and the focus on mortality prevented differentiation between these. However, mortality outcomes would be expected to be less susceptible to misclassification and to potential diagnostic biases, and readily permitted investigation of a wide spectrum of infectious diseases. Assessment of the relevance of glycemic control for infectious disease mortality risks was based on single HbA1c measurements, which may not reflect longer term trends. However, there is arguably clinical value in understanding the relevance of single measurements for future infectious disease mortality risks. More women than men were recruited into the study (because women were more likely to be at home when the fieldworkers’ visit was during standard working hours). However, the size of the study meant that large numbers of deaths were observed in both men and women, leading to reliable sex-specific estimates. The two study districts are also not representative of the overall Mexican population, or even the overall Mexico City population. However, prospective studies of non-representative cohorts of individuals can provide reliable evidence about the associations of risk factors with disease that are widely generalizable.34 35 Finally, the observational study design precludes assessment of the likely causality of the observed associations.
Diabetes is highly prevalent in Mexico and is associated with very high risks of infectious disease mortality, particularly among those with longer duration of diabetes diagnosis and poorer glycemic control. The findings presented clearly highlight the need for an increased focus on prevention of infectious diseases in the care of individuals with diabetes, including through effective implementation of existing vaccination policies, with potential for significant reductions in premature mortality. Moreover, prevention (or delay) of diabetes onset, including through prevention and management of the high levels of adiposity in the Mexican population, will be essential for reducing diabetes-associated infectious disease mortality.
## Data availability statement
Data are available upon reasonable request. Data from the Mexico City Prospective Study are available to bona fide researchers. For more details, the study’s Data and Sample Sharing policy may be downloaded (in English or Spanish) from https://www.ctsu.ox.ac.uk/research/mcps. Available study data can be examined in detail through the study’s Data Showcase, available at https://datashare.ndph.ox.ac.uk/mexico/.
## Patient consent for publication
Not applicable.
## Ethics approval
This study involves human participants and ethics approval was granted by the Mexican Ministry of Health, the Mexican National Council of Science and Technology (0595 P-M), the Central Oxford Research Ethics Committee (C99.260) and the Ethics and Research Commissions from the Medicine Faculty at the National Autonomous University of Mexico (FMED/CI/SPLR/$\frac{067}{2015}$). Participants gave informed consent to participate in the study before taking part.
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|
---
title: Inhibition of apoptosis through AKT-mTOR pathway in ovarian cancer and renal
cancer
authors:
- Hongrun Chen
- Lianfeng Zhang
- Meini Zuo
- Xiaowen Lou
- Bin Liu
- Taozhu Fu
journal: Aging (Albany NY)
year: 2023
pmcid: PMC10008491
doi: 10.18632/aging.204564
license: CC BY 3.0
---
# Inhibition of apoptosis through AKT-mTOR pathway in ovarian cancer and renal cancer
## Abstract
Objective: Ovarian cancer and renal cancer are malignant tumors; however, the relationship between TTK Protein Kinase (TTK), AKT-mTOR pathway and ovarian cancer, renal cancer remains unclear.
Methods: Download GSE36668 and GSE69428 from Gene Expression Omnibus (GEO) database. *Weighted* gene co-expression network analysis (WGCNA) was performed. Created protein-protein interaction (PPI) network. Used Gene *Ontology analysis* (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis. Gene Set Enrichment Analysis (GSEA) analysis and survival analysis were performed. Created animal model for western blot analysis. Gene Expression Profiling Interactive Analysis (GEPIA) was performed to explore the role of TTK on the overall survival of renal cancer.
Results: GO showed that DEGs were enriched in anion and small molecule binding, and DNA methylation. KEGG analysis presented that they mostly enriched in cholesterol metabolism, type 1 diabetes, sphingolipid metabolism, ABC transporters, etc., TTK, mTOR, p-mTOR, AKT, p-AKT, 4EBP1, p-4EBP1 and Bcl-2 are highly expressed in ovarian cancer, Bax, Caspase3 are lowly expressed in ovarian cancer, cell apoptosis is inhibited, leading to deterioration of ovarian cancer. Furthermore, the TTK was not only the hub biomarker of ovarian cancer, but also one significant hub gene of renal cancer, and its expression was up-regulated in the renal cancer. Compared with the renal cancer patients with low expression of TTK, the patients with high expression of TTK have the poor overall survival ($$P \leq 0.0021$$).
Conclusion: TTK inhibits apoptosis through AKT-mTOR pathway, worsening ovarian cancer. And TTK was also one significant hub biomarker of renal cancer.
## INTRODUCTION
Ovarian cancer and renal cancer can occur at any age, its incidence is increasing [1, 2]. It initially manifests with very little symptoms and is difficult to detect at first. When a patient presents their symptoms to a hospital for examination, it is often in late stages, and treatment has already been delayed [3]. Ovarian and renal cancers pose a major health threat to women. Ovarian cancer and renal cancer can seriously affect the life of patients, and even threaten their lives. Surgical treatment is the main treatment, supplemented by chemotherapy, radiotherapy, immunotherapy, etc., [ 4, 5]. However, pathogenesis of ovarian cancer and renal cancer is unknown, and more researches are needed.
Bioinformatics is the intersection of biology and computer science. It is also an important content of proteome research [6]. Today bioinformatics technology is advancing, it has been used to analyze known or new gene products [7].
TTK (TTK Protein Kinase) is a Protein coding gene located in chromosome 6q13-6q21. Its related pathways include DNA damage and cell cycle, encodes phosphorylated Protein kinases on serine [8]. TTK protein kinase is a mitotic kinase that participates in control of cell progression through mitosis, and affects cell division [9].
Akt/mTOR pathway has multiple initiation mechanisms, and it manifests in some cancer subtypes [10]. Akt/mTOR pathway affects protein translation, survival, metabolism, its abnormalities can cause cancer [11]. But, how TTK and AKT-mTOR pathways affect ovarian cancer and renal cancer is uncertain.
This research with aid of bioinformatics, digging at core genes of ovarian cancer and renal cancer, through some experiments to determine whether TTK and AKT-mTOR pathway can affect ovarian cancer and renal cancer.
## Ovarian cancer data set
Profiles of ovarian cancer GSE36668 and GSE69428 were generated using GPL570, at the same time we also use GSE140082 data set as a survival data validation from GEO database (http://www.ncbi.nlm.nih.gov/geo/). Among them, GSE36668 included 8 ovarian cancer and 4 normal tissue samples, GSE69428 included 10 ovarian cancer and 10 normal tissue samples to get DEGs in ovarian cancer.
## Batch processing
For combination of multiple data sets, we firstly combined data sets GSE36668 and GSE69428 with R software package inSilicoMerging (https://doi.org/$\frac{10.1186}{1471}$-2105-13-335) to obtain merge matrix. Further, we used remove Batch Effect function of the R software package limma (version 3.42.2) to remove batch effect, finally obtained matrix after removing batch effect, which was applied to subsequent analysis.
## Screening of DEGs
R package “limma” was used for probe summary and background correction of batched-effect post-matrix for GSE36668 and GSE69428. Used Benjamini-Hochberg method to set raw P values. Used fold change (FC) to get false discovery rate (FDR). Cut-off criterion for DEG was FDR < 0.05. And make a volcano diagram.
## Weighted gene co-expression network analysis (WGCNA)
Top $50\%$ genes with smallest median absolute deviation were acquired and excluded. For all genes in pairs perform Pearson correlation matrix and average chain method, using power function a|mn=| C|mn |^β build weighted adjacency matrix. After choose soft threshold parameter, converts adjacency matrix to topological overlap matrix. Average linkage hierarchical clustering was performed, minimum size (genome) was 30. Sensitivity was set to 3. We calculated the phase divergence of module feature genes, incorporating modules with distances less than 0.25. At the same time, we also predicted the inter-relationship of genes in the module to obtain core genes.
## PPI network
Intersect the core genes of WGCNA with the genes selected in the volcano map. The list of genes was input into the STRING (http://string-db.org/) database to build a PPI network (confidence >0.4) for predicting core genes. PPI network was imported into cytoscape software. Three algorithms (MCC, MNC, DMNC) were used to calculate ten best correlation genes and take intersection, and core gene list was exported after visualization.
Selected DEGs in tumor group, analyzed by Cytoscape software (Figure 4A), a total of 3 core modules were obtained using MCODE algorithm (Figure 4B–4D), and 22 common hub genes were obtained using MCC algorithm to identify the core genes (Figure 4E–4G).
**Figure 4:** *Construction of protein-protein interaction (PPI) network. (A) PPI network; (B–D) 3 core modules; (E–G) 22 common central genes.*
## Functional enrichment analysis
Gene *Ontology analysis* (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis are computational methods for assessing gene function and biological pathways. The core of this research will figure out Venn diagram list input KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html), to obtain the latest KEGG *Pathway* gene annotation. As the background, the genes were mapped to the background set, and the R software package clusterProfiler (version 3.14.3) was used for enrichment analysis to obtain the results of gene set enrichment. Also use R software package org.Hs.eg.db (version 3.1.0) gene in the GO annotation, as the background, to map genes to background in the collection, set the minimum gene sets 5, biggest gene sets, 5000, P value of < 0.05 and a FDR of < 0.25 were considered as measures of statistical significance.
This study will Wayne figure out the difference of gene list input KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html) to get latest KEGG *Pathway* gene annotation, Used R package clusterProfiler (version 3.14.3) for enrichment analysis to get results of gene set enrichment. GO annotation of genes in R software package org.Hs.eg.db (version 3.1.0) was used as background, genes were mapped to background set. The minimum gene set was 5, maximum gene set was 5000. P value of < 0.05, FDR of < 0.25 were measures of statistical significance.
In addition, we use Metascape database (http://metascape.org/gp/index.html), for above differences in gene enrichment of function analysis and export list.
Used GSE10540 gene matrix for enrichment analysis, and it can be seen that the GSEA enrichment project was validated with the GO and KEGG enrichment projects among differentially expressed genes, which were mainly enriched in the endoplasmic reticulum, organelle subcompartment, purine ribonucleotide transmembrane transporter (Figure 5A, 5B).
**Figure 5:** *Functional enrichment analysis and GSEA analysis. (A) GO and KEGG enrichment projects were verified; (B) GO and KEGG enrichment projects were verified; (C) GO enrichment term; (D) GO enrichment term; (E) GO enrichment terms under GSEA analysis; (F) KEGG enrichment terms under GSEA analysis; (G) GO enrichment term; (H) KEGG enrichment item.*
In GO analysis, they were mostly concentrated in anion and small molecule binding, eye development, visual system formation, lipid biosynthesis, sensory system formation, DNA methylation. In KEGG analysis, target genes were mostly concentrated in cholesterol metabolism, type 1 diabetes, sphingolipid metabolism, ABC transporters, etc. Figure 5C, 5D are bubble plot P-values of GO-enriched terms. GO enrichment terms under GSEA analysis are shown in Figure 5E, 5F are KEGG enrichment terms. At the aspect of biological process, the DEGs were mainly enriched in the bile secretion, ovarian steroidogenesis, steroid biosynthesis (Figure 5G, 5H). Are bubble plot P-values of KEGG-enriched terms.
## GSEA analysis
GSEA, computational method that can perform GO and KEGG analyses on complete genomes. In our study, we grouped the samples by tumor tissue and normal tissue, performed GO and KEGG analyses on the whole genome. Developed by GSEA.
## Heat map of gene expression
By R package heatmap to make a heatmap of expression degree of core genes found by three algorithms in PPI network to visually displayed expression differences of core genes between cancer and normal tissue.
## Survival analysis
We selected the ovarian cancer survival data from the dataset GSE140082, used R software package maxstat (version:0.7–25) to calculate optimal cut-off value of RiskScore of ten core genes, best cut-off value is calculated, Survfit function of the R package survival was further used to find prognostic differences. We also used R package forest to make a forest map of 10 core genes to observe whether each independent core gene had a significant effect on prognosis of renal cancer.
We selected 14 core genes with large expression differences to combine KM survival curve and forest plot with the survival data of GSE140082, and MELK and TTK genes had significant prognostic differences ($P \leq 0.05.$ Figures 8, 9).
**Figure 8:** *Survival analysis, survivorship curve. (A) Survival data of 14 core genes with significantly different expression and GSE140082; (B) CENPU: p = 0.45, HR = 0.86, 95% CI (0.57, 1.28); (C) CEP55: p = 0.81, HR = 1.05, 95% CI (0.70, 1.57); (D) KIF23: p = 0.43, HR = 0.85, 95% CI (0.57, 1.27); (E) MELK: p = 0.08, HR = 0.70, 95% CI (0.47, 1.05); (F) NCAPH: p = 0.34, HR = 1.22, 95% CI (0.81, 1.82); (G) NDUFA9: p = 0.64, HR = 1.10, 95% CI (0.74, 1.64).* **Figure 9:** *Survival analysis, forest map. (A) NDUFS1: p = 0.36, HR = 0.83, 95% CI (0.56, 1.24); (B) NUF2: p = 0.62, HR = 0.90, 95% CI (0.60, 1.35); (C) PLK4: p = 0.10, HR = 1.41, 95% CI (0.94, 2.12); (D) PSMB2: p = 6.9e-4, HR = 0.50, 95% CI (0.33, 0.75); (E) PSME4: p = 0.32, HR = 1.23, 95% CI (0.82, 1.84); (F) TTK: p = 0.37, HR = 1.20, 95% CI (0.80, 1.79); (G) UBE2C: p = 0.37, HR = 0.83, 95% CI (0.56, 1.24); (H) YKT6: p = 0.27, HR = 0.80, 95% CI (0.53, 1.19).*
## Establishment of animal models
Measure weight of C57BL/6J mice (Female, 8 ± 1 Weeks) and recorded. They were then randomly numbered and grouped. Divide rats into 4 groups of 6 rats each. Group A: Con; Group B: OV; Group C: OV/TTK-OE; Group D: OV/TTK-KO. The oncogene of human ovarian cancer tumor was directly transferred into mice for expression. The target gene (genome fragment) was injected into the fertilized egg of the mouse by microinjection method, and the fertilized egg was implanted into the fallopian tube (or uterus) of the recipient animal to develop transgenic mice carrying foreign gene.
## Western blot
Extracting total protein from tissue, after concentration was determined by UV method, $\frac{1}{4}$ of protein sample volume of 5× protein loading buffer (reduced) was added to tissue, boiled at 100°C for 10 min, cooled, packed and frozen in −80°C refrigerator until use. Protein samples were subjected to $12\%$ SDS-PAGE gel electrophoresis, membrane transformation, other operations. Block $5\%$ skim milk at room temperature for 1 h. Added primary antibody, incubated samples overnight at 4°C. After shaking TBST for 3 times (5 min/time), rabbit secondary antibody was added. After incubation for 1 h at room temperature, TBST was shaken 3 times (5 min/time). Analyzed results after chemiluminescence solution was developed.
## GEPIA for the TTK and renal cancer
Through the GEPIA, the expression of TTK in the renal cancer was analyzed, and the relationship between relative expression of TTK and pathological stage was also explored. Furthermore, the overall survival of renal cancer was analyzed.
## Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
## Differentially expressed genes (DEGs)
1052 DEGs were found based on DEGs identified in debatching merge matrix of GSE36668 and GSE69428 (Figure 1).
**Figure 1:** *DEGs were identified. 602 up-regulated (Red) and 450 down-regulated genes (Green).*
## WGCNA analysis
Soft threshold power in WGCNA analysis was set to 7, which is lowest power for scale-free topological fit index of 0.9 (Figure 2A, 2B). Hierarchical clustering trees were constructed for all genes and yielded 14 significant modules (Figure 2C). Interactions between these modules were then analyzed (Figure 2D). Relationship between modules and clinical manifestations of ovarian cancer is shown in Figure 3A. Highly correlated modules were then plotted against a column scatter plot of clinical characteristics (Figure 3B–3G). We take as standard | MM | > 0.8, a total of 1909 in clinical important modules with high connectivity genes have been identified as core.
**Figure 2:** *WGCNA analysis. (A) β = 7,186.37; (B) β = 7,0.707; (C) 14 important modules; (D) A high degree of independence between modules.* **Figure 3:** *WGCNA analysis. (A) relationship between module and clinical manifestations of ovarian cancer; (B) Module Membership in darkolivegreen4 module: p = 9.1e-6, r = 0.70; (C) Module Membership in black module: p = 1.6e-37, r = 0.52; (D) Module Membership in brown2 module: p = 5.7e-228, r = 0.63; (E) Module Membership in darkmagenta module: p = 0.0e + 0, r = 0.65; (F) Membership in coral1 module: p = 5.5e-6, r = 0.64; (G) Membership in salmon4 module: p = 9.7e-15, r = 0.57.*
## Metascape enrichment analysis
Content enriched by Metascape includes GO enrichment terms (Figure 6A), enrichment networks colored by enrichment terms and P values (Figure 6B, 6C), and PPI networks and core modules formed in the Metascape website based on core genes (Figure 7A, 7B).
**Figure 6:** *Metascape enrichment analysis. (A) Enrichment of GO; (B) Enrichment networks colored by enrichment terms; (C) Enrichment networks colored by P-value.* **Figure 7:** *Metascape enrichment analysis. (A) PPI network. (B) 3 core modules.*
## CTD analysis
In this study, we input the core gene list into the CTD website to search for diseases associated with core genes and improve the understanding of gene-disease association (Figures 10, 11).
**Figure 10:** *CTD analysis. The list of core genes was entered into the CTD website. (A) CENPU; (B) CEP55; (C) KIF23; (D) MELK; (E) NCAPH; (F) NDUFA9; (G) NDUFS1; (H) NUFE.* **Figure 11:** *CTD analysis. (A) PLK4; (B) PSMB2; (C) PSME4; (D) TTK; (E) UBE2C; (F) YKT6.*
## microRNAs analysis
In this study, we input the hub gene list into TargetScan to find relevant miRNA and improve the understanding of gene expression regulation (Table 1).
**Table 1**
| Unnamed: 0 | Gene | MIRNA | MIRNA.1 | MIRNA.2 |
| --- | --- | --- | --- | --- |
| 1 | PSMB2 | hsa-miR-31-5p | | |
| 2 | PSME4 | hsa-miR-6088 | hsa-miR-143-3p | hsa-miR-4770 |
| 3 | YKT6 | hsa-miR-129-1-3p | hsa-miR-129-2-3p | |
| 4 | NCAPH | hsa-miR-493-5p | | |
| 5 | NDUFS1 | hsa-miR-599 | hsa-miR-320d | hsa-miR-320a |
| 6 | KIF23 | hsa-miR-103a-3p | hsa-miR-107 | |
| 7 | MELK | hsa-miR-802 | | |
| 8 | TTK | hsa-miR-455-3p.1 | | |
| 9 | CEP55 | hsa-miR-144-3p | | |
| 10 | NUF2 | hsa-miR-599 | | |
| 11 | NDUFA9 | none | | |
| 12 | PLK4 | none | | |
| 13 | CENPU | none | | |
| 14 | UBE2C | none | | |
## Heat map
Expression of core genes in de-batched matrix was subjected to heat map processing, expression of all genes was up-regulated in tumor group (Figure 12).
**Figure 12:** *Heat map. The expression levels of all genes were up-regulated in the tumor group.*
## Western blot (WB)
Western blotting analysis showed that TTK, mTOR, AKT were highly expressed in ovarian cancer ($P \leq 0.05$). TTK, mTOR, p-mTOR, AKT, p-AKT, 4EBP1, p-4EBP1, Bcl-2 are highly expressed in ovarian cancer, Bax, Caspase3 are lowly expressed in ovarian cancer, and cell apoptosis is inhibited, leading to the deterioration of ovarian cancer. When TTK, mTOR and AKT were overexpressed, main molecules of apoptotic pathway were more inhibited. Conversely, main molecules of the apoptotic pathway are activated to induce apoptosis in ovarian cancer cells (Figure 13).
**Figure 13:** *Western blotting. TTK, mTOR, p-mTOR, AKT, p-AKT, 4EBP1, P-4EBP1, Bcl-2, Bax, Caspase3. P < 0.05.*
## Role of TTK on the renal cancer
Compared with the normal tissues, the expression of TTK in the renal cancer was higher (Figure 14A). There is a positive correlation between expression of TTK and the pathological stage of renal cancer ($P \leq 0.05$, Figure 14B). Compared with the renal cancer patients with low expression of TTK, the patients with high expression of TTK have the poor overall survival ($$P \leq 0.0021$$, Figure 14C).
**Figure 14:** *Role of TTK on the renal cancer. (A) Comparison of expression of TTK between normal and renal cancer. (B) Correlation between expression of TTK and the pathological stage of renal cancer. (C) Overall survival of renal cancer.*
## DISCUSSION
Patients with high expression of TTK in ovarian cancer have unsatisfactory results. KEGG signaling pathway was enriched into multiple metabolic pathways, and these biological processes were related to AKT-mTOR signaling pathway. High expression of TTK inhibited cell apoptosis, thereby leading to tumor enlargement. Furthermore, the TTK was not only the hub biomarker of ovarian cancer, but also one significant hub gene of renal cancer, and its expression was up-regulated in the renal cancer. Compared with the renal cancer patients with low expression of TTK, the patients with high expression of TTK have the poor overall survival ($$P \leq 0.0021$$).
Ovarian cancer and renal cancer are prone to invasive growth and metastasis [12]. Nowadays, ovarian cancer and renal cancer group in China are gradually younger, urban women are more susceptible to disease. Ovarian cancer and renal cancer are of great harm, which not only has a great impact on the patient's physiology and psychology, but also has a high treatment cost, which causes a heavy burden on the patient's family economy.
TTK is a bispecific protein kinase [13, 14]. When it makes too many centrosomes, it has potential to trigger tumors, affecting mitotic spindles [15]. TTK functions in relation to cell proliferation and enhances Aurora kinase B activity [16]. TTK is a regulator of cell cycle, [17], and is linked to tumorigenesis [18]. TTK can help bladder cancer cells activity and mediate epithelial-mesenchymal transition [19]. There is evidence that TTK linked to glioblastoma [20]. Elevated TTK levels lead to centrosome enlargement and chromosomal instability, which leads to tumorigenesis [21, 22]. Therefore, it is possible that TTK has a certain effect on ovarian cancer and renal cancer.
AKT belongs to AGC family of protein kinases [23]. Activity of AKT affects cell function [24], activate protein translation and enhance cell growth, phosphorylates target proteins in cytoplasm and nucleus [25, 26], stimulates cell reproduction [27]. It has been shown that inhibition of Akt can affect tumor cells [28]. Other studies have shown that, AKT is a therapeutic target for cancer [29]. AKT can directly phosphorylate mTOR and act indirectly on mTOR.
The mTOR is the serine/threonine protein kinase [30]. And it acts on signaling pathway of cell reproduction [31], and is influenced by cell signaling [32]. In cells, it exists in form of two different multiprotein complexes. One is mTORC1, it stimulates cell development and is activated primarily via PI3P/AKT pathway [33]. One is mTORC2, it promotes AKT activation through direct phosphorylation of its hydrophobic motif (Ser473) [34]. mTOR kinases are involved in key events that integrate external and internal signals, coordinating cell growth and proliferation. Multiple components of the signaling pathway that signals through mTOR are dysregulated in many cancer types. Therefore, mTOR can be a good entry point for tumor treatment [35].
Akt/mTOR is important signaling pathway of cellular activity, which can regulate cell size, metabolism, motility, so on [36]. PI3K/AKT/mTOR pathway affects normal cellular processes, also have abnormal manifestations in many cancers [37]. It is evidence that, PI3K/Akt/mTOR pathway targets non-small cell lung cancer [38], also affects breast and gastric cancer [37, 39]. Akt/mTOR pathway may be influenced by many factors, its activation has been implicated in pathogenesis of a variety of tumors [40]. Thus, we hypothesized, AKT-mTOR pathway can affect cancer. TTK can affect cancer cells through AKT-mTOR pathway [41]. This supports our point, TTK inhibits apoptosis through AKT-mTOR pathway, which in turn leads to worsening of cancer.
Our investigation also has some shortcomings, we have not conducted clinical validation to solidify this view. We should explore this more next.
In summary, TTK and AKT-mTOR pathways affect ovarian cancer. High TTK expression means poor outcomes for ovarian cancer patients. And TTK was also one significant hub biomarker of renal cancer.
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|
---
title: Circulating levels of MOTS-c in patients with breast cancer treated with metformin
authors:
- Elisabet Cuyàs
- Sara Verdura
- Begoña Martin-Castillo
- Javier A. Menendez
journal: Aging (Albany NY)
year: 2022
pmcid: PMC10008497
doi: 10.18632/aging.204423
license: CC BY 3.0
---
# Circulating levels of MOTS-c in patients with breast cancer treated with metformin
## Abstract
The mitokine MOTS-c is a mitochondrially-encoded “exercise-mimetic peptide” expressed in multiple tissues, particularly skeletal muscles, which can be detected as a circulating hormone in the blood. MOTS-c mechanisms of action (MoA) involve insulin sensitization, enhanced glucose utilization, suppression of mitochondrial respiration, and targeting of the folate-AICAR-AMPK pathway. Although MOTS-c MoA largely overlap those of the anti-diabetic biguanide metformin, the putative regulatory actions of metformin on MOTS-c have not yet been evaluated in detail. Here, we measured circulating MOTS-c in paired baseline and post-treatment sera obtained from HER2-positive breast cancer patients randomized to receive either metformin combined with neoadjuvant chemotherapy and trastuzumab or an equivalent regimen without metformin. We failed to find any significant alteration of circulating MOTS-c –as measured using the commercially available competitive ELISA CEX132Hu– in response to 24 weeks of a neoadjuvant chemotherapy/trastuzumab regimen with or without daily metformin. Changes in circulating MOTS-c levels failed to reach statistical significance when comparing patients achieving pathological complete response (pCR), irrespective of metformin treatment. The inability of metformin to target skeletal muscle, the major tissue for MOTS-c production and secretion, might limit its regulatory effects on circulating MOTS-c. Further studies are needed to definitely elucidate the nature of the interaction between metformin and MOTS-c in cancer and non-cancer patients.
## INTRODUCTION
Mitochondrial open reading frame of the 12S ribosomal RNA type-c (MOTS-c) is a mitochondrially-encoded 16-amino-acid biopeptide that functions as an exercise-induced regulator of metabolic homeostasis [1, 2] and a modulator of obesity-, diet-, and aging-dependent metabolic function by acting as a systemic, endocrine-acting mitokine [3, 4]. MOTS-c dynamically translocates to the nucleus in response to metabolic stress to directly regulate the expression of a broad spectrum of genes, including antioxidant response element-containing target genes [5]. By targeting folate-dependent de novo purine biosynthesis, MOTS-c boosts the levels of the endogenous AMP analog 5-aminomidazole-4-carboxamide ribonucleotide (AICAR), which in turn activates the master energy sensor AMP-activated protein kinase (AMPK). Skeletal muscle cell-targeted involvement of the folate-AICAR-AMPK pathway constitutes the mechanistic basis of MOTS-c as an endogenous “exercise mimetic”, which can stimulate glucose utilization and fat oxidation and suppress inflammation [4].
Exercise interventions show promise as effective adjunct strategies to prevent and/or attenuate chemotherapy-associated toxicity (e.g., cardiotoxicity and cardiopulmonary dysfunction) in patients with early-stage breast cancer (BC) [6, 7]. Exploratory studies have demonstrated that exercise interventions might also modulate host- and tumor-related pathways in patients on standard chemotherapy [8]. Indeed, several ongoing and planned interventional studies (e.g., Neo-ACT NCT05184582, Neo-Train NCT04623554) have been designed to examine whether physical exercise interventions during the neoadjuvant chemotherapy period can bolster treatment efficacy [9, 10]. Pharmacological therapeutics that partially mimic the systemic impact of exercise have also been proposed for those cancer patients for whom exercise training may not be an option [11–13]. One putative exercise mimetic, metformin, shares many mechanistic features with MOTS-c, including: (a) insulin sensitization, (b) enhancing glucose utilization, (c) suppressing mitochondrial respiration, and (d) targeting the folate-AICAR-AMPK pathway [4, 14–16]. To date, however, no study has examined whether metformin can influence the expression of MOTS-c in cancer patients. Here, we explored the impact of metformin on circulating MOTS-c levels in the METTEN study (EudraCT number 2011-000490-30), a phase 2 clinical trial of women with HER2-positive BC randomized to receive either metformin (850 mg twice daily) for 24 weeks concurrently with 12 cycles of weekly paclitaxel plus trastuzumab, followed by four cycles of 3-weekly FE75C plus trastuzumab (arm A), or an equivalent regimen without metformin (arm B), before surgery [17].
The present study was conducted with paired baseline and post-treatment serum samples collected from 38 patients ($$n = 19$$ in each arm) belonging to the intention-to-treat population of the METTEN trial (Figure 1A, left), which included randomly assigned patients receiving at least one dose of study medication. All samples were evaluated in parallel for circulating MOTS-c using a commercially available competitive ELISA (CEX132Hu; CloudClone Corp., Wuhan, China) [18, 19]. Within- and between-group data were assessed by paired t-test and post hoc Tukey multiple comparison tests on repeated measures ANOVA. No statistically-significant differences were found between the pre- and post-levels of circulating MOTS-c irrespective of the treatment arm (Figure 1A, right). Similarly, changes in circulating MOTS-c levels failed to reach statistical significance when comparing patients achieving pathological complete response (pCR), defined as the absence of invasive tumor cells in post-neoadjuvant therapy surgical histopathology of the complete resected breast specimen, including sample regional lymph nodes [17], and patients with non-pCR, irrespective of the treatment arm (Figure 1B).
**Figure 1:** *Circulating levels of MOTS-c in patients with HER2+ breast cancer treated with neoadjuvant metformin. (A) Left. METTEN study design. Circulating MOTS-c levels were determined through blood draws obtained at pre- (0 weeks) and post- (24 weeks) treatment using a commercial ELISA kit (CloudClone Corp., Wuhan, China; Catalog No. CEX132Hu). Right. Box plot (median, 25%–75% quartiles and minimal and maximal values) of the pre- and post-treatment distribution of circulating MOTS-c in women randomized to arms A (metformin-containing) and B (without metformin). (B) Box plot (median, 25%–75% quartiles and minimal and maximal values) of the pre- and post-treatment distribution of circulating MOTS-c in non-pCR and pCR groups. No between-group comparisons reached statistical significance in A and B.*
Our findings indicate that metformin does not operate as an exercise mimetic to augment the circulating levels of MOTS-c in patients with BC treated with neoadjuvant therapy. It is possible that the lack of effect relates to different target tissues of metformin and MOTS-c. We have recently learned that an expansion of the skeletal muscle-derived MOTS-c protein pool occurs concomitantly with increases in mitochondrial DNA [20], which might suggest that circulating MOTS-c could operate as a surrogate marker of phenotypic and functional shifts of mitochondrial networks [21]. Metformin is known to activate AMPK when targeting the liver, kidney, and intestine but not skeletal muscle [22] –the major tissue for MOTS-c production and secretion–, which might prevent any regulatory effect on circulating MOTS-c. Moreover, we are accumulating evidence that metformin does not enhance (and instead dampens) the beneficial strength gains and muscle activation in response to exercise training in healthy elderly people [23–26]. The mechanisms of MOTS-c production, secretion, distribution, and metabolism in the human body remain to be fully elucidated. Likewise, the extent of involvement of various tissue targets and/or the effects of metformin on skeletal muscle metabolism and how they determine the pharmacodynamics and endogenous serum levels of MOTS-c in patients with BC await evaluation in future studies. One should acknowledge that plasma and muscle MOTS-c show opposing responses to aging in older men, thereby suggesting that the primary source of circulating MOTS-c is not skeletal muscle or the pharmacokinetics of MOTS-c changes with age [27]. Similarly, the ability of muscle cells to release MOTS-c can be impaired due to changes in the export process and/or to the exhausted capacity of muscle (or hepatic) cells to tolerate or adapt to systemic metabolic stress occurring in cancer patients. Therefore, we cannot exclude the possibility that serum circulating MOTS-c and muscle MOTS-c can be differentially regulated by metformin, as aging does [27].
There are several limitations to this study. Endogenous levels of circulating MOTS-c have been shown to vary significantly (from 154 pg/mL to 584 ng/mL) depending on the assay method used [5, 18, 19, 28–30]. In our series of patients, the endogenous serum levels of MOTS-c ranged from 181 ng/mL to 1033 ng/mL. Overall, these findings would strongly suggest that the immunoreactive species of circulating MOTS-c detected using different kits are not identical. Our previous analysis confirmed that treatment of non-diabetic patients with HER2+ BC with oral metformin (850 mg twice daily) for 24 weeks produced blood levels of circulating metformin of ~7 μmol/L, equivalent to those generally achieved in diabetic patients with the usual clinical doses and schedule [17]. The exercise-induced augmentation of circulating MOTS-c in young subjects was found to return to baseline after only 4 hours of resting [4]. As we measured circulating MOTS-c in blood that was not strictly timed in relation to the last preceding oral dose of metformin [17], our data need to be viewed cautiously in terms of association between metformin treatment, achieved serum concentration of MOTS-c, and probability of pCR in BC patients. Moreover, the METTEN trial was conducted in patients with the HER2+ subtype of breast cancer, which leaves open the question of whether the circulating levels of MOTS-c and/or the regulatory activity of metformin on MOTS-c might be different in patients with other BC subtypes, such as luminal A, HER2-negative luminal B or triple negative [31]. Nonetheless, this is a retrospective study in a small sample size for which the evaluation of MOTS-c was not part of the original study design. Care should therefore be taken in interpreting and generalizing these findings. With an ever-growing recognition of the role of MOTS-c on age-related diseases including diabetes, obesity, osteoporosis, cardiovascular, and neurodegenerative diseases [32, 33], further studies are needed to elucidate the nature of the interaction between metformin and MOTS-c in cancer and non-cancer patients.
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|
---
title: Circ_0001052 promotes cardiac hypertrophy via elevating Hipk3
authors:
- Mengyue Yang
- Weichen Wang
- Longlong Wang
- Yuze Li
journal: Aging (Albany NY)
year: 2023
pmcid: PMC10008499
doi: 10.18632/aging.204521
license: CC BY 3.0
---
# Circ_0001052 promotes cardiac hypertrophy via elevating Hipk3
## Abstract
Cardiac hypertrophy (CH) is a crucial risk factor for sudden death. Circular RNAs (circRNAs) exert significant effects in various biological and pathological processes. Circ_0001052 is sourced from Hipk3 (homeodomain-interacting protein kinase 3) and is reported to aggravate myocardial fibrosis. The purpose of the current study was to clarify the role and mechanism of circ-Hipk3 in CH. Transverse aortic constriction (TAC) was used to create an in vivo CH model, and angiotensin II (Ang II) therapy was used to create an in vitro CH model in cardiomyocytes (CMs). It was uncovered that circ_0001052 exerted pro-hypertrophic effects in Ang II-treated CMs. Next, the circular characteristics of circ_0001052 were verified, and we identified that circ_0001052 positively regulated Hipk3. Hipk3 exerted the same functions as circ_0001052 did. It is significant to note that circ_0001052 acted as the ceRNA of Hipk3 by sponging miR-148a-3p and miR-124-3p. According to rescue assays, miR-148a-3p and miR-124-3p partially reversed the effects of circ_0001052. Further, we testified that circ_0001052 recruited Srsf1 to stabilize Hipk3. Finally, rescue assays demonstrated that circ_0001052 promoted CH via up-regulation of Hipk3. In conclusion, our work unveiled that circ_0001052 promoted hypertrophic effects through elevating Hipk3 via sponging miR-148a-3p and miR-124-3p and recruiting Srsf1.
## INTRODUCTION
Pathological cardiac hypertrophy (CH) is triggered by continuous hypertrophic stresses like hypertension, ischemia and myocarditis [1]. Initially, CH is not harmful since it maintains normal cardiac function by adaptively responding to the increased cardiac load. However, extended hypertrophy motivates adjustments in metabolism, loss of adrenergic responsivity and deposition of extracellular collagen [2], which result in irreversible cardiac remodeling, eventually leading to heart failure or even sudden death [3, 4]. The in vivo CH model is usually established by conducting transverse aortic constriction (TAC) on mice [5]. The in vitro CH model has been widely reported to be established under the inducement of angiotensin II (Ang-II) [6, 7].
As a class of non-coding RNAs, circular RNAs (circRNAs) are specifically expressed in tissues [8]. CircRNAs are differentially generated by back splicing and are characterized by covalently closed continuous loops [9]. Compared with linear RNAs, circRNAs possess no free 3′ or 5′ end and have stronger stability [10]. CircRNAs regulate the expression of target genes via sponging microRNAs (miRNAs) [8] and therefore exert significant roles in various biological and pathological processes in diseases, including CH. CircSlc8a1 is a potential therapeutic target for CH via endogenously sponging miR-133a [11]. Circ-HIPK3 interacts with miR-17-3p to elevate ADCY6 expression, thereby strengthening adrenaline-mediated effects in heart failure [12].
Recently, the competitive endogenous RNA (ceRNA) pattern has attracted much attention. CircRNAs sponge miRNAs, reducing the suppression of miRNAs on target messenger RNAs (mRNAs), which is called the ceRNA mechanism. CircRNA_000203 increases the expression of Gata4 via sequestering miR-26b-5p and miR-140-3p to promote CH [7]. CircRNA HRCR enhances ARC expression by sponging miR-223, resulting in mitigated CH [13]. Circ_0076631 sponges miR-214-3p to up-regulate caspase-1, therefore attenuating cell pyroptosis in diabetic cardiomyopathy [14]. CircRNA ncx1 promotes ischemic myocardial injury through sponging miR-133a-3p to elevate CDIP1 expression [15]. CircRNA ACR regulates the Pink1/FAM65B axis to alleviate myocardial ischemia/reperfusion injury [16].
CircRNAs derived from HIPK3 have been discovered to play an important role in cardiovascular diseases. For instance, in Ang II-induced cardiac fibrosis, circRNA HIPK3 regulates the proliferation and migration of cardiac fibroblasts via sponging miR-29b-3p [17]. However, whether such circRNAs function in CH remains unclear. Here, we sought to examine the function and underlying mechanism of certain circRNA from Hipk3 in Ang II-induced CH.
## Circ_0001052 promotes hypertrophic effects
The first step was to establish the in vivo and in vitro CH models. As predicted, in the TAC group, ANF, BNP and β-MHC were up-regulated than sham group (Figure 1A). Accordingly, Ang II treatment significantly increased the expression of ANF, BNP, and β-MHC in HL-1 and PCM cells (Figure 1B and 1C). The cell surface area was then measured using immunofluorescence (IF). The cell surface area was enlarged in CMs treated with Ang II (Figure 1D). Since a circRNA from Hipk3 has been reported to aggravate myocardial fibrosis, we wondered if such circRNAs exerted pro-hypertrophic effects. Based on circBase [18], 3 circRNAs (mmu_circ_0009139, mmu_circ_0009140 and mmu_circ_0001052) from Hipk3 are identified. Interestingly, it was found that only mmu_circ_0001052 (named circ_0001052 for convenience in the following part of the study) was significantly up-regulated in mice after TAC (Figure 1E), as was the case in Ang II-induced CMs (Figure 1F). We then eliminated circ_0001052 in CMs treated with Ang II (Figure 1G). The conclusion showed that the depletion of circ_0001052 greatly inhibited the Ang II-induced expansion of cell surface area (Figure 1H). Similar to this, silencing circ_0001052 reduced the mRNA and protein levels of each of the three hypertrophic indicators in CMs treated with Ang II (Figure 1I and 1J). As a result, we deduced that circ_0001052 promoted the hypertrophic effects in CMs treated with Ang II.
**Figure 1:** *Circ_0001052 promoted hypertrophic effects. (A) The mRNA and protein levels of hypertrophic biomarkers in TAC and sham group. (B and C) The mRNA and protein levels of hypertrophic biomarkers in CMs. (D) The surface area of CMs by using IF staining. (E and F) The levels of circ_0009139, circ_0009140 and circ_0001052 in CMs. (G) The depletion efficiency of circ_0001052 in Ang II-treated CMs. (H) IF assay detected cell surface area under the knockdown of circ_0001052. (I and J). The mRNA and protein levels of hypertrophic biomarkers in Ang II-treated CMs with inhibited circ_0001052. **P < 0.01. Abbreviation: n.s: no significance.*
## Circ_0001052 positively regulates Hipk3 and exerts pro-hypertrophic effects
Then, the circular characteristics of circ_0001052 were verified. In this regard, Rnase R was used to treat cells for exploring the stability of circ_0001052 and linear Hipk3. The results revealed that Hipk3 level was obviously reduced by Rnase R treatment while circ_0001052 was hardly impacted (Figure 2A). Then divergent primers were designed for amplifying circ_0001052 while convergent primers were designed for amplifying Hipk3. The results indicated that circ_0001052 was amplified solely by divergent primers in cDNA, whereas Hipk3 was amplified by convergent primers in both cDNA and gDNA (Figure 2B). After we have verified the circular features of circ_0001052, we went on to explore whether circ_0001052 regulated the expression of its homologous gene Hipk3. As was depicted, knockdown of circ_0001052 significantly reduced the expression of Hipk3 (Figure 2C). On the contrary, enhanced expression of circ_0001052 significantly augmented Hipk3 levels in Ang II-treated CMs (Figure 2D). Furthermore, we detected the function of Hipk3 in in vitro CH models. The depletion efficiency of Hipk3 was firstly verified through qRT-PCR (Figure 2E). Then, the results of the IF staining assay revealed that Hipk3 depletion significantly reduced cell surface area (Figure 2F). Also, the levels of ANF, BNP and β-MHC were remarkably decreased in response to the depletion of Hipk3 (Figure 2G and 2H). In brief, Hipk3 was positively modulated by circ_0001052 and promoted hypertrophic effects in CMs.
**Figure 2:** *Hipk3 was positively regulated by circ_0001052 and exerted pro-hypertrophic functions. (A) The levels of circ_0001052 and Hipk3. (B) The circular characteristics of circ_0001052 verified by Agarose gel electrophoresis (AGE). (C) The mRNA levels of Hipk3 when circ_0001052 was inhibited. (D) The overexpression efficiency of circ_0001052 and the influence of up-regulated circ_0001052 on Hipk3 expression. (E) Depletion efficiency of Hipk3. (F) Cell surface area under Hipk3 depletion was detected by IF staining assay. (G and H) Influence of silenced Hipk3 on the expression of hypertrophic markers. **P < 0.01. Abbreviation: n.s: no significance.*
## Circ_0001052 competes with Hipk3 to bind to miR-148a-3p and miR-124-3p
In this section, how circ_0001052 regulated Hipk3 was explored. To investigate the cellular location of circ_0001052 in Ang II-treated CMs, subcellular fraction and FISH experiments were carried out. Circ_0001052 was distributed in both nuclear and cytoplasmic fractions, with a greater percentage in the latter (Figure 3A). Next, the results of the Ago2-RIP test showed that Hipk3 and circ_0001052 were considerably enriched in the Ago2-assembled RNA-induced silencing complex (RISC) (Figure 3B), suggesting that they could be involved in a ceRNA network. In order to find common miRNAs that combine with both circ_0001052 and Hipk3, starBase database was searched [19]. As was illustrated in the Venn diagram, 15 miRNAs were revealed (Figure 3C). We conducted the RNA pull down assay using biotin-labeled Hipk3 to pull down these 15 miRNAs. It manifested that 5 miRNAs were pulled down in HL-1/Ang-II and 3 miRNAs were pulled down in PCM/Ang-II, with 2 miRNAs (miR-148a-3p and miR-124-3p) shared in both cells (Figure 3D). MiR-148a-3p and miR-124-3p levels were shown to have dramatically decreased in Ang II-induced CMs (Figure 3E). In addition, using miR-148a-3p/miR-124-3p mimics, we increased the expression of these two miRNAs (Figure 3F). As expected, increased levels of miR-148a-3p or miR-124-3p greatly hindered Hipk3 expression (Figure 3G). Moreover, the binding sequences of miR-148a-3p/miR-124-3p and circ_0001052/Hipk3 were obtained from starBase database, with the sequence of circ_0001052-Mut/Hipk3-Mut contained mutant binding sites showed as well (Figure 3H). It was discovered that the up-regulation of miR-148a-3p or miR-124-3p reduced the luciferase activity of circ_0001052-WT or Hipk3-WT but had no effect on the luciferase activity of circ_0001052-Mut or Hipk3-Mut (Figure 3I). Finally, it was certified that circ_0001052, miR-148a-3p, miR-124-3p and Hipk3 were all significantly enriched in RISC (Figure 3J). Therefore, through sponging miR-148a-3p and miR-124-3p, circ_0001052 acted as the ceRNA of Hipk3.
**Figure 3:** *Circ_0001052 competed with Hipk3 to interact with miR-148a-3p and miR-124-3p. (A) The subcellular location of circ_0001052. (B) RIP assay revealed the relative enrichment of circ_0001052 and Hipk3 in Ago2 or IgG group. (C) StarBase database predicted 15 miRNAs binding to both circ_0001052 and Hipk3. (D) RNA Pull down assay illustrated the relative enrichment of 15 candidate miRNAs pulled down by biotin labeled Hipk3. (E) The levels of miR-148a-3p/miR-124-3p in Ang II-treated CMs. (F) The overexpression efficiency of miR-148a-3p and miR-124-3p. (G) Influence of up-regulated miR-148a-3p or miR-124-3p on Hipk3 expression. (H) Binding sites between circ_0001052/Hipk3 and miR-148a-3p/miR-124-3p. (I) The luciferase activity of circ_0001052-WT/Mut or Hipk3-WT/Mut in response to the up-regulation of miR-148a-3p or miR-124-3p. (J) RIP assay tested the enrichment of circ_0001052, miR-148a-3p, miR-124-3p and Hipk3 in Ago2 or IgG group. **P < 0.01. Abbreviation: n.s: no significance.*
## Co-inhibition of miR-148a-3p and miR-124-3p partially rescues the effects of silenced circ_0001052 on Ang II-induced CMs
The rescue experiments were carried out to determine whether circ_0001052 affected CH via miR-148a-3p and miR-124-3p. Prior to that, the depletion efficacies of miR-148a-3p and miR-124-3p were validated for the following assays (Figure 4A). Intriguingly, both miR-148a-3p and miR-124-3p inhibition partially reversed the suppressive effects of silenced circ_0001052 on cell surface area (Figure 4B). Similarly, circ_0001052 deficiency-induced downregulation of hypertrophic markers was partly recovered by co-suppression of miR-148a-3p and miR-124-3p (Figure 4C–4E). We concluded that miR-148a-3p/miR-124-3p partially mediated the effects of circ_0001052, which indicated that circ_0001052 regulated Hipk3 in another way.
**Figure 4:** *MiR-148a-3p/miR-124-3p partially mediated the effects of circ_0001052 in CMs. (A) Depletion efficiency of miR-148a-3p and miR-124-3p. (B) The rescue effects of miR-148a-3p inhibition or together with miR-124-3p inhibition on the surface area of circ_0001052-silenced CMs. (C–E) The levels of hypertrophic biomarkers in indicated CMs. *P < 0.05, **P < 0.01.*
## Circ_0001052 recruites Srsf1 to stabilize Hipk3
CircRNAs indirectly regulate the level of target genes by binding to RNA-binding proteins (RBPs). Hence, we wondered if circ_0001052 recruited certain RBP to stabilize Hipk3. Firstly, we conducted RNA pull down assay to identify the potential RBP that bound to circ_0001052. According to the results of mass spectrometry, Srsf1 was significantly pulled down by Bio-circ_0001052 (Figure 5A). Also, Srsf1 was predicted to have the potential to bind with circ_0001052 based on starBase database. Next, we silenced Srsf1 in both Ang II-treated HL-1 and PCM cells (Figure 5B), and discovered that depletion of Srsf1 significantly reduced the expression of Hipk3 (Figure 5C). Next, the outcomes of RIP assays disclosed that both Hipk3 and circ_0001052 were obviously pulled down by anti-Srsf1 (Figure 5D). Then, we revealed that Srsf1 expression was not impacted by circ_0001052 depletion (Figure 5E). Also, silencing Srsf1 had no influence on circ_0001052 expression (Figure 5F). Further, it was revealed that when circ_0001052 was silenced, the enrichment of Hipk3 in anti-Srsf1 groups was significantly reduced in Ang II-treated CMs (Figure 5G). Of note, we validated that when circ_0001052 or Srsf1 was silenced, the resistance of Hipk3 to ActD treatment was significantly mitigated (Figure 5H). In summary, circ_0001052 served as the scaffold to recruit Srsf1, therefore stabilizing Hipk3 mRNA in CMs.
**Figure 5:** *Circ_0001052 recruited Srsf1 to stabilize Hipk3. (A) RNA pull down assay, mass spectrometry and western blot analyses revealed Srsf1 as the RBP for circ_0001052. (B) Depletion efficiency of Srsf1 in Ang-II induced CMs. (C) The expression of Hipk3 with Srsf1 depletion. (D) RIP assay followed by AGE revealed the enrichment of Hipk3 and circ_0001052 in anti-IgG or anti-Srsf1 group. (E) Influence of silenced circ_0001052 on Srsf1 expression. (F) The impact of silenced Srsf1 on circ_0001052 expression. (G) RIP assay revealed the enrichment of Hipk3 pulled down by anti-Srsf1 when circ_0001052 was silenced. (H) The mRNA level of Hipk3 under ActD treatment when circ_0001052 or Srsf1 was silenced. **P < 0.01. Abbreviation: n.s: no significance.*
## Circ_0001052 serves pro-hypertrophic effects via up-regulation of Hipk3
Finally, rescue assays were carried out to explore whether Hipk3 was required in circ_0001052-regulated CH. Expression level of Hipk3 was firstly verified (Figure 6A). Up-regulation of Hipk3 absolutely rescued circ_0001052 inhibition-mediated suppressive effects on cell surface area (Figure 6B). In the meantime, the repressive influence of silenced circ_0001052 on the expression of three hypertrophic markers was completely counteracted by up-regulated Hipk3 (Figure 6C–6E). All in all, elevated expression of Hipk3 completely rescued the anti-hypertrophic effects of silenced circ_0001052 on Ang II induced CMs.
**Figure 6:** *Circ_0001052 played a pro-hypertrophic part in CMs via targeting Hipk3. (A) Overexpression efficiency of Hipk3. (B) The cell surface area in circ_0001052-silenced CMs with up-regulated Hipk3. (C–E). The levels of hypertrophic biomarkers in CMs under diverse contexts. **P < 0.01.*
Based on all the findings in this work, we drew a conclusion that circ_0001052, a circRNA derived from Hipk3, boosted Hipk3 expression via sequestering miR-148a-3p/miR-124-3p and recruiting Srsf1, therefore facilitating hypertrophic phenotypes in CMs (Figure 7).
**Figure 7:** *Schematic abstract revealed the mechanism underlying circ_0001052 regulated Hipk3 in CMs.*
## DISCUSSION
CircRNAs generated from human HIPK3 (circ-HIPK3) have been shown to play important roles in cardiovascular disease. Circ-HIPK3, for example, exacerbates the effect of adrenaline on heart failure through modulating the miR-17-3p/ADCY6 axis [12]. Circ-HIPK3 contributes to the differentiation of myoblasts [20]. High glucose induces circ-HIPK3 downregulation in human umbilical vein endothelial cells [21]. Also, circRNA from Hipk3 (circ-Hipk3) was suggested to aggravate myocardial fibrosis via sponging miR-29b-3p [17]. Present study verified that a novel circRNA from Hipk3, circ_0001052, was considerably up-regulated in both in vivo and in vitro CH models. Silencing circ_0001052 impaired hypertrophic effects in Ang II-induced CMs.
Mounting evidence has indicated that circRNAs have the potential to regulate their homologous genes. For instance, circRNA-ENO1 elevates ENO1 expression to enhance glycolysis and tumor progression in lung adenocarcinoma [22]. CircABCC2 modulates ABCC2 expression via endogenously sponging miR-665 [23]. CircRUNX2 elevates RUNX2 expression and attenuates osteoporosis by sponging miR-203 [24]. In the present study, we consistently revealed that circ_0001052 positively regulated its host gene Hipk3. Also, Hipk3 exerted pro-hypertrophic effects in Ang II-induced CMs.
Previous studies have largely proposed the ceRNA pattern as a potential way for circRNAs to modulate their host genes [23]. In this work, we uncovered that both circ_0001052 and Hipk3 were abundantly enriched in the RISC. Further, miR-148a-3p and miR-124-3p were validated as the shared miRNAs by both circ_0001052 and Hipk3. Former reports indicated that miR-148a-3p suppresses IKBKB to inactivate NF-κB signaling in human aortic valve cells [25]. MiR-148b-3p is directly associated with Epicardial adipose tissue [26]. MiR-124-3p is a survival-predictor for patients with cardiac arrest out of hospital [27]. LncRNA ROR modulates ischaemia reperfusion injury-induced inflammatory response in human CMs via sponging miR-124-3p and up-regulating TRAF6 [28]. Besides, the ceRNA networks involving circRNAs and miR-148a-3p or miR-124-3p have also been reported in several previous studies. CircANKS1B serves as the ceRNA of USF1 by sponging miR-148a-3p and miR-152-3p [29]. CircRNA_005186 is a sponge for miR-124-3p and regulates the expression of Epha2 [30]. The present study indicated that miR-148a-3p and miR-124-3p were involved in the regulation of circ_0001052 on Hipk3. Intriguingly, the co-inhibition of miR-148a-3p and miR-124-3p partially rescued the effects of silenced circ_0001052 on the hypertrophic phenotypes of Ang II-induced CMs.
As supported by numerous researches, circRNAs can also interact with RBP to promote or inhibit the expression of protein-coding genes. For instance, circ-PABPN1 binds to HuR protein and prevents the binding of HuR to PABPN1 mRNA, thus reducing PABPN1 translation [31]. CircRNA ZKSCAN1 interacts with FMRP to prevent the binding of FMRP with CCAR1 complex in hepatocellular carcinoma [32]. Circ-HuR interacts with CNBP protein to restrain its binding to HuR promoter in gastric cancer cells [33]. In present study, we uncovered that circ_0001052 served as the scaffold of Srsf1 to stabilize Hipk3. Srsf1 was reported to increase the stability of mRNAs. CircRNA SMARCA5 binds to SRSF1 protein to modulate VEGFA mRNA splicing in glioblastoma multiforme [34]. Also, SRSF1 could protect DBF4B from DNA damage via modulation on pre-mRNA splicing [35]. Current study revealed that circ_0001052 recruited Srsf1 protein to stabilize Hipk3 mRNA and therefore elevate Hipk3 expression. Of note, we proved that Hipk3 overexpression completely offset the impact of silenced circ_0001052 on Ang II-induced CMs.
Although we have fully demonstrated that circ_0001052 promoted cardiac hypertrophy, there are still some limitations in our study. Firstly, the evidence from animal experiments is insufficient, and there is a lack of histological and morphological evidence. Secondly, we have only confirmed that circ_0001052 promoted cardiac hypertrophy through the ceRNA mechanism. The deeper mechanism remains unknown, such as the effect of circ_0001052 on cell signaling pathways and the cell metabolic cycle.
In conclusion, present study uncovered that circ_0001052 sponged miR-148a-3p/miR-124-3p and recruited Srsf1 protein to boost Hipk3 level, finally aggravating hypertrophic phenotypes in CMs. Of importance, these findings indicated circ_0001052 as a putative biomarker for CH treatment.
## Animal study
This animal study protocol was approved and supervised by the Animal Ethics Committee of the First Hospital of China Medical University. C57BL6 mice (male; 8 weeks) were used to create an in vivo CH model via transverse aortic constriction (TAC). Mice in the sham group were experienced the same as the TAC group, except aorta seam.
## Cell culture and treatments
The isolated primary CMs (PCM) and mouse CMs HL-1 (ATCC; Manassas, VA, USA) were cultured in DMEM (Gibco, Grand Island, NY, USA) with $10\%$ FBS (Gibco) and $1\%$ penicillin/streptomycin (Gibco) at 37°C with $5\%$ CO2. 1 mmol/L of angiotensin II (Ang-II; Sigma-Aldrich) was used to establish an in vitro CH model.
## Quantitative real-time PCR (qRT-PCR)
Cells were treated with Trizol reagent (Invitrogen, Carlsbad, CA, USA) to extract total RNA, then total RNA was reverse-transcribed into cDNA by the Reverse Transcription Kit (Invitrogen). SYBR-Green Real-Time PCR Systems (Invitrogen) were used to progress qRT-PCR. Using GAPDH/U6 as the endogenous control, relative RNA expression was evaluated.
## Western blot
Cells were treated with RIPA lysis buffer (Beyotime, Shanghai) to extract the total protein, which was then separated by $12\%$ SDS-PAGE (Bio-Rad, Hercules, CA, USA) and transferred to PVDF membranes (Millipore, Bedford, MA, USA). Membranes were then interacted with the matching primary antibodies (Abcam, Cambridge, MA, USA) for ANP (ab225844), BNP (ab236101), β-MHC (ab172967), Srsf1 (ab129108), Hipk3 (ab72538), and the loading control GAPDH(ab181602) for the duration of the night at 4°C. Later, for a total of two hours, secondary antibodies (ab205718, Abcam) were added. All the primary antibodies were diluted 1000 times, and the secondary antibodies 10,000 times.
## Immunofluorescence (IF) staining
CMs were washed twice using phosphate-buffered saline (PBS; Sigma-Aldrich), followed by fixing in $4\%$ paraformaldehyde (PFA; Sigma-Aldrich) for 20 min and washing thrice in PBS. Then CMs were blocked with $1\%$ bovine serum albumin (BSA; Sigma-Aldrich). Sequentially, cells were treated with anti-actin (ab7817; Abcam) and secondary antibody (ab150117, Abcam). It was decided to use DAPI (Sigma-Aldrich) for nucleus labeling. A fluorescent microscope was used to measure the size of the cell surface (Zeiss, Jena, Germany).
## Plasmid transfection
The particular shRNAs for the genes Hipk3, Srsf1, and circ_0001052, as well as their corresponding control shRNAs, were purchased from GenePharma (Shanghai, China). Besides, we got the pcDNA3.1 vector targeting Hipk3 and the pcDNA3.1 (+) CircRNA Mini Vector targeting circ_0001052 from GenePharma. Genechem created the miR-148a-3p mimics/inhibitors and the miR-124-3p mimics/inhibitors (Shanghai, China). These plasmids were appropriately transfected into CMs using Lipofectamine 2000 (Invitrogen).
## Subcellular fraction assay
The nuclear and cytoplasmic fractions of CMs were obtained by using Nuclear/cytoplasmic fractionation PARIS Kit (Thermo Fisher Scientific, Waltham, MA, USA). Then qRT-PCR was carried out to assess the concentration of circ_0001052 in various fractions, using GAPDH and U6 as fractionation markers.
## FISH assay
The specific probe of circ_0001052 was designed by Ribobio (Guangzhou, China) and used as per the direction. The fixed CMs were dehydrated and cultivated with FISH-probe in hybridization buffer. Following DAPI staining, samples were examined under a fluorescence microscope (Zeiss). The sequence of circ_0001052 probe was as follow: 5′-GAGGCCAUACCUGUAGUAGCG-3′.
## RNA immunoprecipitation (RIP)
RIP assay was carried out in CMs according to the instructions of the Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore). Anti-Ago2 antibody (03-110, Sigma-Aldrich) or a control anti-IgG antibody (ab190475, Abcam) were used for immunoprecipitation, which was then completed with the addition of magnetic beads. The precipitated RNAs were ultimately examined using qRT-PCR.
## RNA pull down assay
Hipk3 or circ_0001052 biotinylated RNA probes were incubated with the lysates of CMs, and then magnetic beads were added and incubated for an additional hour. The RNAs or proteins in pull-downs were analyzed using qRT-PCR or western blotting.
## Luciferase reporter assay
Using pmirGLO dual-luciferase vectors (Promega, Madison, WI, USA), the Hipk3 3′UTR or circ_0001052 fragments encompassing the miR-148a-3p or miR-124-3p target sequences (wild-type or mutant) were used to create Hipk3-WT/MUT or circ_0001052-WT/MUT. MiR-148a-3p, miR-124-3p, or NC mimics were co-transfected into CMs with the obtained constructs. Dual luciferase reporter assay system was used to check the luciferase activity.
## Statistical analyses
The mean and standard deviation of the data were displayed. With $p \leq 0.05$ serving as the significant level, differences between groups were examined using the Student’s t test or one-way ANOVA. Experiments were implemented at least 3 times.
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|
---
title: Effects of magnetically targeted iron oxide@polydopamine-labeled human umbilical
cord mesenchymal stem cells in cerebral infarction in mice
authors:
- Jun Yan
- Te Liu
- Yang Li
- Jun Zhang
- Bo Shi
- Fuqiang Zhang
- Xuejia Hou
- Xiaowen Zhang
- Wanxing Cui
- Jing Li
- Hua Yao
- Xiuying Li
- Yufei Gao
- Jinlan Jiang
journal: Aging (Albany NY)
year: 2023
pmcid: PMC10008509
doi: 10.18632/aging.204540
license: CC BY 3.0
---
# Effects of magnetically targeted iron oxide@polydopamine-labeled human umbilical cord mesenchymal stem cells in cerebral infarction in mice
## Abstract
Mesenchymal stem cells are a potential therapeutic candidate for cerebral infarction due to their anti-inflammatory proprieties. However, ensuring the engraftment of sufficient cells into the affected brain area remains a challenge. Herein, magnetic targeting techniques were used for the transplantation of a large number of cells noninvasively. Mice subjected to pMCAO surgery were administered MSCs labeled or not with iron oxide@polydopamine nanoparticles by tail vein injection. Iron oxide@polydopamine particles were characterized by transmission electron microscopy, and labeled MSCs were characterized by flow cytometry and their differentiation potential was assessed in vitro. Following the systemic injection of iron oxide@polydopamine-labeled MSCs into pMCAO-induced mices, magnetic navigation increased the MSCs localization to the brain lesion site and reduced the lesion volume. Treatment with iron oxide@polydopamine-labeled MSCs also significantly inhibited M1 microglia polarization and increased M2 microglia cell infiltration. Furthermore, western blotting and immunohistochemical analysis demonstrated that microtubule-associated protein 2 and NeuN levels were upregulated the brain tissue of mice treated with iron oxide@polydopamine-labeled MSCs. Thus, iron oxide@polydopamine-labeled MSCs attenuated brain injury and protected neurons by preventing pro-inflammatory microglia activation. Overall, the proposed iron oxide@polydopamine-labeled MSCs approach may overcome the major drawback of the conventional MSCs therapy for the treatment of cerebral infarction.
## INTRODUCTION
In the last decade, cerebral infarction become the second leading cause of adult death and long-term disability worldwide, in particular in developing countries, which imposes a heavy financial burden on the affected individual as well as on the society [1–4]. Blood-vessel occlusion and subsequent neuronal damage is a main pathological event associated with cerebral infarction, of which neuroinflammation is a major consequence [2, 4]. Neuroinflammation, as a key mechanism behind secondary injury of cerebral infarction, is caused by dead cells and debris due to the infarction injury [5], and is characterized by microglia-induced peripheral leukocyte influx into the brain parenchyma and the release of proinflammatory cytokines. Indeed, microglia, which have long been considered one of the earliest and important participants in neuroinflammation of the central nervous system, is considered a critical factor in the inflammatory response after cerebral infarction [6, 7]. Altogether these events are detrimental to trigger and support a pro-inflammatory status in the brain microenvironment [8–10]. Therefore, investigations on the regulatory mechanisms of the inflammatory response during cerebral infarction has received more attention in recent years.
Mesenchymal stem cells (MSCs) are believed to hold therapeutic potential to ameliorate the effects of cerebral infarction owing to their multifaceted functions, such as secretion of numerous trophic factors that can modulate inflammation and angiogenesis [11], apoptosis [12, 13], and the immune response [12, 14–19]. It is generally believed that the ability of MSCs to target brain lesions and the number of functional MSCs transplanted can determine their therapeutic effectiveness [20]. The intracerebral implantation of cells into the infarcted brain is invasive and may cause additional damage to the healthy tissues. Intravenous administration is simpler and less invasive in comparison with direct implantation; however, only a small proportion of MSCs transplanted intravenously can effectively reach the brain lesion site in vivo, which hampers the clinical use of MSCs for treatment of cerebral infarction. Several strategies have been applied to enhance the migration and maintain the function of these cells at targeted sites. For example, genetic modifications have been used to overexpress receptors that recognize chemoattractants and promote cell migration, but only few of the injected MSCs are delivered to its targets [21]. Nonetheless, the migration efficiency of MSCs in vivo remains is unsatisfactory. Therefore, enhancing the homing strategy of MSCs to the ischemic brain may help enhance the treatment outcome. Magnetic iron oxide nanoparticles (MIONs) are a conventional magnetic resonance imaging contrast agent that holds application value for MSCs labeling. MIONs are approved for clinical use due to their pronounced biocompatibility, and have garnered increased attention because of their unique response features to external magnetic fields. Polydopamine (PDA) is highly biocompatible and biodegradable, and therefore, it is widely used to coat nanoparticles for numerous biomedical applications. Hence, PDA-capped Fe3O4 (MIONs@PDA) and their composites are among the safest nanomaterials used for clinical diagnosis and therapy. To obtain MSCs with a suitable targeting ability, magnetic MSCs were prepared using MIONs@PDA-coated MSCs (MIONs@PDA-MSCs). These nanoparticles contain Fe3O4 that mediates their magnetic navigation to the target brain infarction lesion with the assistance of an external magnetic field (MF).
The present study describes an animal model of permanent middle cerebral artery occlusion (pMCAO) that was established to explore the effects of MIONs@PDA-MSCs(MF) on cerebral infarction and assess the impact of MSCs in microglia activation and inflammation.
## Preparation of MIONs and transmission electron microscopy (TEM) analysis
MIONs were synthesized by the thermal decomposition method as previously described [22]. Briefly, the Fe3O4 nanoparticles were injected into sodium dodecyl sulfate (SDS, $99\%$), which was heated to obtain SDS-capped Fe3O4 super particles. Next, the separation of oleic acid stable nanoparticles was achieved using a magnet. The capped Fe3O4 nanoparticles were dispersed in Tris buffer (10 nM, pH = 8.5), which contained 6 mg/mL PDA aqueous solution and stirred for 3 h. The obtained MIONs@PDA were detected using an H-800 transmission electron microscope (Hitachi Ltd., Tokyo, Japan) with a charge coupled device camera.
## Culture, expansion, identification, and MIONs-labeling of human umbilical cord MSCs (HUMSCs)
Briefly, HUMSCs were obtained after normal deliveries following 38–40 week gestations. HUMSCs were cultured in Dulbecco’s modified *Eagle medium* (Gibco, Waltham, MA, USA) containing $10\%$ fetal bovine serum (Gibco) and negative for mycoplasma contamination. All experiments were conducted using HUMSCs at passages 5–8. After reaching $80\%$ confluency, various concentrations of MIONs@PDA (0, 10, 25, 50, 100, and 200 μg/mL) were added to the medium for 12 h. The Cell Counting Kit-8 (Sigma-Aldrich, St. Louis, MO, USA) assay was used to detect the cytotoxicity of MIONs@PDA. HUMSCs were stained with the Prussian blue iron staining kit (Solarbio, Beijing, China), according to the manufacturer’s instructions. The phenotype of HUMSCs was confirmed by the expression of surface markers (positive for CD44-fluorescein isothiocyanate (FITC) and CD105-phycoerythrin (PE), and negative for CD45-FITC) (all from BD Biosciences, San Jose, CA, USA) using a FACSC anto II flow cytometer (FC500; Beckman Coulter Brea, CA, USA), and the data were analyzed with the CXP software (Beckman Coulter). Differentiation capacity of the HUMSCs was assessed by inducing osteocyte and adipocyte differentiation using the StemPro Osteogenesis/Adipogenesis Differentiation Kit (Invitrogen, Waltham, MA, USA) and evaluated by Alizarin Red S, Oil red O, and Alcian blue staining, respectively.
## Mouse treatment and transplanted HUMSCs
Male C57/BL6 mice were purchased from the Beijing Weitong Lihua Experimental Animal Technology Co. (Beijing, China) and were housed in the animal facilities of the animal center of the College of Basic Medical Sciences, Jilin University, China.
The mice were anesthetized with $1.5\%$ isoflurane (RWD Life Science, Shenzhen, China). Through a midline skin incision, the right common carotid artery (CCA), external carotid artery, and internal carotid artery (ICA) were isolated and ligated. Monofilament nylon suture was inserted from the right CCA to the ICA through a small incision in the CCA, and then advanced to the Circle of Willis to occlude the origin of the right middle cerebral artery. Subsequently, a silk suture was then tightened around the right common carotid artery stumps and nylon filament and then sutured the skin incision. Sham-operated mice underwent the same procedures except for the pMCAO. Behavioral evaluations of the mice were performed 24 h after surgery, using the Bederson 4-point rating scale scored as: 0, no deficit; 1, failing to stretch right forepaw during tail suspension test; 2, decreasing ability of forelimb resistance to contralateral thrust; and 3, circling to the right after holding the tail. The standard PMCAO model was defined as a Bederson scale score >1 point, and animals that did not meet this criterion were excluded from the study [23]. The rats were randomly divided into five groups ($$n = 5$$ in each group): non-treated sham, pMCAO surgery, and pMCAO treated with HUMSCs, MIONs@PDA-MSCs, or MIONs@PDA-MSCs(MF). A total of 5 × 105 HUMSCs were injected through the tail vein 24 h after the surgery. The non-treated groups were given equal volume of phosphate-buffered saline (PBS).
## Calculation of infarct volume
Frozen sections were made every 2 mm along the sagittal axis of the brain. Then, TTC (2,3,5-triphenyl-2H-tetrazoliuM chloride) staining of tissue sections was carried out in a conventional manner. The percent infarct (mm2) was measured as follows: %infarct = [(VC − VL)/VC] × 100, where VC and VL represent the volume of the control hemisphere and the noninfarcted tissue in the lesioned hemisphere, respectively.
## Near-infrared fluorescence (NIRF) imaging
CM-Dil-labeled HUMSC were used for NIRF imaging. For the targeting study, 12 h and 5 d after intravenous HUMSCs administration, the mice were anesthetized and euthanized. An IVIS Spectrum imaging system (PerkinElmer, Waltham, MA, USA) was employed to capture the NIRF images, and the CM-Dil-related fluorescent signals were discriminated using the Living Image software (PerkinElmer).
## Histopathological, immunohistochemical (IHC), and immunofluorescence evaluation of brain tissues
The mice were decapitated and their organs (heart, liver, spleen, lung, kidney, and brain) were fixed in $4\%$ paraformaldehyde overnight at 4° C and embedded in paraffin. The organs were sectioned into 5 μm thick pieces and partial dewaxing was immediately performed with xylene (5 mm tissue) followed by washing using a graded ethanol series ($100\%$, $95\%$, $80\%$, and $75\%$ diluted in distilled water). For histopathological examination, the samples were stained with hematoxylin (2 g/L) for 5 min and eosin ($1\%$) for 2 min before washing with distilled water. For IHC analysis, the paraffin sections were blocked for 1 h and then incubated with antibodies against microtubule-associated protein 2 (MAP2) and NeuN (both at 1:200; ProteinTech, Chicago, IL, USA) at 4° C, overnight. After washing, the sections were incubated with a biotin-labeled secondary antibody and streptavidin-peroxidase for 30 min. Color development was achieved upon incubation with diaminobenzidine (MaiXin, Fuzhou, China), after which hematoxylin staining, dehydration, and neutral resin mounting were performed. For immunofluorescence, the sections were incubated with a fluorescent-dye-conjugated secondary antibody. Next, dehydration was carried out with absolute ethanol, and the tissue was sealed with a neutral resin. Images were collected at ×200 amplification with a microscope (Olympus Corporation, Tokyo, Japan).
## Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting
Total RNA from the tissues corresponding to the lesion region was extracted using the Trizol reagent (Life Technologies, Waltham, MA, USA) and cDNA synthesis was performed using Reverse Transcriptase II (Invitrogen) according to the manufacturer’s instructions. QRT-PCR reactions were carried out in an ABI 7500 system in 10 μL reactions, with 1 μL cDNA samples and SYBR Premix ExTaq (TaKaRa, Kusatsu, Japan). Relative mRNA expression was calculated and analyzed using the comparative 2-∆∆Ct method. All experiments were performed independently at least three times. The primers used were the following: TNF-α, (FW) 3′–CCCCAGTCTGTATCCTTCTA–5′ and (RV) 3′–CACTGTCCCAGCATCTTGT; IL-1β, (FW) 3′–AAGGGCTGCTTCCAAAC–5′ and (RV) 3′–TGTGCTGCTGCGAGATT–5′; IL-6, (FW) 3′–TACCACTCCCAACAGACC–5′ and (RV) 3′–TTTCCACGATTTCCCAGA–5′; β-actin, (FW) 3′–ATGTGGATCAGCAAGCAGGA–5′ and (RV) 3′–AAGGGTGTAAAACGCAGCTCA–5′; CD206, (FW) 3′–GCCGTCTGTGCATTTCCATTCAAG–5′ and (RV) 3′–TTTGTCGTAGTCAGTGGTGGTTCC–5′; ARG1, (FW) 3′–GTGAGAGACCACGGGGACCTG–5′ and (RV) 3′–CCACACCAGCCAGCTCTTCATTG–5′; iNOS, (FW) 3′–ACAGGAACCTACCAGCTCACTCTG–5′ and (RV) 3′–ACCACTGGATCCTGCCGATGC–5′; IL-10, (FW) 3′–CTGCTATGCTGCCTGCTCTTACTG–5′ and (RV) 3′–TGGGAAGTGGGTGCAGTTATTGTC–5′; TGF-β, (FW) 3′–ACTTGCACCACGTTGGACTTCG–5′ and (RV) 3′–TGGGTCATCACCGATGGCTCAG–5′; MAP2, (FW) AAGGCACCTCACTGGACCTCAG–5′ and (RV) ACCCTCTTCATCCTCCCTGTATGG–5′; NeuN, (FW) 3′–AGACAGACGAGGCGGCACAG–5′ and (RV) 3′–AGGGGATGTTGGAGACGTGTAGC–5′.
For western blot, equal amounts of protein were extracted using RIPA buffer (Sigma-Aldrich) and separated in SDS polyacrylamide gel. After electrophoresis, the proteins were transferred onto polyvinylidene difluoride membranes, which were then blocked with milk for 1 h. Afterwards, the proteins were labeled with the following primary antibodies overnight at 4° C: anti-MAP2, anti-CD206, anti-CD11b, anti-IBA-1, anti-β-actin (ProteinTech, Chicago, IL, USA) and anti-NeuN (Cell Signaling Technology, Danvers, MA, USA). After washing, the membranes were incubated for 1 h with a fluorescently labeled secondary antibody (1:5,000; Thermo Fisher Scientific, Waltham, MA, USA). β-actin was used as internal reference. The labeled proteins were observed using Odyssey (LI-COR Biosciences, Lincoln, NE, USA). ImageJ software (National Institutes of Health, Bethesda, MD, USA) was used for quantitative analysis of the protein bands.
## Statistical analysis
Statistical analyses were conducted using SPSS software v.16 (SPSS Inc., Chicago, IL, USA) and analysis of variance was used. All results were considered significant at p ≤ 0.05 and expressed as mean ± standard deviation (SD, $$n = 6$$). Image analysis was performed using GraphPad Prism v.6 (GraphPad Software, San Diego, CA, USA).
## Characterization and toxicity of MIONs@PDA
Nanoparticles larger than 100 nm can scarcely penetrate cells by cellular phagocytosis. TEM images showed that the average diameter of MIONs was about 45–50 nm, which slightly increased to 50–60 nm after evenly encapsulated within the PDA, with MIONs@PDA being appropriately sized nanoparticles for labeling cells (Figure 1A). Viability experiments indicated that different concentrations of MIONs@PDA had a small negative effect on HUMSCs (Figure 1B). To investigate the internalization potential of MIONs@PDA by HUMSCs, Prussian blue staining was performed (Figure 1C), revealing that 50 μg/mL MIONs@PDA efficiently induced blue-stained deposits and cell labeling.
**Figure 1:** *Characterization, viability, and internalization potential of polydopamine-capped Fe3O4 nanoparticles (MIONs@PDA). (A) Transmission electron microscopy imaging of MIONs@PDA. Scale bar = 50 nm. (B) Proliferation of human umbilical cord mesenchymal stem cells (HUMSCs) labeled with MIONs@PDA at concentrations of 0, 6.25, 12.5, 25, 50, 100, 150, and 200 μg/mL by Cell Counting Kit-8 assay. (C) Morphology of HUMSCs labeled with the MIONs@PDA at concentrations of 0, 25, 50, 75, 100, and 150 μg/mL. Scale bars = 100 μm.*
## Characteristics of HUMSCs labeled with MIONs@PDA
To determine whether the MIONs@PDA-MSCs did not lose differentiation potential, control HUMSCs and MIONs@PDA-MSCs were subjected to a differentiation assay. Von Kossa, Oil red O, and Alcian blue staining confirmed that both MIONs@PDA-labeled and unlabeled HUMSCs maintained their differentiation potential into osteocyte and adipocyte, respectively. Flow cytometry analysis showed that cultured HUMSCs and MIONs@PDA-MSCs highly expressed the cell surface markers of typical MSCs (cluster of differentiation CD105: 95.8 ± $1.7\%$, CD44: 100 ± $0.1\%$) but not hematopoietic cell markers (CD45: 0.1 ± $0.1\%$) (Figure 2A). Differences noted between the control and labeled cells were without statistical significance. These results indicated that the MIONs do not affect the characteristics of the HUMSCs. Therefore, these cells were used in the subsequent experiments (Figure 2B).
**Figure 2:** *Characterization of human umbilical cord mesenchymal stem cells (HUMSCs) labeled or not with polydopamine-capped Fe3O4 nanoparticles (MIONs@PDA). (A) Similar to normal HUMSCs, MIONs@PDA-MSCs highly expressed the typical surface markers CD44 and CD90, but not the hematopoietic cell marker CD45. (B) Osteocyte and adipocyte differentiation of MIONs@PDA-MSCs vs. control HUMSCs. All cells exhibited adipogenic and osteogenic differentiation potential similar to that of control HUMSCs. Scale bars = 50 μm.*
## Ability of HUMSCs to target the lesion region and reduce the volume of the infarct zone
To evaluate the therapeutic efficacy of transplanted HUMSCs at 5 d post-transplantation, a pMCAO in vivo model was achieved through surgery and infarct areas were confirmed based on TTC-stained brain sections. In this well-established animal model, the cortex mas the mainly affected region (Figure 3A). The percentage of infarct zone was significantly lower in all HUMSCs-transplanted mice than in the PBS group, and was significantly lower in mice treated with MIONs@PDA-MSCs(MF) group than in the other two groups. There was no significant difference between the HUMSCs and MIONs@PDA-MSCs treated groups (Figure 3B, 3C). For fluorescence imaging in vivo, MSCs were labeled with cm-dil before injection. Fluorescence imaging at 12 h after injection showed that in the absence of MF (MF-), a small amount of MSCs accumulated in the brain, whereas in the presence of MF, a large number of cells targeted the brain tissue. After 5 days, the fluorescence content in brain tissue of the magnetic target group was significantly higher than that of the non-MF group (Figure 3D, 3E).
**Figure 3:** *Effects of HUMSCs on infarct volume and behavioral improvement. (A) Representative brain slices with infarction volume shown by TTC staining. (B, C) HUMSCs treatment significantly reduced infarct volume. (D, E) Bio-distribution of the MSCs following their intravenous injection into the pMCAO-induced mice with or without the MF, evaluated by the IVIS imaging of major organs. Data are presented as the means ± standard deviation. HUMSC, human umbilical cord mesenchymal stem cell; MIONs@PDA-MSCs, HUMSCs labeled with polydopamine-capped Fe3O4 nanoparticles; MIONs@PDA-MSC(MF), MIONs@PDA-MSCs with external magnetic field; PBS, middle cerebral artery occlusion with phosphate-buffered saline administration; Sham, sham operation; TTC, 2,3,5-triphenyl-2H-tetrazoliuM chloride. *p < 0.05 vs. Sham group, #p <0 .05 vs. PBS group, &p <0 .05 vs. HUMSCs group, $p <0 .05 vs. MIONs@PDA-MSCs group.*
## Histopathological changes induced by HUMSCs
To evaluate the potential toxicity of the nanoparticles, hematoxylin and eosin staining showed no noticeable morphological changes in the heart, liver, spleen, lungs, and kidney in both treatment groups compared with the control group after 5 days of HUMSCs therapy, further indicating the low toxicity of the MIONs in vivo. As shown in Figure 4, cortical neurons and glial cells in brain tissues of the sham group were normally arranged and had normal structure. The cortical cells in the PBS group were disorganized, showing large areas of necrotic neurons, and glial cells proliferated significantly around the necrotic foci, with slight proliferation of small blood vessels. The neurons showed different degrees of ischemic changes, with the cytoplasm of the swollen neurons being tinged and having damaged membranes, whereas the shrunken neurons were deeply stained, with small and triangular cell bodies. The gap around the nerve cells and glial cells was widened, and the neurons were fixed and contracted. In comparison to the PBS group, neuronal and glial cell necrosis, cell membrane and cell structure destruction were significantly reduced after HUMSCs transplantation, and new small blood vessels were observed. In particular, most HUMSCs were magnetically targeted to the infarction area, where more residual neurons were and scattered glial cell proliferation was active.
**Figure 4:** *Histopathological changes observed in different organs of mice in each group. (A) The organs of each group were stained with hematoxylin and eosin. scale bars = 100 μm. (B) Pathological changes in cortical tissue. scale bars = 50 μm.*
## Stem cell differentiation potential
To verify whether HUMSCs could differentiate into neural cells, tissue slices are stained with different cells type markers: NeuN for neurons, GFAP for neurogliocyte and Nestin for neural stem cells. The results showed no overlap between CM-Dil+HUMSCs and NeuN+, GFAP+, or Nestin+ cells (Figure 5).
**Figure 5:** *Human umbilical cord mesenchymal cells (HUMSCS) differentiate into neural cells. Immunofluorescence analysis showed no overlap between CM-Dil+HUMSCs and neurons (NeuN+), neurogliocyte (GFAP+), or neural stem cells (Nestin+). scale bars = 20 μm.*
## Anti-inflammatory and neuroprotective effects of HUMSCs are promoted by the transition of microglia from M1 to M2 phenotype
Next, it was investigated whether HUMSCs could exert enhanced anti-inflammatory effects in vivo. Immunofluorescence staining showed that Iba1+ microglia was increased in the cortex (infarct region) of mice treated with PBS. In comparison, HUMSCs inhibited pMCAO-induced microglia activation and MIONs@PDA-MSCs(MF) also revealed a clear inhibition (Figure 6A). qRT-PCR, immunofluorescence, and western blotting analysis of the brain following pMCAO were performed to study the effect of HUMSCs on pro-inflammatory and cytotoxic (M1), and anti-inflammatory and regenerative (M2) states (Figure 6). The mRNA expression of M1 macrophage markers (inducible nitric oxide synthase (iNOS), interleukin (IL)-1β, and tumor necrosis factor (TNF-α)), as well as M2 macrophage markers (arginase 1 (Arg-1), cluster of differentiation 206 (CD206), and IL-10) were evaluated (Figure 6C). The PBS group exhibited highly upregulated expression of the M1 markers, whereas the HUMSCs treatment significantly downregulated these genes, especially in the MIONs@PDA-MSCs(MF) group. In turn the M2 markers levels were considerably increased upon HUMSCs treatment. Western blotting and immunofluorescence analysis of M1 macrophage markers confirmed that the levels of CD11b and iNOS were increased after pMCAO, but reduced after HUMSCs transplantation, especially in the MIONs@PDA-MSCs(MF) group. The levels of M2 macrophage markers CD206 and Arg-1 were found to be decreased after pMCAO, but increased after HUMSCs transplantation, especially in the MIONs@PDA-MSCs(MF) group. These results suggest that HUMSCs can affect microglial phenotype transition to promote neurological functional recovery after pMCAO.
**Figure 6:** *In vivo anti-inflammatory effects of the HUMSCs and MIONs@PDA-MSCs. (A) Immunofluorescence analysis and quantification for M1 (CD11b) and M2 (CD206) macrophage markers in the cortex tissues of mice. scale bars=20 μm. (B) Western blot analysis and quantification of M1 (iNOS) and M2 (Arg-1) macrophage markers in the cortex tissues of mice. (C) Relative expressions of M1 (IL-6, IL-1β and CD11b) and M2 (ARG-1, IL-10 and CD206) genes in the brain after treatment. *p < 0.05 vs. Sham group, #p <0 .05 vs. PBS group, &p <0 .05 vs. MSCs group, $p <0 .05 vs. MIONs@PDA-MSCs group.*
The effects of HUMSCs on cortical neurons survival were then investigated. The results showed that after pMCAO treatment, neuronal activity was significantly decreased and HUMSCs transplantation significantly improved neuronal survival, especially when more cells were targeted at the infarct site (Figure 7).
**Figure 7:** *HUMSCs exert neuroprotective effects in vivo. (A) Immunofluorescence analysis, (B) relative mRNA expression, and (C) western blot analysis of neuronal markers (MAP2 and NeuN) in the cortex tissues of mice. *p < 0.05 vs. Sham group, #p <0 .05 vs. PBS group, &p <0 .05 vs. MSCs group, $p <0 .05 vs. MIONs@PDA-MSCs group. scale bars = 20 μm.*
## DISCUSSION
MSCs were shown to protect several organs from damage and have been proposed as a promising strategy for patients with cerebral infarction who do not respond to other therapeutic strategies. Transplanted MSCs can migrate to the injury site and mediate tissue regeneration, primarily by the delivery of trophic and paracrine factors. As a result, in vivo persistence and secretory functions of transplanted MSCs are critical to the therapeutic outcome. Nevertheless, only few MSCs can effectively migrate to and engraft into the brain, with <$0.001\%$ of total administrated cells being able to survive and migrate to the infarcted cortex. Moreover, MSCs administrated to rodent models showed a higher mortality rate due to their tendency to adhere and aggregate, leading to capillary blockage [24]. Therefore, the main goal of this study was to improve the ability of stem cells to target and repair the site of cerebral infarction in mice.
To obtain MSCs with a good targeting capability, magnetic nanoparticles were prepared using PDA-capped Fe3O4. Since the 1970s, MIONs have been widely studied in the field of biomedicine, and their magnetic targeting properties have been developed for many biophysical and medical applications [25]. For example, MIONs bind to proteins, nucleotides, viral vectors, and immune and stem cells, and remotely control the distribution of drug molecules and cells in the body through MFs, to accumulate in target tissues [26, 27]. Indeed, MIONs, such as ferumoxytol, have been approved by the Food and Drug Administration for the treatment of iron-deficiency anemia. The high aggregation of Fe3O4 nanoparticles often impedes their uptake by cells [28]. Therefore, the present study used PDA encapsulation to reduce Fe3O4 aggregation and increase the nanoparticle uptake by MSCs. The results indicated that the PDA shell reduces the toxicity of the nanoparticles and greatly improved its superparamagnetism (the physiological stability and biocompatibility of Fe3O4 with the nucleus) [29]. Moreover, the collected data showed that 50 g/mL MIONs had minimal toxicity and good labeling effect on HUMSCs, without changing the characteristics and differentiation potential of the cells. HUMSCs internalized MIONs and mediated their magnetic navigation to the target infarction lesion in the brain with the assistance of an external MF. In this study, after more cells were targeted to the lesion site, the cerebral infarction volume was significantly reduced, and the pathological tissue was significantly repaired. In addition, the activity of glial cells was inhibited after MIONs@PDA-MSCs(MF) transplantation, and the survival activity of neurons was significantly improved in the magnetically targeted brain tissue. Therefore, MIONs@PDA-MSCs(MF) has great potential to repair cerebral infarction. However, MIONs@PDA-MSCs(MF) transplanted into mice were unable to differentiate into nerve cells; thus, HUMSCs may function through other mechanisms.
Microglia, as the resident immune cells of the central nervous system, can dynamically exist in surveillance (M0), pro-inflammatory (M1), and anti-inflammatory (M2) states [30–32]. Microglia are the first cells that respond to ischemic insult [33]. During the experimental infarction, the M1 phenotype largely increased in the peri-infarct regions and secreted pro-inflammatory mediators including TNF-α, IL-1β, and IL-6, and interferon (IFN)-γ that promote the secretion of reactive oxygen/nitrogen species and proteolytic enzymes, such as matrix metalloproteinase-9, which will in turn exacerbate inflammation and neuronal injury [34]. The phenotypic transition of M1 toward the M2 subtype is a promising therapeutic strategy for cerebral infarction [35]. It is recognized that the balance between M1 and M2 phenotypes determines the detrimental or beneficial effects of neuroinflammation on cerebral infarction. In the present, in vivo experiments showed that HUMSCs clearly promote the transformation of microglia from the M1 into the M2 phenotype. Moreover, CD11b+ M1 cells with amoeboid morphology were reduced, whereas CD206+ M2 microglia were promoted to exert anti-inflammatory function upon treatment with MION@PDA-MSC(MF). In particular, M2 microglia was found to protect the nervous tissues by the enhanced production of Arg-1, CD206, TGF-β, and IL-10.
In summary, this study revealed that MSCs targeted at the site of cerebral infarction can reduce the volume of cerebral infarction and promote microglial shift from the M1 to the M2 phenotype for neuroprotection and pro-neuroinflammation, thereby representing a potential novel approach for cerebral infarction therapy.
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|
---
title: Mediating role of depressive symptoms on the relationship between sleep duration
and cognitive function
authors:
- Liqun Wang
- Shulan He
- Ning Yan
- Ruiping Pan
- Yang Niu
- Jiangping Li
journal: Scientific Reports
year: 2023
pmcid: PMC10008529
doi: 10.1038/s41598-023-31357-6
license: CC BY 4.0
---
# Mediating role of depressive symptoms on the relationship between sleep duration and cognitive function
## Abstract
Although some studies have shown the association between sleep duration and cognitive impairment is positive, the mechanism explaining how sleep duration is linked to cognition remains poor understood. The current study aims to explore it among Chinese population. A cross-sectional study of 12,589 participants aged 45 or over was conducted, cognition was assessed by three measures to capture mental intactness, episodic memory, and visuospatial abilities. The Center for Epidemiologic Studies Depression Scale 10 (CES-D10) was administered during the face-to-face survey to assess depressive status. Sleep duration was reported by the participants themselves. Partial correlation and linear regression were used to explore the association between sleep duration, cognition, and depression. The Bootstrap methods PROCESS program was used to detect the mediation effect of depression. Sleep duration was positively correlated with cognition and negatively with depression ($p \leq 0.01$). The CES-D10 score (r = − 0.13, $p \leq 0.01$) was negatively correlated with cognitive function. Linear regression analysis showed sleep duration was positively associated with cognition ($$p \leq 0.001$$). When depressive symptoms were considered, the association between sleep duration and cognition lost significance ($$p \leq 0.468$$). Depressive symptoms have mediated the relationship between sleep duration and cognitive function. The findings revealed that the relationship between sleep duration and cognition is mainly explained by depressive symptoms and may provide new ideas for interventions for cognitive dysfunction.
## Introduction
With the rapid development of society, the prevalence of cognitive dysfunction has been gradually rising1 and is a crucial public health issue in China. In 2011, national figures showed that $9\%$ of elderly individuals had cognitive impairment2. In 2016, a meta-analysis suggested that the pooled prevalence of mild cognitive impairment in the Chinese population had increased to $14.71\%$3. Cognitive impairment is associated with an increased risk for disability and economic burden and is an essential element of the diagnostic criteria for dementia4,5. Therefore, cognitive impairment is a significant burden on patients, their families, or society6.
Various factors affect cognitive impairment, such as ApoE4 status, smoking, hypertension, diabetes mellitus, cerebrovascular disease, and physical, intellectual, and social activities7–9. Furthermore, research on sleep and health has become active in recent years. Sleep duration plays a vital role in protecting individuals from cognitive decline10. Short sleep duration was associated with greater age-related brain atrophy and cognitive decline among old adults. Each 1-h decrease in sleep duration could predict a $0.67\%$ greater annual decrease in global cognitive performance11. In addition, several studies have revealed a U-shaped association between sleep duration and cognitive impairment, and both shorter and longer sleep durations have been related to a higher risk of cognitive disorders12–14. However, the relationship between sleep duration and cognitive function in middle-aged and elderly individuals is still unclear.
Sleep duration has also been commonly associated with depression15. Several studies have reported that short sleep durations exacerbate depressive symptoms16. A meta-analysis indicated that short and long sleep duration was significantly associated with an increased risk of depression in adults17. There was a curvilinear relation between sleep duration and depression, such that both short and long sleep durations were associated with depression18. Moreover, severe depression was associated with a relatively faster rate of cognitive decline among middle-aged and old adults19–21.
The links between sleep duration, depression, and cognitive function are interrelated. Depression was shown to mediate the relationship between short sleep duration and life satisfaction22. What then is the relationship between sleep duration, depression, and cognitive function? The current study sought to examine the mediating effect of depression on the relationship between sleep duration and cognitive function in Chinese adults. We hypothesized that sleeping for a long enough duration is associated with better cognitive function and that this association would be mediated at least in part by depression.
## Study sample
The data were from the China Health and Retirement Longitudinal Study (CHARLS) survey that targeted middle-aged and elderly individuals (45 + years of age) in China. The CHARLS is an ongoing follow-up study with exams performed every 2 years for a total of 3 waves from 2011 to 2015, which provided a broad range of information from demographic characteristics to health status23,24. The detailed sampling process can be found elsewhere25. In summary, the baseline survey was fielded from June 2011 to March 2012 and involved 17,705 respondents randomly selected with a probability proportional to scale (PPS) in 450 villages/resident committees, 150 counties/districts, and 28 provinces. A face-to-face interview was conducted via computer-assisted personal interviewing (CAPI) technology.
In this study, we adopted the data from wave 1, and a final sample of 12,589 respondents was included in the analysis. The inclusion criteria were as follows: (i) had complete data related to sleep duration; (ii) had complete data related to cognition measures at baseline; and (iii) had marital status, residency, smoking, drinking, body mass index (BMI), hypertension, dyslipidemia, and diabetes or high blood sugar data at baseline. The exclusion criteria were: (a) unconsciousness caused by any forms; (b) any obvious cognitive disabilities or deafness, aphasia, or other language barriers; and (c) sleep disorders and taking hypnotics, as well as some particular work needed to going to bed late.
This study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015) and the University of Newcastle Human Research Ethics Committee (H-2015-0290). All interviewees were required to sign informed consent.
## Outcome variable
Cognitive function was assessed using three measures at baseline: (a) the Telephone Interview of Cognitive Status (TICS-10); (b) word recall (WR); and (c) figure drawing (FD)23,26. These were used to estimate cognitive domains of mental intactness (orientation to time and attention), episodic memory, and visuospatial abilities. The overall cognitive score was calculated as the sum score of the TICS-10 (a sum of 10 scores), WR (a sum of 10 scores), and FD (a sum of 1 score) and could range from 0 to 21. The TICS-10 includes 10 items (score ranges from 0 to 10) that estimate the intactness of mental health, including orientation to time and attention26. The WR test assesses episodic memory and is the average number of correct immediate and delayed recalls from a list of 10 Chinese nouns24,26. The total score on the WR is 10. The FD test was used to measure visuospatial abilities24. The participants were shown two overlapping pentagons for this test and asked to draw the same picture. Scores of “1” and “0” indicate participants who successfully finished and failed the task, respectively.
## Independent variable
Sleep duration was obtained via the following question. “ During the past month, how many hours of actual sleep did you get at night (average hours for one night)? ( This may be shorter than the number of hours you spend in bed)”.
The Chinese version of the 10-item short form of the Center for Epidemiological Studies Depression Scale (CES-D10)27 was employed to indicate depressive symptoms, which has been validated among elderly individuals in China28,29. Ten items were contained in the CES-D10, and each item was scored from 0 to 3 in response to a 4-point Likert scale (from ‘rarely or none of the time’ to ‘most or all of the time’). The total score can range from 0 to 30, with higher scores indicating higher levels of depression. A cut-off score ≥ 10 was used to distinguish the participants who had obvious depressive symptoms30. A previous study showed that the CES-D10 scale has good reliability and validity (Cronbach’s α = 0.81)31.
## Other covariates
Covariates included age, sex (male vs. female), educational level (illiterate, primary school, or junior high school and above), marital status (married, cohabitating and divorced, separated, widowed, or never married), residency (rural vs. urban), BMI (continuous data), smoking, drinking, hypertension, dyslipidemia, and diabetes or high blood sugar. Smoking status was measured with the question, “Have you ever chewed tobacco, smoked a pipe, smoked self-rolled tobacco or smoked cigarettes/cigars,” and the possible answers included the following 3 options: [1] Yes; [2] No, or [3] Quit. Alcohol consumption status was measured with the question, “Did you drink any alcoholic beverages, such as beer, wine, or liquor in the past year, and if so, how often?”, and the possible answers included the following: [1] Drink more than once a month; [2] Drink but less than once a month; or [3] Do not drink. The status of hypertension, dyslipidemia, diabetes or high blood sugar was estimated with the following question: “Have you been diagnosed with the conditions listed below by a doctor?”.
## Statistical analyses
Analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 24.0 (SPSS Inc., Chicago, Illinois, USA). Means and standard deviations or frequency percentages were used to describe variables. Partial correlations were performed after controlling for age, sex, education, marital status, residency, smoking, drinking, BMI, hypertension, dyslipidemia, diabetes, or high blood sugar. A linear regression model was used to examine the association between sleep duration, depressive symptoms, and cognitive function. Bootstrapping methods of PROCESS developed by Hayes32 were employed to examine the mediation effect of depressive symptoms on the relationship between sleep duration and cognitive function. In the PROCESS procedure, the number of models chosen was 4, the bootstrap sample was set to 5000, and the bias-corrected percentile bootstrap confidence interval was used to evaluate the effect size. If the confidence interval does not contain 0, it would indicate that the mediation effect was statistically significant33. Sensitivity analyses were performed using Structural Equation Modelling (SEM) approach.
## Ethics approval and consent to participate
Ethics approval for the study was granted by the Ethics Review Committee of Peking University, and all the participants provided signed informed consent at the time of participation. The study methodology was carried out in accordance with approved guidelines.
## Demographic characteristics
As shown in Table 1, approximately half of the participants were male, and approximately $\frac{1}{5}$ were illiterate. Most ($73.1\%$) were rural residents. Overall, $45.9\%$ of the participants had depressive symptoms. The average age was 67.5 (SD = 9.6) years, with a range of 45 to 105 years. The average sleep duration was 6.4 (SD = 1.8) hours, the average CES-D10 score was 9.8 (SD = 4.7), and the average cognitive function score was 8.1 (SD = 2.8).Table 1Demographic characteristic of participants. VariablesTotal ($$n = 12$$,589)Age, mean (SD)67.5 (9.6)Gender, male, n (%)6131 (48.7)Marital status, Married, n (%)44 (2.6)Education level, n (%) Illiterate2832 (22.5) Primary5017 (39.9) Junior2870 (22.8) Senior or above1870 (14.9)Residency, rural, n (%)9307 (73.1)Smoking, n (%)4969 (39.5)Drinking, n (%)4171 (33.1)BMI, mean (SD)23.7 (4.0)Hypertension3099 (24.6)dyslipidemia1233 (9.8)Diabetes or high blood sugar731 (5.8)Sleep duration6.4 (1.8)CES-D9.8 (4.7)Depressive symptoms, n (%)2077 (16.5)Cognitive function8.1 (2.8)BMI body mass index, SD standard deviation.
## Correlation analysis
After controlling for covariates, sleep duration was positively correlated with cognitive function ($r = 0.03$, $95\%$ CI 0.02 to 0.05, $P \leq 0.01$) and inversely associated with depression scores (CES-D10; r = − 0.24, $95\%$ CI − 0.26 to − 0.22, $P \leq 0.01$). CES-D10 scores were negatively correlated with cognitive function (r = − 0.13, $95\%$ CI − 0.15 to − 0.10, $P \leq 0.01$), as shown in Table 2. As shown in Fig. 1, an inverted U-shaped association between sleep duration and cognitive function was found. Meanwhile, a U-shaped association between sleep duration and depressive symptoms was also found (Fig. 2).Table 2correlation matrix ($$n = 12$$,589).VariablesMeanSDSleep durationCES-DCognitive functionSleep duration6.41.81CES-D9.84.7− 0.24**1Cognitive function8.12.80.03**− 0.13**1Effect size0.03SD standard deviation, CES-D the Center for Epidemiologic Studies Depression Scale. All the analysis controlled for age, gender, education, marital status, residency, smoking, drinking, BMI, hypertension, dyslipidemia, and diabetes or high blood sugar.**$p \leq 0.01$, *$p \leq 0.05.$Figure 1A U-shaped association between sleep duration and cognitive function. Figure 2A U-shaped association between sleep duration and depressive symptoms.
## Linear regression analyses
As shown in Table 3, sleep duration was positively correlated with cognitive function (Model 1). When depressive symptoms were added in Model 2, the association between sleep duration and cognitive function lost significance (β = − 0.06, $95\%$ CI − 0.22 to 0.10, $$P \leq 0.468$$).Table 3Linear regression between depression, sleep duration and cognitive function. VariablesModel 1Model 2P valueβ ($95\%$ CI)P valueβ ($95\%$ CI)Age < 0.001− 0.05 (− 0.06, − 0.05) < 0.001− 0.05 (− 0.06, − 0.05)Gender0.0260.15 (0.02, 0.29)0.1990.09 (− 0.05, 0.22)Education < 0.0011.10 (1.05, 1.16) < 0.0011.09 (1.03, 1.14)*Marital status* (married)0.0120.19 (0.04, 0.34)0.0460.15 (0.01, 0.30)Rural < 0.001− 0.80 (− 0.92, − 0.68) < 0.001− 0.76 (− 0.88, 0.24)Smoking0.0790.11 (− 0.01, 0.24)0.0660.12 (− 0.01, 0.24)Drinking0.068− 0.10 (− 0.21, 0.01)0.039− 0.12 (− 0.23, − 0.01)BMI < 0.0010.01 (0.04, 0.06) < 0.0010.04 (0.03, 0.06)Hypertension0.608− 0.03 (− 0.15, 0.08)0.992− 0.01 (− 0.12, 0.11)dyslipidemia0.1270.13 (− 0.10, 0.30)0.0770.15 (− 0.02, 0.31)Diabetes or high blood sugar0.5920.05 (− 0.15, 0.26)0.3940.09 (− 0.11.0.29)DepressionNANA < 0.001− 0.54 (− 0.65, − 0.43)Sleep duration0.0010.04 (0.02, 0.07)0.468− 0.06 (− 0.22, 0.10)Sleep duration × depressionNANA0.350− 0.10 (− 0.31, 0.11)R20.2880.298BMI body mass index, $95\%$ CI $95\%$ confident interval.
## Mediation
For this analysis, sleep duration was an independent variable, depressive symptoms were the mediator variable, and cognitive function was a dependent variable. After controlling for age, sex, education, marital status, residency, smoking, drinking, BMI, hypertension, dyslipidemia, and diabetes or high blood sugar, a mediation model was conducted. The total effect, direct effect, and mediating effect of sleep duration on cognitive function with standard errors and confidence intervals were obtained using the bootstrap method. The results showed that the direct effect was non-significant, while the total effect and the indirect effect were significant, indicating that depressive symptoms fully mediated the relationship between sleep duration and cognitive function. The mediation effect of depressive symptoms on the relationship between sleep duration and cognitive function accounted for $93.0\%$ ($\frac{0.040}{0.043}$) of the total effect (Table 4). The bias-corrected program test results indicated that the $95\%$ confidence interval was 0.033 to 0.047, a range that did not include 0, indicating that the mediation path was statistically significant. Considering the possible relationship between age and depressive symptoms, the exploring analysis conducted stratified (use a cutoff point of 60 years old) by age showed in Table 5. The mediation effect of depressive symptoms on the relationship between sleep duration and cognitive decline persists in those who < 60 and ≥ 60 years old. Table 4The Mediating effect of depression on the relationship between sleep duration and cognitive function. EffectβSEP valueBias-corrected $95\%$ CILowerUpperTotal ($$n = 1742$$) Total effect0.0430.0130.0010.0170.068 Indirect effects (mediating effect)0.0400.004 < 0.0010.0330.047 Direct effects0.0030.0130.836-0.0230.029SE standard error, $95\%$ CI $95\%$ confident interval. All the analysis under controlling of age, gender, education, marital status, residency, smoking, drinking, BMI, hypertension, dyslipidemia, and diabetes or high blood sugar. Table 5The mediation model of depressive symptoms stratified by agea. EffectBias-corrected $95\%$ CIβSEP-valueLowerUpperAge < 60 years old Total effect0.0870.2860.0020.0310.144 Indirect effects0.0420.008 < 0.0010.0280.059 Direct effects0.0450.0290.119-0.0110.102Age ≥ 60 years old Total effect0.0430.0150.0040.0140.072 Indirect effects0.0370.021 < 0.0010.0290.046 Direct effects0.0060.0150.700-0.2400.036aThe effect adjusted the covariates include gender, education, marital status, residency, smoking, drinking, BMI, hypertension, dyslipidemia, and diabetes or high blood sugar.
## Sensitivity analysis
As showed in Fig. 3, the SEM approach was conducted to sensitivity analysis. The results showed sleep duration was positively associated with cognitive function and inversely related to depressive symptoms, and depressive symptoms were negatively associated with cognitive function. These results were consistent with the binary correlation results even though the pathway coefficient was not the same due to the different methods. This validated the methodological robustness of the findings. Figure 3The mediation model of depressive symptoms in the relationship between sleep duration and cognitive function. TICS the telephone interview of cognitive status, WR word recall, FD figure drawing.
## Discussion
The current study explored the association between sleep duration and cognitive function and the mediating effect of depressive symptoms on this relationship among middle-aged and elderly individuals in mainland China. In this study, we found that a longer enough sleep duration (in our population, only 956 ($7.5\%$) participants reported sleeping ≥ 9 h per night) was associated with a reduced risk of cognitive decline, and these beneficial effects were mediated by depressive symptoms. The mediation effect accounted for $93.0\%$ of the total effect.
Sleep duration was positively associated with cognitive function scores, and these data implied that short sleep duration might be a risk factor for cognitive impairment, consistent with a study showing that women sleeping for short durations had worse global cognition than those sleeping longer34. Short sleep duration was also reported to be a risk factor for cognitive impairment11,35. We speculate that the mechanism may be that short sleep duration has been related to the β-amyloid (Aβ) burden, which is one of the hallmarks of Alzheimer’s disease36. In addition, shorter sleep durations were associated with a greater degree of age-related brain atrophy11. However, our results were conflicted with prior research indicating that long sleep durations have been associated with cognitive impairment37,38. They explained this phenomenon by the fact that increased sleep fragmentation is associated with decreased cognitive performance and that longer sleep durations may result in more frequent nighttime awakenings or more sleep-in-bed time39. It should be noted that the r-value between sleep duration and cognitive function is somehow very small ($r = 0.03$); this maybe because there existed an inverted-U-shaped relationship between sleep duration and cognitive function. Previous research has reported that short or long sleep durations were significantly associated with memory impairment, might be a key marker for increased risk of cognitive impairment, and showed a U-shaped association14, which further confirmed that sleeping for enough duration was essential for maintaining cognitive function.
Furthermore, our results showed that depressive symptoms mediated the relationship between sleep duration and cognitive function. Previous studies have confirmed that depressive symptoms mediated the relationships between sleep duration and several factors, such as life satisfaction22. The present findings are also supported by a study showing that depression mediated the relationship between religiosity and cognitive impairment in older adults40. The relationships between sleep duration and depression have undoubtedly attracted people’s attention. The current study found that sleep duration was inversely associated with depression and implied that short sleep duration may be a risk factor for depression. This was consistent with a previous study that revealed that poor sleep, including short sleep duration, was independently associated with depression41. A meta-analysis also indicated that short sleep duration was linked to an increased risk of depression among adults16. Furthermore, we also found a U-shaped relationship between sleep duration and depressive symptoms, which is consistent with a previous study, that reported that insufficient or prolonged sleep was independently associated with depressive symptoms in middle-aged and elderly people, showing a U-shaped relationship42. Our study found that CES-D10 scores were negatively associated with cognitive function scores; that is, depression was positively associated with an increased risk of cognitive impairment. Previous studies have also suggested that depression was positively associated with cognitive impairment43,44. Furthermore, depression in elderly patients appears to predict dementia strongly45. These results might indicate that short sleep durations are associated with cognitive function through the mediating role of depression. In light of previous research, possible reasons are that shorter sleep duration may lead to daytime tiredness, which may lead to increased negative events and emotions, which may lead to depression46.
By the way, it was found that $45.9\%$ of the participants had depressive symptoms. This significantly exceeds the data on the prevalence of depression in the general population. It may be that the percentage of depressive symptoms reported by previous studies were different because of the different participants, methods, and design or different measurement scales, etc. for example, the results from a systematic review and meta-analysis showed that the summary prevalence of depression or depressive symptoms among medical students estimates ranged across assessment modalities from $9.3\%$ to $55.9\%$47. The prevalence of depressive symptoms among Chinese rural residents was $34.0\%$48. In addition, in our study, we defined the variable as depressive symptoms not depression when using the CES-D scale (CES-D score ≥ 10). Another study also reported that a large sample of Tibetan people with depressive symptoms (scores ≥ 8) accounted for $52.3\%$ of the total sample, and participants with depression (scores ≥ 14) accounted for $28.6\%$49. In addition, the average sleep duration for the Chinese population was 6.4 (SD = 1.8) hours. It looks less than the recommended and differs from the data on the habitual sleep time from Europe or the United States. In our study, the average age of participants was 67.5 (SD = 9.6) years. The old adults usually had short sleep duration. A study showed that participants aged 63.3 slept ≤ 7 h accounted for $84.6\%$50. Besides, it may be the characteristics of the population sample.
Given the increasing proportion of aged individuals and increasing prevalence of dementia in the Chinese population, the strength of the present findings is its relevance for using large-sample data sets to understand the mechanisms underlying the effects of sleep duration on cognitive function, which may stimulate further research. Several limitations existed in the present study. First, this was a cross-sectional analysis, and causal inferences were not possible. Hence, a future study with a longitudinal design would be necessary to determine causal relationships. Second, sleep duration was collected based on self-reporting, which may involve information bias despite common use in epidemiological studies due to feasibility considerations. Meanwhile, the current study does not determine the cause/nature of short sleep duration, which may include sleep apnea, pain, or other sleep-related disorders. Third, a limited number of neuropsychological test variables were used in this study, as the three tests do not encompass most cognitive domains, which may affect the results.
## Conclusion
In conclusion, this study provides evidence for the relationship between sleep duration and cognitive function among middle-aged and elderly individuals, suggesting a possible mechanism to explain how sleep duration is related to reduced cognition in studies examining that association. We found that depressive symptoms may mediate the relationship between sleep duration and cognitive impairment. Hence, in the clinic, clinicians may suggest that patients ensure that they get enough sleep and maintain a good mood. Clinicians may use melatonin to improve the sleep quality of those experiencing cognitive dysfunction and can also consider cognitive-behavioral therapy.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31357-6.
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|
---
title: Longitudinal analysis of long-term outcomes of abdominal flap-based microsurgical
reconstruction and two-stage prosthetic reconstruction
authors:
- Kyeong-Tae Lee
- Jina Kim
- Byung Joon Jeon
- Jai Kyong Pyon
- Sa Ik Bang
- Goo-Hyun Mun
journal: Scientific Reports
year: 2023
pmcid: PMC10008543
doi: 10.1038/s41598-023-31218-2
license: CC BY 4.0
---
# Longitudinal analysis of long-term outcomes of abdominal flap-based microsurgical reconstruction and two-stage prosthetic reconstruction
## Abstract
Two-stage tissue expander/implant (TE/I) and deep inferior epigastric perforator (DIEP) flaps are the two main pillars of breast reconstruction. This study aimed to conduct a longitudinal analysis of long-term outcomes after immediate DIEP- and TE/I-based reconstruction. This retrospective cohort study included patients with breast cancer who underwent immediate DIEP- or TE/I-based reconstruction between 2012 and 2017. The cumulative incidence of major complications, defined as unplanned reoperation/readmission due to complications, was analyzed by the reconstruction modality and its independent association. In total, 1,474 cases (1,162 TE/I and 312 DIEP cases) were analyzed, with a median follow-up of 58 months. The 5-year cumulative incidence of major complications was significantly higher in the TE/I group ($10.3\%$ vs. $4.7\%$). On the multivariable analyses, the use of DIEP flap was associated with a significantly reduced risk of major complications compared to that of TE/I. A more prominent association was observed in the analysis of patients who received adjuvant radiotherapy. Restricting analysis to those who received adjuvant chemotherapy revealed no differences between the two groups. The rate of reoperation/readmission for improving aesthetic outcomes was similar in the two groups. Long-term risks for unexpected reoperation/readmission may differ between DIEP- and TE/I-based immediate reconstruction.
## Introduction
Breast reconstruction has become an essential pillar in breast cancer treatment, owing to its established advantages in improving self-esteem, psychosocial well-being, and quality of life1,2. Accordingly, the global rate of immediate breast reconstruction following total mastectomy has steeply increased. Currently, there are two mainstream breast reconstruction modalities: autologous flap reconstruction and prosthesis-based reconstruction. Specifically, the most commonly used methods of the former and latter types are deep inferior epigastric artery perforator (DIEP) flaps, and two-stage tissue expander/implant-based (TE/I) method, respectively. Each method has its own pros and cons, and the most appropriate method may differ by individuals. Reconstructive surgeons select the best modality for each patient based on a variety of factors, including its safety representatively.
Surprisingly, despite the long-standing, prevalent use of these two methods, the method that provides safer and more reliable outcomes with a lower rate of postoperative morbidities remains unclear. Several clinical investigations compared postoperative complications between the two methods3–7. However, most were limited by their cross-sectional study design, which lacked analyses with time passage. Recently, several longitudinal studies have assessed the development of complications following breast reconstruction and compared them among diverse methods, including DIEP flaps and the two-stage TE/I method6–8. However, these studies did not reach consistent conclusions regarding which method could be safer with a lower cumulative incidence of complications. Despite their elaborate study designs and relatively large sample sizes, these studies had a relatively short follow-up period of 2–3 years, which might have led to inconsistent results. With advancements in cancer treatment, breast cancer survivors may have a long life expectancy, and a reconstructed breast could be a lifelong companion for these patients. In addition, adjunct treatment of breast cancer, including radiotherapy and/or chemotherapy, which may influence the outcomes of breast reconstruction, has been sustained for years. Therefore, the outcomes of breast reconstruction need to be analyzed over longer periods. Debates on the long-term safety of the two representative methods for breast reconstruction are still ongoing with lack of relevant evidence.
This study aimed to investigate the longitudinal outcomes of immediate breast reconstruction using DIEP flaps and two-stage TE/I for a longer follow-up period, to compare the risks of adverse outcomes between the two modalities, and to determine whether the relative risks of the two methods differ according to specific oncologic treatment settings.
## Study participants
Patients with breast cancer who underwent immediate breast reconstruction after total mastectomy between 2012 and 2017 were retrospectively reviewed. Patients who had undergone DIEP flap surgery (DIEP flap group) or two-stage TE/I for breast reconstruction (TE/I group) were included. The following patients were excluded: those who used other reconstruction modalities, such as latissimus dorsi myocutaneous or direct-to-implant flaps, which were rarely adopted in our institution during the study period; those who did not complete both stages during the follow-up period owing to causes unrelated to complications; those who underwent the second stage of exchange-to-implant in another clinic; those who had planned conversion to autologous tissue reconstruction following tissue expander insertion; and those who had been lost to follow-up within 2 years postoperatively.
In the authors’ institution, the reconstruction modality was determined by attending reconstructive surgeons, mainly considering patients body habitus and desires. Generally, for patients having higher BMI or large and ptotic breasts, the use of DIEP flap was preferentially considered. Patients’ oncologic conditions including possibility of receiving adjuvant radiotherapy or chemotherapy were not a primary consideration in choosing reconstruction modality.
This study was conducted in accordance with the principles of the Declaration of Helsinki. The study was approved by the institutional review board of Samsung Medical Center (IRB No. 2022-07-041). The requirement for informed consent was waived owing to the retrospective nature of the study.
## Data collection and outcome measurements
Data regarding patient characteristics (age, body mass index [BMI], comorbidities), operation (type of mastectomy, weight of the mastectomy specimen, type of reconstruction), and adjunct treatment-related characteristics (neoadjuvant chemotherapy, adjuvant chemotherapy, adjuvant radiotherapy) were collected from our prospectively maintained database, which was regularly updated by ancillary doctors.
Data regarding the development of postoperative complications could have been underestimated due to the retrospective study design and the underreporting of adverse events in medical charts. Therefore, we conducted a subsequent analysis focusing on the development of reoperation or readmission, wherein data were accurately recorded in the medical charts and were subject to the least risk of underestimation.
The primary outcome of interest was major complications, defined as unexpected reoperations under general anesthesia due to postoperative complications, which were conducted beyond the index reconstructive operations, and/or unexpected readmission due to postoperative complications. The index reconstructive operations included primary reconstruction (transfer of abdominal flaps or insertion of a tissue expander), the second stage of exchange-to-implant for cases of prosthetic reconstruction, reconstruction of the nipple-areolar complex, and tattooing. The development of reconstruction failure, which was defined as flap removal or TE/I and/or unplanned conversion to other modalities, was also noted and analyzed. The secondary outcomes were reoperations under general anesthesia or readmission for improving aesthetic outcomes or increasing patient satisfaction, which were unrelated to postoperative complications.
The date and type of adverse events (unplanned reoperation, readmission, or reconstruction failure) and the date of the last follow-up were documented. The time until the development of adverse events was calculated and used to assess the event-free duration.
## Statistical analysis
The association between categorical variables of interest was evaluated using Pearson’s chi-squared test or Fisher’s exact test. Analyses between continuous and categorical variables were conducted using the Student’s t-test or the Mann–Whitney U test. The Kaplan–Meier method was used to calculate the cumulative incidence of major complications or reconstruction failure, for which differences were compared using the log-rank test. The endpoint analyzed was the development of adverse outcomes during the follow-up period. Cases were censored if the respective events had not occurred during the follow-up period. Univariate and multivariate Cox regression analyses were conducted to identify independent predictors of outcomes with hazard ratios (HRs) and the corresponding $95\%$ confidence intervals (CIs). For multivariate analyses, the backward selection model was used with variables that p-values were < 0.01 in univariate analyses.
To adjust for potential heterogeneity in baseline characteristics between the two groups, propensity score matching analyses were conducted for 10 variables: age, BMI, diabetes, hypertension, smoking status, type of mastectomy, weight of the mastectomy specimen, neoadjuvant chemotherapy, adjuvant chemotherapy, and adjuvant radiotherapy. Based on the calculated propensity scores, the DIEP flap and TE/implant groups were matched at a ratio of 1:1, and the rates of adverse outcomes were compared. Univariate and multivariate analyses were also conducted after propensity score matching. All analyses were performed using SPSS version 20 (IBM Corporation, Armonk, NY, USA), and statistical significance was set at $p \leq 0.05.$
## Results
During the study period, 11,927 patients with breast cancer were treated with a partial or total mastectomy at our institution. Based on the above selection criteria, 1,474 cases representing 1,380 patients were finally included and analyzed. There were 1,286 unilateral and 94 bilateral reconstructions. Their mean age and BMI were 44.1 years (range, 18–66 years), and 22.5 kg/m2 (range, 15.1–39.8), respectively.
Of these, 1,162 underwent a two-stage prosthetic-based method, and 312 underwent a DIEP flap operation. Table 1 lists the baseline characteristics of the two groups. The DIEP flap group had an older age, a higher BMI, a greater weight of mastectomy specimen, and a lower rate of nipple-sparing mastectomy than the TE/I group. The rate of cases receiving neoadjuvant chemotherapy was higher in the DIEP flap group. Other characteristics, including comorbidities and adjuvant treatment, were similar between the groups. The median follow-up period was 58 months (range, 6–110 months).Table 1Comparison of baseline characteristics between DIEP flap versus TE/I groups. TE/I groupDIEP flap groupp-valuePatient No1,078302Case No1,162312Laterality (per patient)Unilateral994 ($95.9\%$)292 ($96.7\%$)Bilateral84 ($4.1\%$)10 ($3.3\%$)Age, mean (± SD), (yrs)43.6 (± 7.5)46.3 (± 6.6) < 0.001BMI, mean (± SD), (kg/m2)22.2 (± 2.9)23.6 (± 3.4) < 0.001Normal weight925 ($79.6\%$)212 ($67.9\%$) < 0.001Underweight61 ($5.2\%$)9 ($2.9\%$)Overweight/Obesity176 ($15.1\%$)91 ($29.2\%$)Diabetes14 ($1.2\%$)5 ($1.6\%$)0.580Hypertension58 ($5.0\%$)25 ($8.0\%$)0.040Smoking19 ($1.6\%$)7 ($2.2\%$)0.468Type of mastectomy < 0.001Nipple-sparing327 ($28.1\%$)39 ($12.5\%$)Skin-sparing835 ($71.9\%$)273 ($87.5\%$)Mastectomy weight, mean (± SD), (gram)393.7 (± 216.2)479.9 (± 206.1) < 0.001Neoadjuvant chemotherapy0.029Received83 ($7.1\%$)34 ($10.9\%$)Not received1082 ($92.9\%$)278 ($89.1\%$)Adjuvant chemotherapy0.106Received449 ($38.6\%$)105 ($33.7\%$)Not received713 ($61.4\%$)207 ($66.3\%$)Adjuvant radiotherapy0.450Received223 ($19.2\%$)54 ($17.3\%$)Not received939 ($80.8\%$)258 ($82.7\%$) During the follow-up period, the major complications developed in 126 cases, including 94 reoperations conducted under general anesthesia and 32 with readmission only (see Table 2). The TE/I group had a significantly higher rate of the major complications ($9.6\%$ vs. $4.5\%$, $$p \leq 0.004$$). The distribution of the major complications according to the postoperative timing (≤ 2 months, 2 months to 2 years, or > 2 years) differed between the two groups. The rate of the major complications ≤ 2 months postoperatively was similar between the groups, whereas those from 2 months to 2 years and > 2 years postoperatively were significantly higher in the TE/I group. The 2- and 5-year cumulative incidences of the major complications and reconstruction failure rates were also higher in the TE/I group. In the Kaplan–Meier analysis, the TE/I group showed a significantly higher cumulative incidence of the major complications than the DIEP flap group ($$p \leq 0.004$$; see Fig. 1). The rates of reoperation and/or readmission for improving aesthetic outcomes did not differ between the two groups ($$p \leq 0.273$$).Table 2Comparison of outcomes between two groups. ComplicationsTE/I groupDIEP groupp-valueMajor complication112 ($9.6\%$)14 ($4.5\%$)0.004TypeInfection43 ($3.7\%$)2 ($0.6\%$)Hematoma15 ($1.3\%$)3 ($1.0\%$)Extensive wound problem01 ($0.3\%$)Flap failure02 ($0.6\%$)*Fat necrosis* excision*04 ($1.3\%$)Abdominal wall weakness02 ($0.6\%$)Prosthesis failure (rupture or leakage)13 ($1.1\%$)0Capsular contracture19 ($1.6\%$)0Unplanned conversion to other modalities22 ($1.9\%$)0Timing0.019Developed within postop 2 months39 ($3.4\%$)8 ($2.6\%$)Developed within postop 2 years47 ($4.0\%$)4 ($1.3\%$)Developed beyond postop 2 years26 ($2.2\%$)2 ($0.6\%$)2-year cumulative incidence$7.4\%$$3.9\%$5-year cumulative incidence$10.3\%$$4.7\%$Reconstruction failure64 ($5.5\%$)2 ($0.6\%$) < 0.001Timing0.002Developed within postop 2 months14 ($1.2\%$)2 ($0.6\%$)Developed within postop 2 years39 ($3.4\%$)0Developed beyond postop 2 years11 ($0.9\%$)0Reoperation/Readmission for aesthetic purpose16 ($1.4\%$)7 ($2.2\%$)0.273All analyses were conducted with the first event occurred during the follow-up period.*The fat necrosis was a kind of partial flap necrosis that could be derived from insufficient perfusion to the flap adipose tissue. Excision of fat necrosis was conducted in patients who developed palpable nodules over 2 cm in diameter on their reconstructed breasts and wanted to remove the lesions. Figure 1Comparison of cumulative incidence of major complications between the two groups using Kaplan–Meier analysis.
In the univariate Cox regression analyses, the reconstruction method showed a significant association with the development of the major complications which remained significant after adjusting for other variables in the multivariate analyses. That is, the use of DIEP was associated with a significantly reduced incidence of the major complications compared to that of TE/I (HR, 0.415; $95\%$ CI, 0.238–0.724; adjusted $$p \leq 0.002$$). Hypertension, the weight of the mastectomy specimen, and adjuvant radiotherapy were also significantly associated with the development of the major complications (see Table 3).Table 3Univariable and multivariable Cox-regression analysis for major complication. Univariable analysisMultivariable analysisHR ($95\%$ CI)p-valueHR ($95\%$ CI)p-valueAge1.020 (0.996–1.045)0.104BMI1.073 (1.020–1.128)0.006Diabetes2.154 (0.685–6.774)0.189Hypertension2.183 (1.253–3.806)0.0062.108 (1.184–3.752)0.011Smoking0.048 (0–16.919)0.311Type of mastectomy0.058Skin-sparingRefNipple-sparing1.435 (0.988–2.083)Mastectomy weight1.001 (1.000–1.002) < 0.0011.001 (1.000–1.002)0.004Reconstruction method0.0050.002TE/IRefrefDIEP flap0.448 (0.257–0.781)0.415 (0.238–0.724)Neoadjuvant chemotherapy0.604Not conductedrefConducted0.827 (0.404–1.694)Adjuvant chemotherapy0.037Not conductedrefConducted1.451 (1.023–2.060)Adjuvant radiotherapy < 0.001 < 0.001Not conductedrefrefConducted2.451 (1.699–3.535)2.296 (1.580–3.337)HR Hazard ratio, CI Confidence interval, BMI Body mass index, ref reference.
In multivariate analyses, the use of DIEP was also significantly associated with a decreased incidence of reconstruction failure compared to that of TE/I (HR, 0.105; $95\%$ CI, 0.026–0.428).
## Analysis after propensity score matching
Further analyses were conducted on 312 pairs (624 cases) using propensity score matching. The two groups were successfully matched for all baseline characteristics, including age, BMI, co-morbidity, type of mastectomy, and adjuvant oncologic treatments (see Supplementary Table S1). Similar to the above analyses, the cumulative incidence of the major complications was significantly higher in the TE/I group than in the DIEP flap group ($$p \leq 0.027$$).
Multivariate analyses showed that the use of TE/I-based was significantly associated with an increased risk of developing the major complications (HR, 0.510; $95\%$ CI, 0.266–0.977; adjusted $$p \leq 0.042$$).
## Adjuvant radiotherapy
To adjust for the potential confounding effects of adjunct oncologic treatments on the development of postoperative adverse events, subgroup analyses were performed. In the analysis of 277 patients receiving adjuvant radiotherapy, the rate of the major complications was significantly higher in the TE/I group, which was more prominent in the postoperative period between 2 months and 2 years (see Table 4). Kaplan–*Meier analysis* showed a significantly increased cumulative incidence of adverse events in the TE/I group throughout the follow-up period ($$p \leq 0.008$$; see Fig. 2). Multivariate analyses demonstrated that the reconstruction method was associated with the development of the major complications after adjusting for other variables, indicating that the TE/I method was associated with an increased rate of the major complications (see Supplementary Table S2).Table 4Comparison of cumulative incidence of major complications between two groups according to adjuvant oncologic treatments. TE/Implant groupDIEP flap groupp-valueIn cases of adjuvant radiotherapy22354Major complication42 ($18.8\%$)2 ($3.7\%$)0.006Timing0.053Developed within postop 2 months11 ($4.9\%$)1 ($1.9\%$)Developed within postop 2 years23 ($10.3\%$)1 ($1.9\%$)Developed beyond postop 2 years8 ($3.6\%$)02-year cumulative incidence$14.6\%$$3.7\%$5-year cumulative incidence$21.2\%$$3.7\%$In cases of adjuvant chemotherapy449105Major complication49 ($10.9\%$)10 ($9.5\%$)0.678Timing0.198Developed within postop 2 months16 ($3.6\%$)7 ($6.7\%$)Developed within postop 2 years24 ($5.3\%$)2 ($1.9\%$)Developed beyond postop 2 years9 ($2.0\%$)1 ($1.0\%$)2-year cumulative incidence$9.0\%$$8.6\%$5-year cumulative incidence$11.6\%$$9.7\%$Figure 2Comparison of the cumulative incidence of major complications between the two groups in diverse clinical situations according to adjuvant oncologic treatments in cases receiving (left) adjuvant radiotherapy, (middle) adjuvant chemotherapy, or (right) no adjuvant treatments.
## Adjuvant chemotherapy
Among the 554 patients who had received adjuvant chemotherapy, the rate of the major complications was similar between the two groups. In the Kaplan–Meier analysis, the two groups showed similar cumulative incidences of adverse outcomes. However, the rate in the DIEP flap group was higher within two postoperative years, while that of the TE/I group exceeded that of the DIEP flap group in the long-term ($$p \leq 0.683$$; see Fig. 2). Consistent results were also observed in multivariate analyses, showing that the reconstruction method was not associated with the development of adverse outcomes in this subgroup. The weight of the mastectomy specimen and receiving adjuvant radiotherapy were significantly associated with the development of the major complications (see Supplementary Table S2).
## No adjuvant treatment
A total of 806 patients did not receive any adjunct oncological treatment. Kaplan–*Meier analysis* showed a significantly higher cumulative incidence of the major complications in the TE/I group ($$p \leq 0.004$$; see Fig. 2). Consistently, multivariate analyses demonstrated that the DIEP flap group was associated with a significantly decreased rate (approximately one-fourth of the risk) of the major complications compared to the TE/I group after adjusting for other variables (see Supplementary Table 2).
## In DIEP flap reconstruction
The rate of major complications was evaluated in four subgroups categorized by adjuvant oncologic treatments: no adjuvant treatment, adjuvant chemotherapy only, adjuvant radiotherapy only, and adjuvant radiotherapy and chemotherapy. In the DIEP flap group, the two groups receiving adjuvant chemotherapy (adjuvant chemotherapy only and adjuvant radiotherapy and chemotherapy groups) showed significantly higher rates of the major complications than the other two groups ($$p \leq 0.020$$; see Supplementary Fig. S1). Similar results were observed in terms of the cumulative incidence of the major complications in the Kaplan–*Meier analysis* (see Fig. 3). Specifically, no differences were observed in the comparison of the cumulative incidences of the major complications between patients who had received adjuvant radiotherapy and those who had not ($$p \leq 0.753$$). However, significant differences were observed between those who had and had not received adjuvant chemotherapy ($$p \leq 0.002$$). In multivariate analyses, adjuvant chemotherapy, as well as hypertension and the weight of the mastectomy specimen showed a significant association with the development of the major complications in the DIEP flap group. Adjuvant radiotherapy was not associated with adverse outcomes. Figure 3Cumulative incidence of major complications in the (left) deep inferior epigastric perforator (DIEP) and (right) tissue expander/implant (TE/I) groups.
## In TE/I reconstruction
The rate of the major complications was significantly different according to adjuvant treatment, showing that the two groups receiving adjuvant radiotherapy (adjuvant radiotherapy only and adjuvant radiotherapy and chemotherapy groups) had a higher rate of adverse outcomes (see Supplementary Fig. S1). Consistent with the Kaplan–Meier analysis, patients receiving adjuvant chemotherapy and radiotherapy showed the highest cumulative incidence, followed by those receiving adjuvant radiotherapy only, while those receiving adjuvant chemotherapy showed the lowest cumulative incidence ($p \leq 0.001$; see Fig. 3). In contrast to the analyses for the DIEP flap group, comparison by adjuvant chemotherapy showed no difference ($$p \leq 0.288$$), while adjuvant radiotherapy showed a significant difference ($p \leq 0.001$). Multivariate Cox regression analyses demonstrated that adjuvant radiotherapy was significantly associated with the development of the major complications (HR, 2.612; $95\%$ CI, 1.780–3.832; adjusted $p \leq 0.001$). A high BMI and the use of an acellular dermal matrix also showed significant associations. However, adjuvant chemotherapy did not show a significant association with adverse outcomes in this subgroup.
## Discussion
The present study evaluated the cumulative incidence of unexpected reoperation under general anesthesia and readmission following immediate breast reconstruction and compared it between cases using DIEP flaps and those using two-stage TE/I. The potential associations between reconstruction methods and the development of adverse outcomes were investigated in diverse clinical situations including conducting adjuvant treatments. Moreover, longitudinal analyses over a median follow-up period of approximately 5 years were performed in a considerable number of cases.
The main outcomes of interest in this study were unplanned reoperation under general anesthesia and/or unexpected readmission due to complications. These adverse events are the most serious complications that may considerably induce patient morbidity, tarnish the benefits of breast reconstruction, and affect oncologic outcomes, which should be considered primarily when selecting reconstruction methods for patients with breast cancer. Moreover, these events are never omitted from patient medical charts, which may minimize the potential risks of underestimation and overcome the inherent limitation of a retrospective study design to some degree.
In the current study, the two groups were heterogeneous in terms of several baseline characteristics including mastectomy type, weight of mastectomy specimen, BMI, and age. As mentioned above, we preferred to choose the option of DIEP flap for patients having higher BMI or large and ptotic breasts. This may contribute to generation of these heterogeneities. These variables have been reported to affect the outcomes of breast reconstruction. It is well known that patients undergoing nipple-sparing mastectomy may have a higher risk of adverse outcomes, including reconstruction failure, than those undergoing skin-sparing mastectomy, regardless of the reconstruction method9. In addition, several studies have reported that age may also influence the outcomes of breast reconstruction. Moreover, the impact of age on complications and patient satisfaction may differ according to the reconstruction method, showing that old age may have a negative influence on the outcomes of implant-based reconstruction but no influence on those of autologous reconstruction10,11. To minimize the confounding effects of heterogeneity between the groups, we conducted propensity score-matched analyses as well as multivariable logistic regression analyses.
In the long-term follow-up, we found that autologous tissue reconstruction using DIEP flaps was associated with a reduced risk of unexpected reoperation and/or readmission due to complications compared to two-stage TE/I-based reconstruction. These results were also supported by multivariate and propensity score matching analyses. Our findings are contradictory to those of previous studies with relatively short-term follow-ups. Jagsi et al. reported that overall wound complication rates were significantly higher in autologous reconstruction than in implant-based reconstruction within the first two postoperative years12. Bennett et al. reported similar outcomes, in that the use of DIEP flap for breast reconstruction was associated with approximately two-time higher odds of developing any complication than TE/I8. Autologous reconstruction may be more invasive with widely spanning operation fields and more complex in nature, and it can be rather apparent that it contains higher risks for complications during the early postoperative period. However, as time passes, the transferred autologous tissue can mature and gain longevity and stability with less vulnerability to external adverse impacts, such as radiation. This may lead to more favorable results for the DIEP flap over TE/I in the long run, as have previously reported. Fischer et al. reported that autologous tissue-based immediate reconstruction showed a significantly higher rate of 90-day complications and a lower rate of secondary breast procedures for three postoperative years than the TE/I method, which seems consistent with our findings6. It can be assumed that autologous tissue reconstruction may provide more reliable and safer outcomes than prosthetic reconstruction in the long run. However, in addition to postoperative complications, the potential risks of developing medical complications may need to be considered. In particular, deep vein thrombosis and pulmonary thromboembolism, although very rare, may be fatal for patients and may develop more frequently following DIEP flap-based reconstruction due to its long operation time and immobilization period. Also, we have found that the type of the major complications differed significantly between two groups, showing a higher rate of infection in the TE/I group and a higher rate of flap-related complications including total failure and fat necrosis in the DIEP flap group. This may contribute to generating significant differences in the timing of the adverse event development as well as in the rates of the major complications in the long-term follow-up.
We observed a more prominent association between TE/I and the development of major complications when restricting the analyses to individuals receiving adjuvant radiotherapy. This was further supported by additional subgroup analyses that showed that in cases using TE/I, receiving adjuvant radiotherapy was associated with an increased risk of developing major complications compared to the control, but was not associated with such a risk in those using DIEP flaps. Our findings are consistent with those of previous studies, which reported that implant-based reconstruction can be more vulnerable to the potentially adverse effects of radiation than autologous reconstruction5,13,14. In addition, postoperative radiotherapy was not a predictor of adverse outcomes in DIEP flap-based reconstruction. Based on these findings, it might be better for surgeons to inform patients who are expected to receive adjuvant radiotherapy of the potentially higher risks for unexpected reoperation/readmission that they are recommended to choose autologous tissue reconstruction if available.
In the present study, receiving adjuvant radiotherapy was found to be an independent risk factor for developing the major complications. Given that the rate of cases receiving it was slightly higher in the TE/I group than in the DIEP group, though not significantly different, a concern can be raised as to potential confounding effects of adjuvant radiotherapy on the outcomes. However, even in cases not receiving adjuvant treatments, the use of TE/I method was significantly associated with an increasing rate of the adverse outcome, compared to that of DIEP flap. This may suggest that a potential confounding effect related to the adjuvant radiotherapy may not influence the outcomes considerably.
Interestingly, adjuvant chemotherapy was associated with an increased risk of complications in DIEP flap-based reconstruction, but not in TE/I-based reconstruction. Previous studies investigating the potential influences of adjuvant chemotherapy on outcomes of DIEP flap-based breast reconstruction have been sparse15. Although adjuvant chemotherapy is usually delivered after wound healing has been completed, it could hinder the potential of wound healing and may lead to the development of delayed complications. Considering the wide-spanned operation fields and subsequently large-dimension wounds to be healed following DIEP flap-based breast reconstruction, higher complication rates in patients receiving adjuvant chemotherapy compared to the control may be plausible. In contrast, TE/I-based reconstruction has relatively narrow operation fields and less burden on wound healing; therefore, it may be less vulnerable to the potentially adverse effects of adjuvant chemotherapy. Based on these findings, patients who undergo DIEP flap-based breast reconstruction and are expected to receive adjuvant chemotherapy need to be informed of the potentially higher risks of unplanned reoperation/readmission after surgery.
The present study had several limitations. Its retrospective study design, with the subsequent risks of underestimation of the event, was an inherent limitation. All postoperative complications are significant issues for patients and may need to be evaluated to obtain more valid conclusions. However, *This is* challenging in retrospective studies; therefore, the current study evaluated only complications that resulted in unplanned reoperation under general anesthesia and/or unexpected readmission, which may limit the overall picture of the pros and cons of DIEP flap and TE/I reconstruction methods. In addition, although approximately 1,500 cases were included, the sample size based on a single institution may not be sufficient to draw concrete conclusions. An imbalanced sample size between the two groups (< 400 cases in the DIEP flap group) may have acted as a potential confounding factor in the statistical analysis. Lastly, this study population had relatively low BMIs and obesity rates compared to other populations, which may make it difficult to generalize our results. It has been established that obesity is an independent risk factor for postoperative complications, whose detrimental effects can be exaggerated in cases that have undergone operations with wide surgical fields, like DIEP flap-based breast reconstruction16,17. Therefore, conducting a similar analysis on obese patients with high BMIs may generate different results. Further large-scale multicenter studies are warranted to verify these results and generalize the conclusions.
## Conclusions
This 5-year longitudinal retrospective analysis revealed that autologous immediate breast reconstruction using a DIEP flap was associated with low rates of unexpected reoperation or readmission due to complications, with significantly lower odds of adverse events in the long run compared with two-stage TE/I-based reconstruction. This tendency toward favorable outcomes for DIEP flap-based reconstruction may differ according to adjuvant oncologic treatments and is more prominent in patients receiving adjuvant radiotherapy. Although further larger investigations with long-term follow-up are necessary, this information may be helpful in preoperative patient counseling to select a suitable breast reconstruction modality and postoperative care.
## Supplementary Information
Supplementary Legends. Supplementary Figure S1.Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31218-2.
## References
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|
---
title: Neonatal loss of FGFR2 in astroglial cells affects locomotion, sociability,
working memory, and glia-neuron interactions in mice
authors:
- Hanna E. Stevens
- Soraya Scuderi
- Sarah C. Collica
- Simone Tomasi
- Tamas L. Horvath
- Flora M. Vaccarino
journal: Translational Psychiatry
year: 2023
pmcid: PMC10008554
doi: 10.1038/s41398-023-02372-y
license: CC BY 4.0
---
# Neonatal loss of FGFR2 in astroglial cells affects locomotion, sociability, working memory, and glia-neuron interactions in mice
## Abstract
Fibroblast growth factor receptor 2 (FGFR2) is almost exclusively expressed in glial cells in postnatal mouse brain, but its impact in glia for brain behavioral functioning is poorly understood. We compared behavioral effects from FGFR2 loss in both neurons and astroglial cells and from FGFR2 loss in astroglial cells by using either the pluripotent progenitor-driven hGFAP-cre or the tamoxifen-inducible astrocyte-driven GFAP-creERT2 in Fgfr2 floxed mice. When FGFR2 was eliminated in embryonic pluripotent precursors or in early postnatal astroglia, mice were hyperactive, and had small changes in working memory, sociability, and anxiety-like behavior. In contrast, FGFR2 loss in astrocytes starting at 8 weeks of age resulted only in reduced anxiety-like behavior. Therefore, early postnatal loss of FGFR2 in astroglia is critical for broad behavioral dysregulation. Neurobiological assessments demonstrated that astrocyte-neuron membrane contact was reduced and glial glutamine synthetase expression increased only by early postnatal FGFR2 loss. We conclude that altered astroglial cell function dependent on FGFR2 in the early postnatal period may result in impaired synaptic development and behavioral regulation, modeling childhood behavioral deficits like attention deficit hyperactivity disorder (ADHD).
## Introduction
Fibroblast growth factor (FGF) signaling has been characterized extensively for its role in the brain, particularly in neurons and their precursors [1–4]. We and others have determined the importance of FGF signaling for embryonic and postnatal neurogenesis and cortical expansion [5–8], dendrite and synapse development [9], and expression and responsiveness of neuronal components of the glucocorticoid-response system [10, 11]. Furthermore, changes in locomotor activity, anxiety-like behavior, altered stress response, and social interaction occur in animal models with disrupted FGF ligands or FGF receptor 1 (FGFR1) dependent signaling in the forebrain [3, 10, 12–16], implicating FGF signaling in basic brain development and functioning relevant to psychiatric illness. However, even in these studies, understanding the specific role of FGF receptors and ligands in different cell types for specific behavioral brain functions has been difficult. There are many different interactions of FGF ligands and FGFRs, such that specific roles for FGF ligands—such as FGF17 in social behavior, FGF22 in anhedonia-like behavior, or FGF2 or FGF8 in anxiety-like behavior—leaves undetermined what receptors and cells mediate these effects [10, 14, 17, 18]. This is an important challenge to overcome to understand how FGF signaling may contribute to neuropsychiatric risk [19–22] and potential for treatment [23].
FGF receptors, and in particular FGFR2, are almost exclusively expressed in non-neurons in the postnatal forebrain [24–29]. Although FGF signaling is known to promote astrocyte differentiation and astrocyte activation in the mature brain [30, 31], the role of specific FGFRs in astrocytes is still undefined, and unraveling their separate roles at different developmental stages has been challenging. Evidence has recently arisen through animal model systems that suggests the importance of FGF-signaling alterations in astrocytes for processes of brain development and behavior: specifically, FGFR1 expression in dorsal forebrain astroglia secondarily affects the early postnatal development of cortical interneurons, but impacts of astrocytic FGFR2 during early postnatal development is not known, despite its almost-exclusive presence in astroglia postnatally. In cultured human astrocytes, both FGFR1 and FGFR2 are needed for signal transduction effects of anti-depressants, but FGFR2, not FGFR1, is necessary for acidic-FGF (FGF1) to further stimulate activated astrocytes [24, 32]. Exogenous FGF2 reduces stress-induced changes in astrocytes which may be a pathway to regulation of anxiety-like behavior; [33–35] however, anti-anxiety-like effects of FGF2 in mice do not require FGFR1 or R2 [10]. Many gaps in knowledge about the behavioral impacts of astroglial FGF signaling exist. Specifically, how astrocyte-dependent signaling through FGFR2 affects behavioral regulation across different domains has not been determined, despite its importance in postnatal astroglia.
Astrocyte dysfunction in the forebrain results in mixed behavioral outcomes but suggest a role for astroglia in regulation of activity—specifically disrupted astroglial SynCAMI or adenosine kinase increases locomotor activity [36, 37]. Astrocytes have a critical role in regulating neuronal synaptogenesis and pruning, processes that underlie early learning and behavioral outcomes [38, 39], emphasizing the importance of examining not only behavioral outcomes from FGF effects on astrocyte function but how these relate to synaptic development. There have been calls for greater investigation of astrocytic mechanisms relevant to childhood neurodevelopmental disorders [40, 41].
Here, we specifically targeted FGF signaling in the brain at different times as important mechanisms underlying multiple behaviors by inducing the knockout of FGFR2 at different stages of development and in different cell types. We examined three different sets of mice that had a cell type-targeted knock-out of FGFR2: 1) knockout during embryogenesis, in radial glial cells and therefore both their neuronal and glial progeny 2) knockout during the rodent early postnatal period, largely in proliferating astroglial precursors and glial progeny and 3) knockout during adulthood, in largely post-mitotic astroglia during mature brain functioning. We hypothesized that early postnatal loss of FGFR2 in astrocytes would affect astrocyte biological function and impact the regulation of behaviors that are disrupted at early postnatal time points in psychiatric disorders.
## Mice
All experimental procedures involving animals were performed in accordance with the Yale University and University of Iowa Animal Resources Center and Institutional Animal Care and Use Committee (IACUC) policies. Sufficient animals or samples were generated for each assessments using a power analysis based on previous studies and α = 0.05 and β = 0.2 Conditional hGFAP-cre fgfr2 knockout mice (referred to here as cKO) on a mixed background have been previously described [1, 8]. The conditional fgfr2 null allele harbors loxP recombination sites flanking regions encoding the Ig III binding and transmembrane domains of the fgfr2 gene (fgfr2f) [42]. Mice homozygous for fgfr2f alleles were crossed with mice expressing the Cre recombinase transgene under the control of the human GFAP promoter (hGFAP) [43]. The hGFAP-cre transgene targets Cre recombination to radial glia progenitors of the dorsal telencephalon starting at E13.5 [2]. Cre negative mice, littermates when possible, were used as control animals.
To assess the contribution of FGFR2 solely in the postnatal brain, mice homozygous for the fgfr2f alleles were crossed with GFAP-creERT2 (GCE) mice [44] also on a mixed background. The latter express a tamoxifen-inducible Cre recombinase-estrogen receptor fusion protein (CreERT2) [45] under the control of the GFAP promoter. *Specifying* gene knock outs to astroglia is challenging due to the overlapping nature of most genetic drivers in both postnatal neural stem cells and astroglia. Therefore, genetic approaches may have impacts on a small number of neural stem cells (which may impact neurons in the dentate gyrus and olfactory bulb) but will effect astroglia much more substantially; our approach must be considered with this caveat. Previous investigations of the GCE line demonstrate that it largely affects astrocytes; [44] in combination with the postnatal expression of FGFR2 in non-neuronal cells, postnatal induction with the GCE line principally targets fgfr2 loss in astrocytes. We used two different tamoxifen-induction protocols to target the knockout of fgfr2 at different time points. In the first neonatal protocol (referred to here as nKO), mother mice received intraperitoneal (IP) injections of 1 mg of tamoxifen dissolved in sunflower seed oil once daily for five consecutive days, beginning on postnatal day 1, 2, or 3 while Cre- and Cre+ experimental animals were nursing. In the second adult induction protocol (referred to here as iKO), Cre- and Cre+ adult mice received injections of 0.5 mg of tamoxifen dissolved in sunflower seed oil twice daily for five consecutive days at 2–4 months of age as previously [8]. Behavioral testing began at least 9 days after the time of the last tamoxifen injection. A few control animals with solely sunflower seed oil injection using the neonatal and adult protocols were created and tested on some behavioral assays
## Behavior testing
All behavior assessments were performed during the light cycle in a dedicated testing room with only one behavior assessment performed per day in the order described below, allowing mice to habituate to the testing room for 60 min prior to testing. Unless otherwise noted, mice remained in their home cage with cage-mates immediately before and after assessments. Each of the three types of knockout mice were tested alongside their control littermates. Only male mice were tested due to limited resources; childhood psychiatric disorders have a higher prevalence in males which allows for these data only in males, while limited, to be translationally informative [46]. Behavioral testing in each cohort was performed with mice grouped together for testing across close birthdates, with testing beginning for some mice in each cohort at age 2.5 months and others at 4.5 months of age. All testing was completed when mice were between 5.5 and 7.5 months of age.
Open Field: *In a* square or rectangular plastic arena, at least 1500 cm2 in area, mice were tested for locomotor activity for at least 50 min. Test mice were placed in the corner of the arena and their movements recorded using an overhead camera (Anymaze software; Stoelting Co, Wood Dale, Illinois). The main measure of distance traveled was evaluated in 5 min epochs and repeated measures ANOVA was used to evaluate group differences over time.
Social Approach: *In a* three-chamber social approach apparatus [47], mice were tested for social recognition. Two male “stranger” mice unfamiliar to the test mouse but of the same strain and age were first habituated for five minutes to small cylinders in each side chamber. Then, test mice were habituated to the center chamber for five minutes. Subsequently, the test mouse underwent a first stage—a ten-minute trial in which only one stranger was present in one side chamber. This was followed by a second stage—a ten minute trial in which the previous stranger mouse and a new stranger mouse were present in the two side chambers. Movement was recorded using an overhead camera and evaluated for the amount of time the test mouse spent in each chamber and in a one-inch diameter around the cylinders on each side (Anymaze). Social approach was calculated from behavior during the first stage taking the quotient of the time spent with the first stranger divided by the time spent in both sides overall. Social recognition was calculated from the second stage, using the time spent with the second stranger divided by the time spent in both sides overall. Statistical difference was evaluated with ANOVA and Student’s t-test.
Spontaneous Alternation: *In a* plastic Y maze with three 14 inch x 3 inch arms, mice were tested for working memory for 5 min. Mouse movement throughout the maze was monitored from live video recorded from above, noting arm entry order with experimenter blinded to group. For each set of three entries, the spontaneous alternation of those entries through all three arms was counted, and alternation percent calculated. Differences were evaluated with Student’s t-test.
Radial Arm Water Maze: Due to lack of resources, this task was not assessed in FGFR2 cKO mice. A 6-foot maze was filled with room temperature water and fitted with 6 dividers to create 6 radial arms., Mice were tested for spatial memory with distant visual spatial cues across two days with experimenter blinded to group [48]. Briefly, using visible and hidden platforms, mice were evaluated for working memory of platform location across 15 trials on day one. Day two of testing assessed consolidation of short-term memory using only the hidden platform across 15 trials. Working memory was calculated via arm entry errors and time to reach platform in three-trial blocks, with repeated measures ANOVA to evaluate group differences over time.
Elevated Plus Maze: *In a* Stoelting (Wood Dale, Illinois) Elevated Plus Maze, mice were tested for anxiety-like behavior for 5 min. Mouse movement throughout the maze was recorded using an overhead camera. The amount of time spent in each arm and the center of the maze was assessed (Anymaze). Differences were evaluated with repeated measures ANOVA across zones and with a t-test of the ratio of time spent in the open to closed zones.
## Gene expression
To evaluate the penetrance of Cre-mediated deletion of the fgfr2 gene and other genes related to glial regulation of brain function, dorsal forebrain, hippocampus, or medial frontal cortex was dissected out from brains of animals which were neonatally exposed to tamoxifen through maternal injection. RNA was isolated using standard Trizol methods and concentration assessed (Nanodrop Spectrophotometer, Thermo Scientific). cDNA (Superscript III First Strand Synthesis Kit, Invitrogen) was used to evaluate relative gene expression to β-actin using TaqMan primers (B-actin: predeveloped; vGat: Mm00494138_m1; vGlut1: Mm00812886_m1; Fgfr2: Mm1269938_m1) and GeneAmp PCR Mastermix in a StepOne™ Instrument (Applied Biosystems).
## Immunocytochemistry
At least 10 days after completion of behavioral testing, animals were anesthetized and perfused (phosphate buffered saline (1X PBS), then $4\%$ paraformaldehyde), and brain tissue was post-fixed, cryoprotected with a sucrose solution in 1X PBS and embedded in OCT compound, and cryostat (Leica, CM1900, Bannockburn, Illinois) sectioned at 50 µm thickness. Standard immunostaining methods were then used on free floating brain sections: blocking with $10\%$ goat serum in 1XPBS, TritonX-100 and Tween20, incubation with primary antibodies GFAP (DAKO Z0334, rabbit, 1:500), VGLUT1 (EMD Millipore AB5905, guinea pig, 1:4000), PSD95 (Abcam Ab12093, goat, 1:500), gephyrin (SySy 147021, mouse, 1:500), VGAT (SySy 131002, rabbit, 1:1000), glutamine synthetase (EDM Millipore MAB302, mouse, 1:500) washing the primary antibodies 3x in 1XPBS, incubation with Alexa dye-conjugated secondary antibodies, anti guinea-pig or anti mouse Alexa 594, anti-rabbit or anti-goat Alexa 488 (1:500; Molecular Probes), and coverslipping using mounting medium with DAPI (Vector Laboratories, #H-1200).
## Stereology
Glutamine synthetase+ cells in coronal tissue sections were measured using fluorescent microscopy with a Zeiss Axiolmager M2 microscope. Stereological estimates of hippocampus and medial frontal cortex (mFC) cell densities were calculated using optical fractionator approach and unbiased counting rules with 3-dimensional 150 × 100 × 10 μm counting frames, on a 450 × 450 μm grid for mFC and 600 × 600 μm grid for hippocampal CA, using a 40× objective lens with experimenter blinded to group (Stereoinvestigator; MBF Biosciences). Stereological counting to determine cell density, displayed as means and standard errors of the mean, was performed in 3–8 serial coronal sections (every 10th section) of the adult mFC and the hippocampus as previously described [49, 50].
## Astrocyte morphology
Astrocytes, GFAP + cells with well-delineated cell bodies and branches, in the mFC and hippocampus were randomly selected for morphology reconstruction. Ten z-stacks per cell were acquired at 100x and traced in each experimental group (Control and FGFR2 nKO) by using Neurolucida 11.03 (MBF Bioscience, Williston, VT USA) The coordinate files obtained by the 3D reconstruction were analysed in Neuroexplorer [51].
## Punctal assessment
VGLUT1, PSD95, and colocalized puncta as well as VGAT, Gephyrin, and colocalized puncta were assessed by imaging the adult mFC of the FGFR2 nKO with a Zeiss Axioimager M2 microscope equipped with ApoTome2. Four mice per group were examined. Eight z-stacks spanning the entire cortical layers were imaged at 40x with experimenter blinded to group. The ImageJ software (National Institute of Health) Puncta Analyzer plugin for the estimation of these puncta was used as previously described [52].
## Electron microscopy
We performed assessments of glial and synaptic structure as previously [53]. Electron microscopy photographs (16,300×) were used to first measure the perimeter of each neuronal profile analyzed, followed by determination of the amount of membrane covered by astrocytes with experimenter blinded to group. The results are reported as percentage of astrocyte coverage neuronal cell membrane. Synaptic boutons in direct contacts with the same neuronal profiles analyzed for glial coverage was determined as described in our earlier studies [53–55]. Synapse number is reported per 100 μm perikaryal membrane.
## Statistical methods
Data normality and variance was assessed with GraphPad Prism8 to select appropriate statistical tests. Graphs were made and two-tailed Student’s t-tests were performed with Microsoft Excel. ANOVA for repeated measures outcomes were performed with GraphPad Prism8. Outliers were excluded if they were >2 standard deviations from the mean.
## Results
Mice with conditional loss of FGFR2 starting in embryonic radial glia (cKO) have been previously described [1, 8]. In brief, the FGFR2 cKO resulted from recombination of the conditional fgfr2f alleles with the hGFAP-cre transgene, where *Cre is* expressed in radial glia beginning at embryonic day 13.5, therefore affecting all their neuronal and glial progeny in regions where the hGFAP-cre transgene is expressed, primarily the forebrain and cerebellum. An 80–$91\%$ loss of fgfr2 gene expression was previously found and a substantial reduction of FGFR2 protein level [1]. The adult astrocyte FGFR2 knock out (FGFR2 iKO) has also been previously described, with an $80\%$ reduction in fgfr2 gene expression, and was accomplished by recombination of the same fgfr2f alleles with the hGFAP-CreERT2 transgene, where Cre was expressed in postnatal GFAP+ glial cells after tamoxifen injection in adulthood [8].
To knock out FGFR2 in astroglial cells in the neonatal period (FGFR2 nKO), the same mice carrying fgfr2f alleles and the hGFAP-CreERT2 transgene received tamoxifen via the milk in the neonatal period by injecting the Cre negative dam with 1 mg tamoxifen once daily for 5 days. The reduction of fgfr2 assessed by qRT-PCR in juvenile or adult cortex or hippocampus varied between $29\%$ and $43\%$ (Supplementary Table 1). Control animals used for the FGFR2 nKO and FGFR2 iKO lines were also injected with tamoxifen to control for the potential impact of this manipulation.
## Embryonic knock-out of FGFR2: Behavioral changes
In FGFR2 cKO mice, multiple behavioral abnormalities were identified in addition to learning and memory deficits demonstrated previously [8]. Locomotor hyperactivity was found to be similar to that previously characterized in FGFR1 cKO mice (Fig. 1A; $55\%$ greater; rmANOVA: F[1,26] = 20.17, $$p \leq 0.0001$$) [12]. Compared to their control littermates, FGFR2 cKO mice were more active in an open field, also spending more time in the center of the open field (Fig. 1B; $$p \leq 0.04$$)), a phenotype suggesting reduced anxiety-like behavior. Fig. 1Adult male mice embryonically lacking FGFR2 driven by hGFAP-Cre (beginning by at least E13.5) showed locomotor hyperactivity, reduced anxiety-like behavior, increased sociability, and reduced working memory. A Persistently increased distance traveled in the open field in FGFR2 cKO mice. B Reduced anxiety-like behavior with open field increased time in the center in FGFR2 cKO mice. C Three chamber social task with social side compared with non-social side showed increased social preference in FGFR2 cKO mice. D Three chamber social task with familiar social side compared with novel social side showed no differences in social recognition. E Reduced working memory with less Y maze spontaneous alternation in FGFR2 cKO mice. F Reduced anxiety-like behavior with altered time spent in the closed and open arms of the EPM. G Reduced anxiety-like behavior with altered ratio of time in zones of the EPM. H Increase locomotor activity with increased overall entries into all arms of the EPM. $$n = 14$$,14; *$p \leq 0.05$ two-tailed Student’s t-tests or ANOVA. Means and SEM shown.
FGFR2 cKO mice also showed alterations in social approach. When tested on their social preference, they demonstrated a small but significantly higher preference than controls for interacting socially with a novel stranger versus spending time with a novel object (Fig. 1C; ANOVA interaction: F[1,55] = 12.24, $$p \leq 0.0009$$). This higher sociability was true in approach behavior in close proximity to the novel mouse or object (sociability index $32\%$ increase: cKO: 0.77 vs controls: 0.58, $$p \leq 0.0003$$), as well as in larger chambers (sociability index $26\%$ increase: cKO: 0.68 vs controls: 0.54, $$p \leq 0.002$$). This effect was not attributable to altered social recognition, as both cKO and control mice had similar interaction with a familiar versus a stranger mouse target (Fig. 1D; recognition indices: large chamber cKO: 0.60 vs controls: 0.53, $$p \leq 0.19$$; close proximity cKO: 0.64 vs controls: 0.61, $$p \leq 0.46$$).
Working memory was impaired in FGFR2 cKO mice (Fig. 1E, $$p \leq 0.048$$), as shown by a small but significant $10\%$ lower spontaneous alternation in a Y maze.
Behavior on the elevated plus maze was also significantly different from control littermates overall (Fig. 1F, ANOVA interaction: F [2,78] = 7.991, $$p \leq 0.0007$$). Mice demonstrated a small effect on anxiety-like behavior, with less time spent in the closed arms of the maze ($$p \leq 0.02$$) and a lower ratio of time across the elevated plus maze zones (Fig. 1G; $$p \leq 0.03$$), but no difference in the ratio of entries across zones (data not show; $$p \leq 0.32$$). Performance on the elevated plus maze, a different environment than an open field, also confirmed the higher activity level of these mice regardless of context (Fig. 1H; $$p \leq 0.001$$).
## Neonatal knock-out of FGFR2: Behavior changes
The behavior of FGFR2 nKO mice, lacking FGFR2 signaling only in GFAP+ astroglial cells beginning in the neonatal period, was characterized on the same tasks described above (Fig. 2A–H) as well as further characterization of working memory with a radial arm water maze. Compared to Cre- negative littermates with the same tamoxifen exposure, FGFR2 nKO animals showed a small but significant $32\%$ increase in locomotor activity in an open field but no increase in time in the center (Fig. 2A, B; rmANOVA: F [1,15] = 5.542 $$p \leq 0.03$$; center time $$p \leq 0.97$$). We also examined activity level of FGFR2 nKO mice compared to an additional control group—a small sample of vehicle/oil injected Cre+ controls. Activity of FGFR2 nKO mice also trended increased by this comparison (rmANOVA $$n = 3$$ oil inj Cre+ vs $$n = 10$$ tam inj Cre +, rmANOVA: F [1, 11] = 4.574, $$p \leq 0.056$$, Supplementary Fig. 1A), and tamoxifen injection itself compared to oil in Cre- mice did not alter open field behavior. Fig. 2Adult male mice early postnatally lacking FGFR2 driven by GFAP- CreERT2 (induced with neonatal tamoxifen injections P1-7) showed locomotor hyperactivity, reduced anxiety-like behavior, increased sociability, and reduced working memory. A Persistently increased distance traveled in the open field in FGFR2 nKO mice. B No differences in anxiety-like behavior with open field time in the center. C Three chamber social task with social side compared with non-social side showed increased social preference in FGFR2 nKO mice. D Three chamber social task with familiar social side compared with novel social side showed no differences in social recognition. E Reduced working memory with less Y maze spontaneous alternation in FGFR2 nKO mice. F Reduced working memory with increased errors in the Radial Arm Water Maze training trials in FGFR2 nKO mice. G Reduced anxiety-like behavior with altered time spent in the closed and center arms of the EPM. H Reduced anxiety-like behavior with altered ratio of time in zones of the EPM. I Increased locomotor activity with increased overall entries into all arms of the EPM. $$n = 10$$,10; *$p \leq 0.05$ two-tailed Student’s t-tests or ANOVA. Means and SEM shown.
FGFR2 nKO mice also showed the same small increased preference for social interaction indicating higher sociability (Fig. 2C: ANOVA interaction: F [2,28] = 3.343, $$p \leq 0.04$$; sociability index: large chamber $31\%$ increase: nKO: 0.76 vs controls: 0.58, $$p \leq 0.02$$; close proximity $21\%$ increase: nKO: 0.75 vs controls: 0.62, $$p \leq 0.11$$) without a deficit in social recognition (social recognition index: large chamber: nKO: 0.64 vs controls: 0.57, $$p \leq 0.49$$; close proximity: nKO: 0.70 vs controls: 0.66, $$p \leq 0.64$$) (Fig. 2C, D).
Working memory on the Y maze was trend deficient in FGFR2 nKO mice to the same small extent ($12\%$) as shown for FGFR2 cKO mice (Fig. 2E, $$p \leq 0.07$$). Working memory was further measured by performance on the radial arm water maze (Fig. 2F). This task also showed that working memory, as measured by errors during the training phase when animals must keep location information in working memory, was significantly impaired with small effect size in FGFR2 nKO mice (rmANOVA: F (2.725, 49.06) = 5.363, $$p \leq 0.0037$$).
Measures of anxiety-like behavior on the elevated plus maze was also shown to be altered in FGFR2 nKO mice in a comparable fashion to the FGFR2 cKO mice, with a small reduction in anxiety-like behavior: less time in the closed arms of the maze and more time in the center (Fig. 2G, ANOVA interaction: F [2,48] = 3.414, $$p \leq 0.04$$; $$p \leq 0.08$$, $$p \leq 0.048$$). FGFR2 nKO mice showed a trend higher ratio of time in the open to closed zones (Fig. 2H, $$p \leq 0.08$$), but no difference in the ratio of entries in the zones (data not shown; $$p \leq 0.34$$). We also examined these differences in the vehicle/oil injected animals and found that tamoxifen injection which induced the FGFR2 nKO was needed to see this effect (Supplementary Fig. 1B). Just as seen in FGFR2 cKO mice, levels of activity in FGFR2 nKO mice were confirmed to be increased to a small extent on the EPM compared to controls (Fig. 2I, $$p \leq 0.01$$).
In summary, the induced loss of FGFR2 in neonatal astrocytes resulted in many alterations on the same behaviors as those noted here in the FGFR2 cKO mice in which the FGFR loss starts in radial glial cells in the embryonic period—social preference behavior, spontaneous alternation rate, and elevated plus maze closed arm time and total entries were changed to similar extents in both types of FGFR2 deficit mice. In FGFR2 nKO mice, open field activity and elevated plus maze time ratio were increased as in FGFR2 cKO animals but not to the same extent; additionally, open field center time was not increased in FGFR2 nKO mice unlike FGFR2 cKO animals.
## Adult Knock-Out of FGFR2: Behavior Changes
In contrast to the comparable behavioral alterations of FGFR2 cKO and nKO mouse models, FGFR2 iKO mice, lacking FGFR2 signaling only in GFAP+ glial cells beginning in adulthood, showed no alterations of locomotor activity on the open field, working memory on the Y maze or the radial arm water maze, or social preference (Fig. 3A, C, D, E, F). The similar behavior of Cre- control and Cre+ FGFR2 iKO on many behaviors validated that the Cre transgene was not, itself, a source of behavioral differences. We verified that Cre- control mice injected with tamoxifen in adulthood did not differ from Cre- control mice injected with tamoxifen in the neonatal period when considering locomotor activity on the open field, working memory on the Y maze, or social preference (Supplementary Fig. 2A–C).Fig. 3Adult male mice lacking FGFR2 in adulthood driven by GFAP- CreERT2 (induced with adult tamoxifen injections P56-60) showed only reduced anxiety-like behavior. A No difference in distance traveled in the open field. B Reduced anxiety-like behavior with open field increased time in the center in FGFR2 iKO mice. C, D Three chamber social task showed no difference in social preference or social recognition. E No difference in working memory with Y maze spontaneous alternation. F No difference in working memory with errors on Radial Arm Water Maze training trials. G Reduced anxiety-like behavior with altered time spent in the closed and open arms of the EPM. H Reduced anxiety-like behavior with altered ratio of time in zones of the EPM. I No difference in locomotor activity with overall entries into all arms of the EPM. $$n = 7$$,10; *$p \leq 0.05$ two-tailed Student’s t-tests or ANOVA. Means and SEM shown.
Differences of FGFR2 iKO mice from wild type littermates were found only for anxiety-like behavior. On the elevated plus maze (Fig. 3G, ANOVA interaction: F [2, 36] = 5.260, $$p \leq 0.0099$$) these FGFR2 iKO mice demonstrated trend less time spent in the closed arms ($$p \leq 0.05$$), more time in the open arms ($$p \leq 0.08$$), and higher ratio of time in the open to closed zones (Fig. 3H; $$p \leq 0.07$$), indicative of less anxiety-like behavior, but no differences in overall activity level (Fig. 3I) or in the ratio of entries in the zones (data not shown; $$p \leq 0.23$$). There was no effect of adult oil injection itself on elevated plus maze performance (Supplementary Fig. 1C), confirming that the adult FGFR2 iKO with tamoxifen was needed for this effect. This effect was clear despite the increased open arm time in Cre- control mice injected with tamoxifen in adulthood (Supplementary Fig. 2D). Decreased anxiety-like behavior was also demonstrated by increased time spent in the center of the open field (Fig. 3B, $$p \leq 0.02$$).
## Neurobiological findings
Given the multiple behavioral abnormalities induced when FGFR2 was lacking only beginning in neonatal life in primarily astrocytes, we performed pilot investigations of their neurobiology focused mainly on hippocampus as a major region implicated in regulation of the behaviors assessed here. We first assessed GFAP + astrocyte density in the hippocampus of FGFR2 nKO mice, finding no differences ($$n = 3$$,3; $$p \leq 0.36$$, control = 8.45 ± 0.97 × 10−6, FGFR2 nKO=10.14 ± 1.37 × 10−6 cells/µm3). The volume of the hippocampus was also unchanged ($$n = 3$$,3, $$p \leq 0.86$$, control = 3.1 ± 0.6 mm3, FGFR2 nKO = 3.2 ± 0.6 mm3). In addition, the most broadly affected model used here, FGFR2 cKO mice, showed no deficit in GFAP + cell density either ($$n = 3$$,3; $$p \leq 0.32$$, control = 9.26 ± 1.05 × 10−6, FGFR2 cKO = 8.44 ± 1.69 × 10−6 cells/µm3). This suggested that astrocyte numbers themselves were intact regardless of early loss of FGFR2.
To gain insights into astrocyte morphology after early postnatal loss of FGFR2 in astroglia, we performed electron microscopy (EM) analyses of the hippocampus in FGFR2 nKO mice. The analysis of the astrocytic coverage of the cell membrane of neurons in the principal cell layer of the CA3 region of the hippocampus showed reduced coverage by almost half (Fig. 4A–C). Of note, this measure of astrocyte-neuron contact was coupled to increased number of synapses on the same cells (Fig. 4D).Fig. 4Adult male mice early postnatally lacking FGFR driven by GFAP- CreERT2 (induced with neonatal tamoxifen injections P1-7) show reduced astrocyte processes in neuropil by electron microscopy. A–D Less glial coverage of perikaryal membrane (black lines) and more synapse number on the same membrane (asterisks) in FGFR2 nKO compared to control littermate mice in the hippocampus CA3 region (30 cells per genotype). $$n = 3$$,3. These mice also show increased density of pre-synaptic proteins assessed by immunofluorescence. E–H Greater number of puncta of vGLUT1 and vGAT per field of analysis in lateral cortex. I, J Greater number of vGAT puncta in cortical layers I-III when evaluated separately from cortical layers IV-VI. $$n = 4$$,4; *$p \leq 0.05$, **$p \leq 0.01$ two-tailed Student’s t-tests or ANOVA. Means and SEM shown.
The loss of FGFR2 signaling did influence the expression of glutamine synthetase (GS), a metabolic component of glial cells which is regulated by glutamate transport and synaptic activity. In FGFR2 nKO, the density of glutamine synthetase positive cells, as measured by stereological counts in adult brains, trended increased in hippocampus and was also increased to a greater extent in cerebral cortex of FGFR2 nKO mice (Table 1). Regardless of these changes, gross astrocyte morphology in hippocampus was qualitatively normal (Supplementary Fig. 3A). Increased glutamine synthetase expression was also found in the hippocampus after embryonic knock out of FGFR2 (FGFR2 cKO) which had similar behavioral alterations to nKO mice and also lacked FGFR2 in astrocytes from early developmental stages. However, glutamine synthetase change was not seen in FGFR2 iKO mice (Table 1).Table 1Glutamine Synthetase+ cell density differences with reduction of FGFR2 signaling. Control Mean ± SEM cells x 10−5/μm3Experimental Mean ± SEM cells x 10−5/μm3Differencep-valueFGFR2 nKO Medial Frontal Cortex0.23 ± 0.01 ($$n = 3$$)0.47 ± 0.05 ($$n = 3$$)↑$104\%$0.01*FGFR2 nKO Hippocampus1.20 ± 0.15 ($$n = 3$$)1.99 ± 0.43 ($$n = 3$$)↑$68\%$0.07¥FGFR2 cKO Hippocampus1.55 ± 0.06 ($$n = 4$$)2.05 ± 0.13 ($$n = 6$$)↑$30\%$0.02*FGFR iKO Hippocampus1.77 ± 0.19 ($$n = 3$$)2.09 ± 0.40 ($$n = 3$$)↑$18\%$0.55*$p \leq 0.05$, ¥-trending significance, 0.05 ≤ $p \leq 0.10.$
With the greater change in astrocyte glutamine synthetase expression in the cortex of the FGFR2 nKO mice, we examined cortical density of synaptic proteins in adult FGFR2 nKO mice, as assessed by immunohistochemical puncta analysis. FGFR2 nKO mice were found to have increased puncta density after immunostaining for both the GABA neurotransmitter release protein, vGAT, and the glutamate neurotransmitter release protein, vGLUT1 (Fig. 4E–J). In contrast, the density of post-synaptic protein puncta (gephyrin and PSD95) or co-localized pre- and post-synaptic protein densities were unchanged (data not shown). The vGAT and vGLUT1 punctal increases were localized to upper cortical layers I-III (Fig. 4I, J).
Lastly, we examined gene expression of these same synaptic proteins in the hippocampus of another cohort of FGFR2 nKO mice and controls ($$n = 8$$,6). GABA transporter vGat (by RT-qPCR) was increased and synaptic glutamate transporter vGlut1 trended increased in the juvenile hippocampus ($$n = 8$$,5; vGat: $p \leq 0.005$, control = 1.00 ± 0.07, FGFR2 nKO = 3.62 ± 0.91; vGlut1: $$p \leq 0.09$$ control = 1.00 ± 0.12, FGFR2 nKO = 3.70 ± 1.86). These findings suggest that early postnatal glial FGFR2 loss in the dorsal forebrain induces a decrease in astrocyte-neuron contacts and potentially an increase in neuronal synaptic contacts, as shown by EM and level of presynaptic marker expression, particularly in hippocampus.
## Discussion
We have demonstrated a distinct behavioral triad of hyperactivity, working memory deficits, and increased sociability in mice lacking FGFR2 in astroglial cells, only when that loss begins by at least the neonatal period of development. In concomitance with this behavior, the loss of FGFR2 in neonatal astroglial cells results in data suggesting decreased astroglia-neuron membrane appositions as well as increased neuronal synapses and their signaling proteins, as shown by EM and puncta density of presynaptic vesicular proteins. We further show that expression of GS, a critical astrocytic protein for both glutamatergic and GABAergic synaptic function [56, 57], is increased in astroglia. We hypothesize that the increase in GS immunostaining likely represents a functional aftereffect of increased neuronal signaling, informed by others’ findings that GS expression is influenced by neuronal activity [56, 58]. Distinct from these phenotypes, we found that a small decrease in anxiety-like behavior was present in animals with induced loss of FGFR2 in GFAP + cells at all developmental time periods, even when induced only in adulthood. This suggests that hyperactivity, working memory deficits, and increased sociability might be related to the developmental roles of FGFR2 in astrocytes in the neonatal period, whereas the modest decrease in anxiety-like behavior might reflect a continuous, ongoing role of FGFR2 in the functioning of astroglial cells as we previously demonstrated for short term memory [8].
Fibroblast growth factor signaling in the brain has previously been investigated for its role in embryonic patterning and regulation of neurogenesis [19] or, in adulthood, for its implications in behavioral alterations, such as anxiety-like behavior and learning [8, 10, 13]. Because hyperactivity, working memory deficits, and increased sociability were present here when FGFR2 was lacking from the neonatal period onward but not when the loss was induced in adulthood, these results support a new line of thinking, implicating fibroblast growth factor signaling in the early postnatal brain [59] and further suggest that these processes may affect the risk for behavioral disorders [38]. Convergent data on knock out of early postnatal FGF22, a ligand partner of FGFR2, affecting anhedonia supports this idea [18]. These findings together support the notion of “sensitive periods” in development, implying a crucial role of FGFR2 signaling in the perinatal/juvenile period.
The three cohorts here with FGFR2 loss at different time points, as well as in different subsets of cells, showed some similarity and some difference. All three FGFR2 KO lines which had in common their lack of FGFR2 in astroglial cells in adulthood showed small reductions in anxiety-like behavior in the elevated plus maze, as assessed by small shifts in time from closed to open zones; open arm time, the metric most robustly associated with anxiety-like constructs in the literature, was not always increased and EPM findings were only trending at times. These findings of small effect may reflect FGFR2’s mixed roles with multiple ligands (FGFs and other molecules which bind to FGFR2) [60, 61] in regulating anxiety-like behavior [10, 62, 63]. The clearest findings were in the adult-induced FGFR2 knock-out mice (FGFR2 iKO); this suggests that the reduced anxiety-like behavior found in embryonically-induced FGFR2 knock-out (FGFR2 cKO) and FGFR2 nKO mice may also be attenuated by the other roles FGFR2 plays in early development potentially underlying their other behavioral deficits. For example, greater locomotor hyperactivity in the FGFR2 cKO mice compared to FGFR2 nKO and FGFR2 iKO may interact with their anxiety-like behavior regulation in complex ways.
More distinct differences between these three FGFR2 KO lines included the presence of the deficits in hyperactivity, sociability, and working memory in only FGR2 cKO and FGFR2 nKO mice. Analysis of results from vehicle/oil injected mice and comparing mice tamoxifen-injected at different ages provided reassurance that differences were due to the knock out of fgfr. The 25–$30\%$ increase in the social preference index was similar in both these mouse lines and involved increased time spent with a novel stranger mouse relative to time spent with an empty social interaction cup. This may reflect a deficit in typical down-regulation of social approach over time or an increased drive for social reward. While models for the study of neuropsychiatric disorders commonly demonstrate decreased sociability [64], increased rodent social interaction on a variety of tasks has also been demonstrated in a number of studies with different genetic backgrounds relevant to autism spectrum disorder (ASD), schizophrenia, intellectual disability, mood disorders, and attention deficit hyperactivity disorder (ADHD) [64–71]. Other studies have demonstrated heritable patterns of increased social interaction in monkeys which has also been described as social impulsivity [72]. Directionality of behavior on non-human animal social tests cannot be directly compared to human social interactions, although may generally inform the understanding of the development of neural systems underlying social behavior.
Further behavior similarities arose when FGFR2 loss began early in development. While working memory in FGFR2 cKO mice was only tested with the Y maze, this Y maze deficit was very similar to that in the FGFR2 nKO mice (small $10\%$ and $12\%$ deficits respectively) which also showed a radial arm water maze working memory deficit. These deficits were distinct from the short and long term memory deficits previously identified with FGFR2 iKO and cKO mice respectively [8], but findings here were similar to other working memory deficits found with targeted astrocyte dysfunction [73, 74] and models for the study of ADHD [75]. Lastly, hyperactivity levels were much greater in FGFR2 cKO compared to FGFR2 nKO mice ($55\%$ vs $32\%$), suggesting that the earlier and greater loss of FGFR2 expression including in all dorsal forebrain neurons and glia may underlie this outcome.
The importance of glial cells during early developmental time periods has been suggested by other lines of research. Glial proliferation occurs predominantly in the last few embryonic days and first few postnatal weeks of mouse development which also may be a critical time period for establishing these cells’ own later functioning. Although FGF signaling regulates glial proliferation and fate [31, 76], astrocyte density was not reduced by the early loss of FGFR2 in this study. This suggests that this process is redundant between different FGFRs and/or is regulated by non-FGFR2 mechanisms. In the neonatally-induced loss of FGFR2 in GFAP expressing cells, impacts on behavior may be from alterations in more differentiated glial cells. We cannot exclude that loss of FGFR2 in a small number of neural stem cells and their neuronal progeny targeted by GFAP-creERT2 also plays a role in behavioral impacts in the tamoxifen-induced lines. However, regardless of possible inclusion of neuronal FGFR2 signaling in these mechanisms, there were definitive impacts on astrocytes. Our neurobiological data suggest that the deficiency of FGFR2 has an impact on astrocyte differentiation and maturation, processes that occur in the early postnatal period in mouse brain, and which may result in abnormal astrocyte ultrastructure. Astrocyte maturation is regulated by multiple FGF ligands, including changes in protein levels that occur as astrocytes mature: for example, upregulation of the glutamate transporter, GLT-1, and downregulation of GFAP [31]. Our data suggests that FGFR2 may be an important signaling partner for these FGF ligands in these processes.
Early postnatal astrocytes are in a distinct phase of differentiation; [77] as astrocytes develop, cellular extensions are made to promote astroglia-neuron interactions [78], the extent of which we found to be deficient at the ultrastructural level with FGFR2 knockout, similar to findings in drosophila lacking the FGF receptor, Heartless [79]. Decreased FGF signaling reduced astrocytic coverage of neuronal membranes, with synaptic processes increased in these same glial-neuronal couplets. Astrocyte-neuron contacts may in turn regulate the number of neuronal synapses during the early postnatal period by competitive processes or by affecting pruning of synapses, a process that is time-dependent [80, 81] and regulated by astrocytes [80, 82]. Synaptic pruning during these sensitive periods of development allows for experience to shape brain development [38, 39]. In sum, the present study suggests a possible competitive antagonism between astrocyte/neurons at synapses which may be important in regulation of neuronal signaling and synaptic pruning in early postnatal development. These data suggest intriguing mechanisms that should be assessed in future studies.
Given that punctal density of vesicular proteins was increased in neonatally-induced FGFR2 knockout mice when assessed by immunocytochemistry, a disruption of astrocyte regulation of neuronal signaling is likely. Increased glial metabolism, as shown through increased glutamine synthetase positive cells, may reflect a response to a higher level of neuronal signaling from increased vesicular proteins. A similar reduction in FGFR2 signaling in glial cells just a few days later in juvenile brain (postnatal day 8) decreased vesicular proteins [9] suggesting a dynamic role of this receptor with shifting impacts on neuronal structure and functioning as synapse formation, elimination, and maintenance occurs in the developing dorsal forebrain.
Astrocyte functioning has also been implicated in psychiatric disorders [83–85], with a main focus in adult psychopathology. The role of astrocytes in common psychiatric disorders of childhood, in which clinical impairment and pathophysiological mechanisms clearly begin earlyis potentially important given the early timing of astrocyte maturation [41, 86, 87]. Glial functioning may be altered in patients with ASD [88–90] and ADHD [91, 92]. Clinical investigations in ADHD show decreased cortical surface area during childhood [93], which has implications for many aspects of neurobiology beyond dopamine and norepinephrine signaling that are targeted by current treatments. Proton magnetic resonance spectroscopy studies have implicated glutamate-glutamine metabolism in ADHD, which is highly dependent on astrocyte functioning [94]. Especially intriguing, as shown by the results here, is the potential role in childhood psychopathology of astrocytes in their early postnatal phases of differentiation. Astrocyte differentiation could be affected by early postnatal developmental insults [58, 95] but could also be a mechanism by which inherited risk for ADHD and other disorders is manifested.
The range of behavioral alterations manifested by the glial-targeted FGFR2 mice resembles that which individuals with ADHD-combined type display; the diagnostic criteria include increased locomotor activity, poor attention—relevant to working memory deficits— and impulsivity which leads to dysregulated increased social interaction. Some other models for the study of ADHD consistently show all of these impacts, although social approach is not a task typically assessed [75]. Because of the importance of social functioning for children’s success, we suggest this may be an important consideration for future studies. This and other studies suggest the possibility that diagnostic and treatment options for ADHD could be developed to incorporate glial functioning as a target generally or specifically with nanomedicine technological advances [96]. Treatments with great efficacy for ADHD exist. However, because of the relatively common occurrence of the disorder [97], significant numbers of children, adolescents, and adults are not successfully treated and experience great impairments in their functioning. Advancements in understanding the neurobiology of ADHD has the potential to benefit many.
## Supplementary information
Supplemental Material The online version contains supplementary material available at 10.1038/s41398-023-02372-y.
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|
---
title: A cybergenetic framework for engineering intein-mediated integral feedback
control systems
authors:
- Stanislav Anastassov
- Maurice Filo
- Ching-Hsiang Chang
- Mustafa Khammash
journal: Nature Communications
year: 2023
pmcid: PMC10008564
doi: 10.1038/s41467-023-36863-9
license: CC BY 4.0
---
# A cybergenetic framework for engineering intein-mediated integral feedback control systems
## Abstract
The ability of biological systems to tightly regulate targeted variables, despite external and internal disturbances, is known as Robust Perfect Adaptation (RPA). Achieved frequently through biomolecular integral feedback controllers at the cellular level, RPA has important implications for biotechnology and its various applications. In this study, we identify inteins as a versatile class of genetic components suitable for implementing these controllers and present a systematic approach for their design. We develop a theoretical foundation for screening intein-based RPA-achieving controllers and a simplified approach for modeling them. We then genetically engineer and test intein-based controllers using commonly used transcription factors in mammalian cells and demonstrate their exceptional adaptation properties over a wide dynamic range. The small size, flexibility, and applicability of inteins across life forms allow us to create a diversity of genetic RPA-achieving integral feedback control systems that can be used in various applications, including metabolic engineering and cell-based therapy.
Homeostasis and robust perfect adaptation are remarkable features of living cells. Here, to synthetically achieve this, the authors present a theoretical and experimental framework using inteins to implement compact biomolecular integral feedback controllers.
## Introduction
One of the essential features of living systems is their ability to maintain a robust behavior despite disturbances coming from their external uncertain and noisy environments. This feature is referred to as homeostasis, which is typically achieved via endogenous feedback regulatory mechanisms shaped by billions of years of evolution. Pathological diseases are often linked to loss of homeostasis1,2. As a result, restoring homeostasis has become a major focus of research in the emerging field of cybergenetics3, which combines control theory and synthetic biology. In particular, the rational design and implementation of biomolecular feedback controllers4–13 offers promising candidates that may accompany or even replace such failed mechanisms14–16.
A notion, which is similar to homeostasis, but more stringent, is Robust Perfect Adaptation (RPA) (see e.g.17,18) which is the biological analogue of the well-known notion of robust steady-state tracking in control theory. A controller succeeds in achieving RPA if it drives the steady state of a variable of interest to a prescribed level despite varying initial conditions, uncertainties and/or constant disturbances. Motivated by the internal model principle19, which establishes that the designed controller must implement an integral feedback component to be able to achieve RPA, the antithetic integral feedback (AIF) controller20 was brought forward. The basic antithetic integral feedback motif is depicted in Fig. 1(a). It is comprised of two species Z1 and Z2 whose end goal is to robustly steer the concentration of the output species of interest XL to a prescribed level, referred to as the setpoint, in spite of disturbances and uncertainties in the regulated network — represented here as the various reactions occurring between species X1 through XL. RPA is achieved via four controller reaction channels. First, Z1 is constitutively produced at a rate μ to encode for the setpoint. Second, Z2 is catalytically produced from the output species XL at a rate θxL to sense its concentration. The third reaction is the annihilation or sequestration reaction between Z1 and Z2 occurring at a rate ηz1z2. The sequestration reaction encodes a comparison operation and produces an inactive complex that has no function and thus its concentration need not be mathematically tracked. Finally, the feedback control action (actuation) is encrypted as a production reaction of the species X1, which acts as the input of the regulated network, at a rate kz1 proportional to the concentration of controller species Z1. The underlying Ordinary Differential Equations (ODEs) governing the dynamics of the concentrations of Z1 and Z2 are shown in Fig. 1(a). Throughout the paper, bold uppercase letters (e.g. Z1) denote the names of biochemical species, while their corresponding lowercase letters (e.g. z1) denote their concentrations. By looking at the dynamics of z1−z2, it is straightforward to reveal the integral control action where the temporal error μ/θ−xL(t) at time t, or deviation of the output concentration from the setpoint μ/θ, is mathematically integrated. This establishes that, as long as the closed-loop system is stable (i.e. asymptotically converges to some fixed point), the output concentration xL will converge to the prescribed setpoint μ/θ which is independent of the regulated network and initial conditions, and thus achieves RPA. While RPA is a steady-state property, the transient dynamic properties and tuning of the antithetic integral controller are also extensively studied as well21–23.Fig. 1Overview.a The closed-loop system is comprised of the controller network (Z1, Z2) connected in a feedback configuration with an arbitrary regulated network. By examining the controller dynamics, it is straightforward to uncover the integral control action that endows the closed-loop system with RPA. That is, as long as the closed-loop system is stable, the concentration of the regulated output XL converges to a prescribed value μ/θ, referred to as the setpoint, despite the presence of disturbances and uncertainties in the regulated network. b The heart of the basic AIF motif is the sequestration reaction. In this paper, we exploit the exquisite flexibility of split inteins to genetically implement a broad class of integral controllers that endow the closed-loop system with RPA. The flexibility of split inteins offers an easy-to-build biological framework at the price of (potentially) more complex mathematical models. To this end, we establish a set of simple reaction rules that enable RPA. c The shaded blue box schematically depicts the products of intein-splicing reactions starting from the educts. The first schematic (top left) describes the general split intein-splicing reaction where both split inteins are flanked by protein domains, labeled \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\,{{\mbox{N}}},{{{\mbox{N}}}}^{{\prime} })$$\end{document}(N,N′) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\,{{\mbox{C}}},{{{\mbox{C}}}}^{{\prime} })$$\end{document}(C,C′) for the N- and C-terminal protein sequences, respectively. The first product is a new protein containing the N and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mbox{C}}}}^{{\prime} }$$\end{document}C′ domains of the educts, while the second product is a heterodimer containing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{\mbox{N}}}}^{{\prime} }$$\end{document}N′ and C, which are held together by the two inactive split inteins (see Supplementary Information, Fig. 22). The remaining four schematics in the shaded blue box are instantiations of the general case and are labeled according to the perspective of the protein containing the IntN segment. As the labels suggest, a part of the protein sequence is either exchanged for another one or removed through cleavage. Furthermore, it is possible to ligate another sequence to the protein of interest or make it non-responsive to future splicing reactions through intein removal. To illustrate the design modularity and flexibility, we list a selection of intein-based implementation examples of the antithetic sequestration motif (below the shaded blue box) based on the described possible splicing reactions. In the first example (bottom left), inteins are used to shuffle proteins between the nucleus and the cytoplasm due to the NLS and the NES which flank the protein sequences. In particular, the intein-splicing reaction exchanges the NLS with a NES which leads to the export of a transcription factor (TF) out of the nucleus where it cannot initiate transcription anymore. In the second example, inteins are used to exchange an AD by a RD, which inverts the function of the TF. In the third example, a split inteins is introduced within a functional domain without disturbing it. The splicing reaction results in the cleavage of the domain rendering it nonfunctional. In the fourth example, a protein is fused to the first split-intein and a split-degradation tag, while the second split-intein is fused to the other half of the split-degradation tag. The splicing reaction re-ligates the degradation tag rendering it functional and capable of degrading a POI. In the final example, a DBD can reversibly heterodimerize with an AD via its split inteins. Note that the split intein on the DBD is mutated so that it cannot perform the splicing reaction upon dimerization. A separate non-mutated intein is able to remove the intein from the AD through splicing. This renders the AD unable to heterodimerize with the DBD. AD: activation domain, RD: repressing domain, IntC: intein C, IntN: intein N, DBD: DNA binding domain, NLS: nuclear localization signal, NES: nuclear export signal, N-Deg: N terminus of split degradation domain, C-Deg: C terminus of split degradation domain. d A simple recipe is developed to reduce the otherwise mathematically complex controller models into simple motifs that resemble the basic AIF motif, but with a fundamental difference: the sequestration product is allowed to have a function that can be leveraged as a tuning knob to enhance the controller performance while maintaining RPA.
In vivo antithetic integral feedback controllers have been previously built in both bacteria4,6 and mammalian cells7, where RPA is experimentally demonstrated. A quasi-integral controller using a slight variant of the antithetic controller was also demonstrated in Escherichia coli9. More recently, a protein based antithetic controller in mammalian cells was also recently proposed24. In6, the controller is implemented in E. coli using sigma/anti-sigma factors as the basic parts that realize the sequestration reaction — the heart of the antithetic integral motif. In7, the controller is implemented in HEK293T cells using sense/anti-sense mRNAs. The sequestration reactions in both designs are achieved by the heterodimerization of Z1 and Z2. In the case of sigma/anti-sigma factors, the heterodimerization reaction is reversible, a fact that may lead to reduced performance in certain operating regimes. Moreover, when present in high quantities, xenogenic sigma factors may be toxic due to their inherent property of sequestering RNA polymerases from housekeeping sigma factors25. Finally, sigma factors are specific to the transcription mechanism of bacteria and cannot be easily transferred to other domains of life, which is why the sense/anti-sense RNA controller7 was developed for mammalian cells. At the same time, sense/anti-sense hybridizations produce double-stranded RNAs that, in high abundance, may initiate global translational repression26, leading to a reduction in the effective dynamic range of operation. These constraints give rise to the need for genetic parts that are nontoxic, are transferable between different forms of life, and enjoy wider dynamic ranges of operation. Nevertheless, the suitable choice of genetic parts is a difficult task because they need to adhere to the strict design rules of the basic antithetic integral feedback motif. In this paper, we show that split inteins serve as the ideal candidate parts that are capable of doing both: adhering to the design rules and avoiding the aforementioned disadvantages. We build on the universality result of the antithetic motif6 to examine more complex integral controller designs for RPA as demonstrated in Fig. 1(b). While more complex mathematical topologies do not have to be necessarily more difficult to implement, they certainly broaden the biological design space. This expansion, in fact, becomes necessary due to the biological implementation constraints.
An intein is a protein segment that is capable of autocatalytically excising itself from the protein while re-ligating the remaining segments, called exteins, via forming new peptide bonds27 (see Supplementary Information, Fig. 22). Inteins are universal as they can be naturally found in all domains of life spanning eukaryotes, bacteria, archaea and viruses28,29. Split inteins - a subset class of inteins - are, as the name suggests, inteins split into two halves commonly referred to as IntN and IntC. Split inteins have been widely studied and characterized due to their extensive usage in various life science disciplines and their ability to perform fast, reliable and irreversible post-translational modifications30–34. Small split inteins like Gp41-1C are comprised of around 40 amino acids35 and are well within the size range of synthetic protein linkers36. It is then possible to use them as “functional” linkers to connect different protein segments. The split inteins, when active, are capable of heterodimerizing and performing protein splicing reactions on their own where they irreversibly break and form new peptide bonds in a strict stoichiometric ratio of one to one. We shall refer to these reactions as “intein-splicing reactions” where molecules containing active IntC segments react with molecules containing active IntN segments to undergo a particular splicing mechanism. When two molecules undergo an intein-splicing reaction, the IntN and IntC segments are permanently inactivated as they are unable to perform further splicing reactions due to the alteration of their respective biochemical structures. However the products of such a reaction may still have other functions such as activating or repressing gene expression due to the presence of other protein domains that may not be affected by the splicing reaction. Split inteins can be exploited to exchange, cleave or ligate amino acid sequences (see Fig. 1(c)). These features serve as the basis of realizing the sequestration reaction of the antithetic integral motif. A selection of antithetic “sequestrations” based on functional conversion, spatial separation, inactivation, degradation and intein removal are shown in Fig. 1(c) to emphasize the modularity and the vast flexibility of intein-based designs. Nonetheless, this high design flexibility comes with a price: simple intein-based implementations may lead to complicated network topologies very quickly as illustrated in Fig. 1(d). Here we exploit a time-scale separation argument to establish a structural model reduction result which provides an easy-to-use recipe to simplify the underlying models. This facilitates the mathematical analysis of the otherwise complicated controller network, and allows us to uncover the underlying controller structure which is not necessarily limited to integral control only.
Integral control is the fundamental building block in most controllers spanning a broad range of industrial applications in the fields of electrical, mechanical and chemical engineering; however, it is rarely used alone. In fact, Integral (I) controllers are typically augmented with Proportional (P) and/or Derivative (D) controllers to obtain PI/PID controllers that offer more flexibility in enhancing the dynamic performance while maintaining the RPA property. Recently, more advanced molecular controllers such as PI/PID controllers found their way to molecular biology7,37–42. Ideally, pure proportional control is achieved via instantaneous negative feedback from the output XL to the input species X1 and it is shown that it is not only capable of enhancing the transient dynamic performance, but also reducing cell-to-cell variability37,40. The first biomolecular (filtered) PI controller was genetically engineered in7 where additional genetic parts are appended to the antithetic integral motif to realize the proportional component. Here, we establish that a filtered PI controller can be built without introducing additional genetic parts by harnessing the sequestration products of the split inteins38.
Besides proposing intein-based implementation strategies for RPA-achieving controllers and laying down the necessary theoretical foundation, we have also selected, built and tested five structurally different controller topologies for experimental verification of RPA. All circuits were tested in HEK293T cells and range from pure I to filtered PI controllers based on the functional conversion, inactivation and intein removal strategies illustrated in Fig. 1(c).
Split inteins offer a high degree of flexibility in realizing biomolecular integral feedback controllers. This flexibility is mainly a consequence of their compatibility with essentially any transcription factor (TF). In fact, the particular structure of the expressed transcription factor including the choices of the Activation Domain (AD), Dimerization Domain (DD), DNA-Binding Domain (DBD) and insertion position of the split intein (IntC) open the possibilities to a broad design space of controllers. Specifically, dimeric transcription factors, such as tetracycline transactivator (tTA), give rise to multiple homo- and hetero-dimerization reactions as well as multiple sequestration reactions and thus make the controller network more complex to mathematically analyze. To this end, we develop a theoretical framework tailored to mathematically analyze and simplify complex intein-based controller networks that generalize the basic antithetic integral motif which has no dimerization reactions and a single sequestration reaction. We refer the reader to Supplementary Table 1 for a list of all the abbreviations used in this paper.
## Achieving robust perfect adaptation using inteins
In this section, we establish a theoretical framework embodied as a set of simple rules that allows us to design biomolecular controllers enabling RPA using split inteins. Consider the general closed-loop network depicted in Fig. 2 where an arbitrary network comprised of L species \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\bf{X}}}}}}:\!\!\!=\left\{{{{{{{\bf{X}}}}}}}_{{{{{{\bf{1}}}}}}},\cdots \,,{{{{{{\bf{X}}}}}}}_{{{{{{\bf{L}}}}}}}\right\}$$\end{document}X:=X1,⋯,XL, referred to as the regulated network, is in a feedback interconnection with the controller network comprised of M species \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\bf{Z}}}}}}:\!\!\!=\left\{{{{{{{\bf{Z}}}}}}}_{{{{{{\bf{1}}}}}}},\cdots \,,{{{{{{\bf{Z}}}}}}}_{{{{{{\bf{M}}}}}}}\right\}$$\end{document}Z:=Z1,⋯,ZM. The overall objective of the feedback controller network is to achieve RPA of the regulated output species XL by automatically actuating (producing and/or degrading) the input species X1. Each controller species Zi, for $i = 1$,2,⋯,M, belongs to one of three classes: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{C}}}}}}}}$$\end{document}C-class, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{N}}}}}}}}$$\end{document}N-class and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{S}}}}}}}}$$\end{document}S-class. These classes separate the controller network into three subnetworks as depicted in Fig. 2. The classification of the controller species and the allowed reactions follow the rules that are listed in Fig. 2. In particular, the setpoint and sensing of the regulated output species XL are encoded in the constitutive and/or catalytic production reactions following Reaction Rule 1 given by1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop{{{{{{{{\bf{Setpoint/Sensing}}}}}}}}}\limits_{{{{{{{{\bf{Rxns}}}}}}}}}:\,\,\,\varnothing \mathop{---\longrightarrow }\limits^{{\mu }_{i}+{\theta }_{i}{x}_{L}}{{{{{{{{\bf{Z}}}}}}}}}_{{{{{{{{\boldsymbol{i}}}}}}}}}\quad ($i = 1$,\cdots,\,M),$$\end{document}Setpoint/SensingRxns:∅---→μi+θixLZi($i = 1$,⋯,M),with at least one μi and one θi strictly positive. The following theorem provides a guarantee for RPA of the regulated output species when controlled with intein-based controllers. Fig. 2A theoretical framework for RPA-achieving intein-based integral controllers. The closed-loop network is formed of a controller network, comprised of M species Z1, ⋯, ZM, connected in a feedback configuration with the regulated network, comprised of L species X1, ⋯, XL. Following the general biomolecular control paradigm37, it is assumed that the controller interacts with the regulated network via X1 and XL only, referred to as the input and regulated output species, respectively. The objective of the controller network is to steer the concentration of the regulated output XL to a prescribed value, referred to as the setpoint, despite the presence of constant disturbances and uncertainties in the regulated network. The controller network is divided into three subnetwork classes according to the list of Species Rules. The allowed reactions within and between the three subnetworks are listed as Reaction Rules. The feedback controller network operates by “sensing” the abundance of the concentration of the regulated output XL(θixL), and “actuating” the input X1 by producing it (h+(z,xL)) or removing it (h−(z,xL)). The total control action u is given by h+(z,xL) − h−(z,xL)x1. Note that, throughout the paper, the diamond-shaped arrowhead denotes either an activation or repression. The setpoint and output-sensing mechanisms are jointly encoded in the vectors μ and θ to allow for multiple setpoint/sensing-encoding reactions.
## Theorem 1
Consider the closed-loop network depicted in Fig. 2 where the controller network respects the set of listed rules. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}_{i}^{+}$$\end{document}qi+ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}_{i}^{-}$$\end{document}qi− respectively denote the number of active IntC and IntN segments present in controller species Zi for $i = 1$, ⋯, M. Define the vector \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q:\!\!\!={\left[\begin{array}{ccc}{q}_{1}^{+}-{q}_{1}^{-}&\cdots &{q}_{M}^{+}-{q}_{M}^{-}\end{array}\right]}^{T}$$\end{document}q:=q1+−q1−⋯qM+−qM−T. Then, if the closed-loop network is stable, the controller network ensures RPA of XL with2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop{\lim }\limits_{t\to \infty }{x}_{L}(t)=-\frac{{q}^{T}\mu }{{q}^{T}\theta } \, > \,0,$$\end{document}limt→∞xL(t)=−qTμqTθ>0,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu:\!\!\!={\left[\begin{array}{ccc}{\mu }_{1}&\cdots &{\mu }_{M}\end{array}\right]}^{T}$$\end{document}μ:=μ1⋯μMT and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta:\!\!\!={\left[\begin{array}{ccc}{\theta }_{1}&\cdots &{\theta }_{M}\end{array}\right]}^{T}$$\end{document}θ:=θ1⋯θMT. Furthermore, the integrated variable is given by zI:= qTz which reveals the underlying integral controller given by3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${z}_{I}(t)={z}_{I}[0]+\int\nolimits_{0}^{t}\left[{\mu }^{{{{{{{{\rm{eff}}}}}}}}}-{\theta }^{{{{{{{{\rm{eff}}}}}}}}}{x}_{L}(\tau)\right]d\tau,$$\end{document}zI(t)=zI[0]+∫0tμeff−θeffxL(τ)dτ,with μeff:= qTμ and θeff:= −qTθ.
The proof of Theorem 1 can be found in Supplementary Information Section 2. Before we proceed, we provide two remarks.
## Remark 1.1
Theorem 1 is a special case of a more general theorem (see Supplementary Theorem 1) which can be also applied to any non-intein-based biomolecular controller with similar structure as demonstrated in the example of Box 3. This more general theorem interprets q+ and q− as the number of positive and negative charges (where, here, the number of inteins is an instantiation of the charge analogy) and extends the RPA sufficiency result in6 to the case of multiple sensing and setpoint reactions. In fact, if Z1 is the only controller species that is constitutively produced and Z2 is the only controller species that is catalytically produced by the regulated output species XL, then μ1, θ2 > 0, μi = θj = 0 for (i,j) ≠ [1,2] and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q={\left[\begin{array}{ccccc}1&-1&&\star &\end{array}\right]}^{T}$$\end{document}$q = 1$−1⋆T which yields \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop{\lim }\limits_{t\to \infty }{x}_{L}(t)={\mu }_{1}/{\theta }_{2}$$\end{document}limt→∞xL(t)=μ1/θ2 — the RPA result in6.
## Remark 1.2
Although the result presented in Theorem 1 is for the deterministic setting, it also holds in the stochastic setting. It is shown in Supplementary Information Section 2 that, under the assumption that the closed-loop network is ergodic — a stochastic notion of stability, the steady-state (stationary) expectation of the regulated output is also given by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\mathbb{E}}}_{\pi }[{X}_{L}]=-\frac{{q}^{T}\mu }{{q}^{T}\theta }$$\end{document}Eπ[XL]=−qTμqTθ.
## Remark 1.3
The catalytic sensing terms θixL for $i = 1$,⋯,M shown in Fig. 2 and [1] do not necessarily have to be linear in the deterministic setting. In fact, these terms can be replaced by more general nonlinear functions fi(xL) such as Hill-type functions that are allowed to be monotonically decreasing to account for repressive sensing. These sensing mechanisms will preserve RPA in the deterministic setting, but the setpoint expression will be different from [2].
## Implementations using various transcription factors
So far, we have described, theoretically, how split inteins can be exploited to build a broad class of biomolecular integral controllers capable of achieving RPA. Here, we demonstrate how commonly used transcription factors can be converted into controller species that respect the rules of Fig. 2 and, as a result, enables RPA according to Theorem 1. In particular, we use three common DNA binding domains: zinc finger (ZF)43,44, tetracycline repressor (TetR)45 and Gal4 to construct four structurally different biomolecular controllers (Fig. 3), that serve as instantiations of the class of controllers described in Theorem 1. We also provide experimental proof (Fig. 4) that these intein-based controllers are indeed capable of achieving RPA and thus rejecting perturbations over a wide dynamic range. Fig. 3Intein-based implementation of RPA-achieving integral controllers using ZF, TetR and Gal4 as DBDs. In all four circuits, the protein Z1 is constitutively expressed at a rate μ1 from Plasmid 1 to encode for the desired setpoint. One of the two main tasks of Z1, which contains one IntC within a TF, is to either directly actuate the regulated network by producing the input species X1, or to dimerize first and then actuate. The second task of Z1 is to undergo an intein-splicing reaction with the second split intein IntN, denoted by Z2, whose production is driven by the regulated output XL at a rate θ2xL to encode for the “sensing” reaction. Different positions of the IntC segment and different TF structures yield different control topologies. Controller species containing DDs undergo reversible homo- or hetero-dimerization reactions with association and dissociation rates of ai and di; whereas, controller species containing active IntC and IntN segments undergo irreversible intein-splicing reactions with rate η multiplied by an integer that depends on the number of participating inteins. Note that inactive splicing products are omitted here for simplicity. The control action u is mathematically expressed as a (Hill-type) function of the repressors and activators depicted in the dashed bubbles. Every reaction is labeled from 1 to 6 according to the permitted reaction rules stated in Fig. 2. Furthermore, every monomer, independent of its dimerization status, is labeled in the yellow boxes with one of the following “charges”: +, −, 0, according to Theorems 1 and 2. This is also repeated in the charge vectors q+, q− and q0 that encode, for each controller species, the number of active IntC, IntN, and monomers with no active inteins. Furthermore, in the blue boxes, all controller species are grouped into \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{C}}}}}}}},{{{{{{{\mathcal{N}}}}}}}}$$\end{document}C,N and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{S}}}}}}}}$$\end{document}S classes according to the split inteins they contain (see Species Rules in Fig. 2). Since all the Species and Reaction Rules of Fig. 2 are respected, then by Theorem 1, we conclude that all four controllers ensure RPA with a setpoint of μ1/θ2.Fig. 4Experimental demonstration of RPA.a Illustration of the experimental setup. All four controllers of Fig. 3 were tested in a two-plasmid system. Plasmid 1 encodes an IntC segment incorporated in an activator driven by a constitutive promoter. Plasmid 2 encodes IntN (resp. IntC) in the closed-loop (resp. open-loop) setting, which is fused to the fluorophore mVenus via a P2A-T2A linker and is driven by the activator expressed from Plasmid 1. The fluorescent protein mVenus is used as a proxy reporter for its own mRNA which is the regulated output expected to exhibit RPA in closed loop. In open loop, there is no interaction between the two IntC segments; whereas, in closed loop, the inteins pair can perform the intein-splicing reaction to produce (possibly) functional products. The setpoint is tuned by changing the constitutive production rate μ1 via the transfected copy numbers of Plasmid 1. The reference, or undisturbed output level, is obtained by fixing the copy numbers of the transfected Plasmid 2 across all setpoints; whereas, the disturbed output level is obtained by repeating the same experiment for all setpoints but with a higher copy number of Plasmid 2. b Steady-state errors. For each controller, we show a simplified schematic (top left) and two bar graphs. The bottom bar graphs show the normalized fluorescence of the proxy reporter with (red) and without (green) disturbance and for the closed-loop (left) and open-loop (right) settings. The disturbed and reference triplicate measurements were normalized to the mean fluorescence of the reference data for each setpoint. The x-axis follows a log2-scale and shows the amount of Plasmid 1 transfected within every well. The red horizontal lines give the normalized output averaged over all the setpoints, and the numbers above the lines indicate the averaged error of the disturbed output relative to the reference. The top bar graphs show the non-normalized data over a selected subset of setpoints (pointed out by the dashed lines) that match in absolute fluorescence between the open- and the closed-loop circuits. For all the data, the HEK293T cells were measured using flow cytometry 48 h after transfection, and the normalized data are shown as mean + SD for $$n = 3$$ technical replicates.
There are a few considerations that have to be taken into account to successfully build intein-based integral controllers.
These considerations, given in Box 1, should be experimentally verified for any intein-based controller to function properly. In particular, to minimize the impairment of the protein of interest as per Building Consideration 1, we use the smaller IntC of the fast reacting intein Gp41-146 for all our modified activators.
Next, we provide a detailed description of the four different controller circuits depicted in Fig. 3. We start with the ZF controller which has the simplest topological structure. It is obtained by using ZF as the DBD, and introducing the split intein in the floppy linker between the AD VP64 and the DBD ZF. This TF, denoted by Z1, is constitutively produced at a rate μ1 and is capable of actuating the regulated network of interest by activating the expression of the input X1. The regulated output XL produces the second split intein IntN, denoted by Z2, at a rate θxL that is proportional to the regulated output concentration. The intein-splicing reaction between Z1 and Z2 occurs at a rate η and leads to a cleavage within the TF, which separates the AD from the DBD. The resulting free floating AD is not tracked due to its inability to initiate transcription on its own. The other spliced product is the DBD, denoted by Z3, which competes with Z1 for the promoter binding sites, and thus exerts a repressive actuation.
The second controller design, labeled as intra dimerization domain (intraDD), is based on TetR whose goal is to illustrate that it is possible to build intein-based antithetic integral controllers without functional spliced products. This controller is obtained by introducing the split intein within the DD of TetR without disrupting it. The transcription factor Z1 is generated by fusing VPR to the modified TetR. The dimer Z4 comprised of two molecules of Z1 acts as the actuating controller species. Unlike the previous controller, IntN, denoted by Z2, can now undergo an intein-splicing reaction with either the monomer Z1 or the dimer Z4. The intein-splicing reaction with Z1 leads to the cleavage of the protein sequence next to the IntC, which is acting as a linker holding the two halves of the split DD together. This results in two products: the AD VPR with part of the disabled DD, denoted by Z3, and a monomeric TetR with the rest of the disabled DD (not tracked in Fig. 3 due to its inactivity). Neither of them are able to further interact with the controller or the regulated network. Similarly, the intein-splicing reaction with the dimer Z4 leads to the cleavage of one of the monomers within the DD. This results in the immediate falling apart of the dimer into one Z1 and one Z3.
The third controller design is obtained by inserting an IntC segment between TetR and the AD. The expressed TF, denoted by Z1, has to dimerize to form Z5 to be able to actuate the regulated network. IntN, denoted by Z2, can undergo an intein-splicing reaction with either the monomer Z1 or the dimer Z5. The intein-splicing reaction between Z1 and Z2 leads to the separation of the AD from the remaining DBD and DD to produce Z3 and a free floating AD which is not tracked anymore due to its inactivity. The spliced product Z3 can still heterodimerize with Z1 to yield a TetR dimer with only one AD, denoted by Z6, which is sufficient to bind to the promoter and initiate transcription. Note that Z6 can be also obtained via the intein-splicing reaction between the fully intact dimer Z5 and Z2. Furthermore, since Z6 still has one functional IntC segment, it is able to perform a second intein-splicing reaction with Z2, which removes the last AD by cleavage and hence forms a tetR dimer, denoted by Z4. Note that, this dimer can be also obtained via the homodimerization of Z3. The dimer Z4 can recognize and bind to the promoter, but can not initiate transcription unlike the other two dimers Z5 and Z6. It therefore, competes with Z5 and Z6 for the promoter binding sites and, as a result, acts as a repressor.
The last controller design of Fig. 3 is based on the yeast derived DBD Gal4 and is thus labeled as the Gal4 controller. Here, we introduced an IntC segement between the DBD and the DD. Similar to TetR, Gal4 needs to be a dimer (Z5) in order to bind to the promoter and actuate the regulated network. Once again, IntN, denote by Z2, can undergo an intein-splicing reaction with either Z5 or Z1. The intein-splicing reaction with Z1 leads to the separation of the DBD from the remaining DD and the AD to produce Z3. As already mentioned, Gal4 cannot bind to the promoter as a monomer, and so we do not track this species due to its inactivity. Furthermore, the intein-splicing reaction with Z5 leads to the removal of one DBD from the dimer through cleavage, which renders the entire complex unable of binding to the DNA. This truncated dimer, denoted by Z6, can perform a second intein splicing reaction with Z2 to remove the second DBD and form a new dimer denoted by Z4 which is also incapable of acting directly on the regulated network. However, it is able to disassociate into its monomers, Z3, which are able to reversibly sequester Z1 through a heterodimerization reaction yielding the non-functional dimer Z6.
It is fairly straight forward to verify that all the reaction rules listed in Fig. 2 are respected by all of the proposed four controllers. As a result, by applying Theorem 1, we conclude that all four proposed controllers achieve RPA (as long as the closed-loop network is stable) such that the concentration of the regulated output xL converges to μ1/θ2 at steady state. Next, we provide an experimental verification to back up our developed theory. To do so, all of the four proposed controller circuits were first tested for the three Building Considerations. In fact, to test for Building Consideration 1, we expressed all of the modified activators constitutively and compared their ability to transcribe a fluorophore. We observed a drop in activity for all modified ZF, tetR and GAL4 based TFs ranging from significant to minor (see Supplementary Information, Fig. 24). To this end, strong impairments were partially compensated by using stronger activation domains like VPR. Intein insertions within floppy linkers were relatively straight forward; however, insertions within functional protein domains, as was the case for the intraDD-Circuit (see Fig. 3), required some screening (see Supplementary Information, Fig. 23). Next we tested for Building Considerations 2 and 3 by constitutively expressing the modified activator carrying IntC together with the second split intein (IntN) and observed the levels of a fluorescent reporter. If the Building Considerations are satisfied the fluorescent output will decrease with increased levels of IntN. We were able to reach background levels for every controller type upon a high expression of the second split intein (see Supplementary Information, Fig. 25). This indicates that the intein-splicing reaction is indeed happening as expected.
After making sure that all Building Considerations were fulfilled, we proceeded with characterizing the controllers in the closed-loop setting. We opted for a simple two-plasmid, closed-loop system for testing the controller performance as demonstrated in Fig. 4(a). This allowed us to focus on the controller behavior without having to worry about potential cross-talks47, resource burden48 or saturation49 which might appear in larger circuits. The first plasmid encodes for the modified transcription factor Z1 and the other one encodes for either IntC for the open-loop circuit or IntN for the closed-loop circuit. In both cases, the split intein was encoded with a P2A-T2A linker and the fluorophore mVenus. Note that the P2A-T2A linker leads to the translation of two separate proteins (IntN and mVenus) in a fixed ratio from a common mRNA due to ribosome skipping50. The fluorophore is used as a proxy for its own mRNA, which is the regulated species expected to exhibit RPA. The advantage of this setup is that changing the copy numbers of the two transfected plasmids can be conveniently used to characterize the controllers. More precisely, μ1 and hence the setpoint can be easily tuned by altering the amount of the plasmid encoding for the activator. Furthermore, the translation rate θ2 of the mRNA is independent from the plasmid copy numbers in the cell. Perturbing the copy numbers of plasmid 2 only leads to an increase in the transcription rate of the output mRNA and should be rejected if the integral controller works as expected. Hence, to experimentally test the four controllers for RPA, we perturb the regulated network by increasing the copy number of plasmid 2 as it does not affect the setpoint parameters μ1 and θ2. The experimental results, depicted in Fig. 4, detail the steady-state measurements of the reporter, serving as a proxy for the regulated output (mRNA) for all four controllers. The measurements were taken for all the circuits operating in both open and closed loop, with and without disturbance. All four circuits were able to reject the disturbance over a wide titration of plasmid 1, which defines the output setpoint through tuning μ1. The best performance was observed with the ZF circuit, which succeeded in rejecting the disturbances over the entire range from the detection limit to the onset of burden (see Supplementary Information, Fig. 27).
We have used so far only the split inteins of Gp41-1, and we have successfully shown the implementation of intein-based RPA-achieving integral controllers using different TFs. Many split intein pairs with different properties have been described in literature with some of them being orthogonal to each other51. To demonstrate that intein-based integral controllers are not limited to Gp41-1, and that it is possible to have multiple orthogonal intein-based integral controllers within the same cell, we have modified our ZF controller accordingly. In particular, we exchanged the Gp41-1, for NrdJ-1 IntC, one of the many orthogonal split inteins characterized by Pinto et al.51 and closed the loop with the corresponding IntN of NrdJ-1. However, instead of using IntC for the open-loop circuit, we used the IntN that corresponds to Gp41-1. Finally, we performed the experiment with the same plasmid ratios, which was deemed suitable for the previous Gp41-1 ZF experiment. The disturbance rejection was only visible for the compatible intein pair, and the dynamic range was similar to the experiment performed with the Gp41-1 containing ZF (see Supplementary Information, Fig. 26).
## Model reduction
The broad class of intein-based, RPA-achieving controllers introduced in Theorem 1 gives rise to a high degree of design flexibility and thus allows topologies that may possibly involve a large number of controller species Zi. Furthermore, these species are allowed to react among each other via multiple binding, conversion and intein-splicing reactions according to the Reaction Rules listed in Fig. 2. This possible large number of control species and reactions may lead to complex mathematical models of high dimensions whose dynamics are not easy to understand. In this section, we consider a subset of the general RPA-achieving controllers of Theorem 1 to provide a model reduction result that makes the otherwise complex dynamics more transparent and easy to analyze. Our model reduction result is structural in the sense that its validity is independent of the particular values of the rate parameters.
Consider the Species and Reaction Rules of Fig. 2 and replace Reaction Rule 8 with five additional rules given in Box 2.
Note that Rule 9 makes Rule 2 stricter in the sense that the intein-splicing reactions are not optional anymore so that any two active intein pairs have a strictly positive propensity to undergo an intein-splicing reaction. Rule 12 takes into account the more realistic situation where δ > 0 which implies that RPA is not exact anymore; however, robust adaptation remains practically satisfactory as long as the dilution rate is small compared to the other rates in the network (see6,52). Finally, Rule 13 relates the intein-splicing rate to the number of participating active inteins. The following theorem provides a recipe for model reduction of (possibly complex) intein-based controllers. The model reduction result is valid in both the ideal (δ = 0) and non-ideal (δ > 0) settings and for any rate-parameter regimes.
## Theorem 2
Consider the closed-loop network depicted in Fig. 2 where the controller network respects Species Rules 1-3 and Reaction Rules 1-7,9-13. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}_{i}^{+}$$\end{document}qi+ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}_{i}^{-}$$\end{document}qi− respectively denote the number of active IntC and IntN segments present in controller species Zi for $i = 1$, ⋯, M. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}_{i}^{0}$$\end{document}qi0 denote the number of monomers in species Zi with no active inteins, and construct the three vectors \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}^{k}:\!\!\!={\left[\begin{array}{ccc}{q}_{1}^{k}&\cdots &{q}_{M}^{k}\end{array}\right]}^{T}$$\end{document}qk:=q1k⋯qMkT, for k ∈ { +, −, 0}. Furthermore, let (SB, SC) and (λB(z), λC(z)) respectively denote the stoichiometry matrices and total propensity functions associated with the reversible binding and conversion reactions that are assumed to be fast enough. If the following conditions are satisfied:SB is full-column rank. The columns of SC are linearly independent from those of SB.p + rank(SC) = M − 3,where p is the number of reversible binding reactions, then all controller networks respecting the structure described in Fig. 2 reduce to the simple motif, depicted in Fig. 5, which is governed by only three effective species Z+, Z− and Z0 whose concentrations are linear combinations of the controller species Zi for $i = 1$, ⋯, M.Fig. 5A model reduction recipe for Intein-based controllers. Under the conditions of Theorem 2, all controllers comprised of M species (where M can be large) that respect the flexible structure depicted in Fig. 2, reduce to the simple motif shown here. The reduced model is shown schematically as a motif comprised of only three effective species (Z+, Z−, Z0), and mathematically as a set of Differential Algebraic Equations (DAEs) comprised of only three differential equations in (z+, z−, z0) and M − 3 algebraic equations in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{z}$$\end{document}z~. Note that (SB, λB) and (SC, λC) denote the stoichiometry matrices and total propensity functions (forward minus backward) of the reversible binding and conversion reactions, respectively. Furthermore \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb{1}}(.),\circ,{I}_{M}$$\end{document}1(.),∘,IM and (.)T denote the indicator function, the Hadamard (element-wise) product, the identity matrix of size M and the transpose of a matrix, respectively. In certain scenarios (see Fig. 6), the algebraic equations can be solved explicitly and thus further simplifying the model to only three Ordinary Differential Equations (ODEs). Observe that the schematic of the simple motif is fully determined once the three vectors q+, q−, q0 and the function ψ(ztot) are calculated. The vectors q+, q− and q0 are easily calculated by counting active split inteins (see Theorem 2); whereas, ψ(ztot) can be calculated by solving the algebraic equations for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{z}\ge 0$$\end{document}z~≥0 as a function of ztot.
The proof of Theorem 2 can be found in Supplementary Information Section 2. Before we proceed, we provide five remarks.
## Remark 2.1
Once again, Theorem 2 is a special case of a more general theorem (see Supplementary Theorem 2) which can be also applied to any non-intein-based biomolecular controller with similar structure as demonstrated in Box 3. The proof essentially invokes the deficiency-zero theorem53 and singular perturbation theory54.
## Remark 2.2
The dynamics of the reduced model are depicted in the box of Fig. 5, in general, as a set of Differential Algebraic Equations (DAEs) comprised of only three differential equations (describing the basic effective motif) and a set of M − 3 algebraic equations that should be solved for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{z}\ge 0$$\end{document}z~≥0. In certain cases, these algebraic equations can be explicitly solved and thus further reducing the dynamics to a set of three ODEs (see Fig. 6). Otherwise, the algebraic equations can be left in their implicit form. Fig. 6Reduced models for the ZF, intraDD, TetR and Gal4 controllers.a Reduced Motif. For simplicity, we assume that the protein degradation rates are negligible compared to the dilution rate δ; however, this assumption can be easily relaxed (see Supplementary Information Section 3). Note that δ is assumed to be non-zero here to capture the more realistic scenario. The model reduction recipe presented in Theorem 2 and Fig. 5 can be straightforwardly applied to all of the four controller topologies in Fig. 3, where the “charge” vectors q+, q− and q0 are shown explicitly. Observe that all four controllers reduce to the same motif comprised of the three effective species Z+, Z− and Z0. The difference between them appears only in the effective control action \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u={{{{{{{\mathcal{U}}}}}}}}\left({z}^{+}\!\!,\, {z}^{0}\right)$$\end{document}u=Uz+,z0. b Effective control actions. The control actions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u={{{{{{{\mathcal{U}}}}}}}}\left({z}^{+}\!\!,\, {z}^{0}\right)$$\end{document}u=Uz+,z0 is given separately for each controller as a function of the effective species concentrations. For the intraDD controller, the control action u is a strictly monotonically increasing function of z+ only, and hence the control structure is a standalone integrator. In contrast, for the ZF and Gal4 controllers, it is shown (see Supplementary Information Sections 3.D and 3.B) that the control action u is strictly monotonically increasing (resp. decreasing) in z+ (resp. z0). This gives rise to a filtered proportional-integral (PI) control structure38. Finally, for the TetR controller, it is shown that the control action u is stricly monotonically increasing in z+; whereas, its monotonicity switches from increasing to decreasing as the levels of (z+, z0) rise (see Supplementary Information Section 3.A). This gives rise to a filtered PI control structure where the P-component switches sign. Note that the algebraic equations presented in Fig. 5 are solved explicitly for the ZF and intraDD controllers; however, they are kept in their implicit form for the TetR and Gal4 controllers.
## Remark 2.3
Unlike the effective species Z+ and Z−, Z0 has an extra production term, in general, that is equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{0}{\left[{\mathbb{1}}({q}^{+}+{q}^{-})\circ {q}^{0}\right]}^{T}\psi ({z}^{{{{{{{{\rm{tot}}}}}}}}})$$\end{document}δ01(q++q−)∘q0Tψ(ztot), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb{1}}(.)$$\end{document}1(.) is the indicator function, ∘ is the Hadamard (elementwise) product and ψ(ztot) is given implicitly in Fig. 5. This production term is zero in two cases: [1] if there are no degradation reactions (δ0 = 0), or [2] if no controller species simultaneously hold both an active intein and a monomer with no active inteins (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb{1}}({q}^{+}+{q}^{-})\circ {q}^{0}=0$$\end{document}1(q++q−)∘q0=0). Intuitively, this extra production term can be explained as follows. Controller species holding both an active intein and a monomer with no active inteins belong to either the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{C}}}}}}}}$$\end{document}C- or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{N}}}}}}}}$$\end{document}N-class (Species Rules), and are thus not allowed to degrade (Reaction Rules). Nevertheless, these species are still represented within Z0 since they hold monomers with no active inteins. As a result, the extra production term compensates for those species that do not degrade yet are represented by Z0 which degrades at a rate δ0.
## Remark 2.4
Observe that no matter what the original controller network in Fig. 1 is and as long as it satisfies the conditions of Theorem 2, the underlying effective motif is the same and is dictated by the three effective species Z+, Z− and Z0 as depicted in Fig. 5. However, different controller networks give rise to different actuation functions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{{\mathcal{U}}}}}}}}}^{\pm }$$\end{document}U± and production functions ψ. The forms of these functions lead to different control designs that may offer different tuning knobs capable of enhancing the overall performance.
## Remark 2.5
Unlike Theorem 1, it is unclear whether Theorem 2 can be extended to the stochastic setting. While a mathematically rigorous approach is left for future work, we have conducted a simulation-based case study which revealed that the reduced model was capable of accurately capturing the stochastic dynamics of the full model. See Supplementary Information Section 6 for more details.
Next, we apply Theorem 2 to the four controller circuits of Fig. 3 to obtain a reduced mathematical model for each. Here, we consider the more practical scenario where all controller species dilute at a rate δ > 0. Furthermore, we assume, for simplicity, that the degradation of the various proteins are negligible compared to the dilution rate; however, this assumption can be easily relaxed (see Supplementary Information Section 3). The model reduction results are compactly depicted in Fig. 6 for all four controllers. The underlying reduced motif, as illustrated in Fig. 6, is the same for all four controller circuits and is comprised of only three effective species Z+, Z− and Z0 whose concentrations are linear combinations of the biological species Zi. The differences between the reduced models of each controller circuit is encrypted in the effective control action \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u={{{{{{{\mathcal{U}}}}}}}}({z}^{+}\!\!,\,{z}^{0})$$\end{document}u=U(z+,z0) which is a function of the concentrations of Z+ and Z0. Observe that the control action is given in an explicit form for the ZF and intraDD controllers; whereas, for the TetR and Gal4 controllers, it is given implicitly as a set of three algebraic equations. Once these algebraic equations are solved for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({\tilde{z}}_{1},\,{\tilde{z}}_{2},\,{\tilde{z}}_{3}\right)\ge 0$$\end{document}z~1,z~2,z~3≥0, the control actions can be directly computed as a function of z+ and z0. The topology of the reduced models is clearly simpler to analyze compared to the full models described in Fig. 3, and thus the underlying control architecture can be uncovered more easily. In fact, the intraDD controller realizes a standalone antithetic integral controller since the control action \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u={{{{{{{\mathcal{U}}}}}}}}({z}^{+})$$\end{document}u=U(z+) depends (monotonically) on Z+ only. On the other hand, it is shown in Supplementary Information Sections 3.D and 3.B that the control action \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u={{{{{{{\mathcal{U}}}}}}}}({z}^{+}\!\!,\, {z}^{0})$$\end{document}u=U(z+,z0) of the ZF- and Gal4-Circuits depends on both Z+ and Z0, such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{U}}}}}}}}$$\end{document}U is monotonically increasing (resp. decreasing) in z+ (resp. z0). This particular topology can be shown to realize a filtered Proportional-Integral (PI) controller, where the proportional component can be used as an additional knob to enhance the dynamic performance (see38 for a thorough analysis). Finally, it is shown in Supplementary Information Section 3.A that the control action \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u={{{{{{{\mathcal{U}}}}}}}}({z}^{+}\!\!,\, {z}^{0})$$\end{document}u=U(z+,z0) of the TetR controller also depends on both Z+ and Z0. Nevertheless, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{U}}}}}}}}$$\end{document}U is a monotonically increasing function of z+, but its monotonicity switches from increasing (at low levels of z0 and z+) to decreasing (at higher levels of z0 and z+). We refer the reader to Supplementary Information Section 3.A for more details on the exact monotonicity analysis of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{U}}}}}}}}$$\end{document}U. Interestingly, this architecture realizes a filtered PI controller whose proportional component switches from positive to negative gain. This gives rise to a nice feature that initially speeds up the response when the concentrations of the controller species are low, and then switches to negative feedback as the concentrations rise and thus favoring closed-loop stability. The various reduced models are validated via simulations that demonstrate the highly accurate matching between the dynamics of the full and reduced models in Supplementary Information Section 4.
## Integral controllers with competing sequestrations
In this section, we demonstrate that Theorem 1 can be applied to controller circuits that are more general compared to those of Theorem 2. That is, there are certain intein-based controllers that can be easily tested for RPA using Theorem 1; however, their model reduction cannot be carried out by applying Theorem 2. We do so by considering the circuit depicted in Fig. 7(a), where two independent controller species (active IntN denoted by Z2 versus inactive IntN denoted by Z4 in Fig. 7(a)) stoichiometrically compete to sequester another controller species (Z1 in Fig. 7(a)). In this circuit, we constructed two genes encoding for an AD fused to an active IntC (expressing Z1) and a DBD-DD fused to an inactive IntN (expressing Z4). Although the inactive IntN lacks essential amino acids to undergo the intein-splicing reaction55, Z4 can still reversibly bind to Z1 to form a heterodimeric transcription factor. In this controller design, the intein-splicing reaction can occur only between the expressed IntN, denoted by Z2, and Z1, because Z1 is the only controller species that contains an active IntC segment in its unbound state. In fact, although the other controller species containing active IntC segments (Z6, Z7 and Z8) belong to the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{C}}}}}}}}$$\end{document}C-class, they cannot directly undergo intein-splicing reactions since they are bound to the inactive IntN. This results in a violation of Reaction Rule 9 rendering the model reduction recipe of Theorem 2 inapplicable. Nonetheless, it is straightforward to check that the conditions of Theorem 1 still apply and, as a result, RPA is still guaranteed as long as the closed-loop system is stable. Furthermore, applying [2], by noting that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q={\left[\begin{array}{cccccccc}1&-1&0&0&0&1&2&1\end{array}\right]}^{T}$$\end{document}$q = 1$−1000121T, yields the setpoint expression given by μ1/θ2 (see Fig. 7(a)). Observe that the rate of expression μ4 of Z4 does not affect the setpoint — a result that is not immediate without resorting to [2]. Similar to Fig. 4, the experimental results depicted in Fig. 7(b) demonstrate that the controller indeed ensures RPA yielding an average steady-state error of $3.9\%$ over a wide dynamic range of setpoints compared to an error of $40.9\%$ when operating in open loop. Fig. 7Inactive-intein controller: theoretical and experimental analysis.a A schematic of the inactive-intein controller. This controller consists of two genes, realized on separate plasmids. *The* gene in Plasmid 1 encodes for a protein (Z1) comprised of IntC-AD; whereas, the gene in Plasmid 1' encodes for a protein (Z4) comprised of TetR-inactive IntN. *Both* genes are driven by a strong constitutive promoter (EF-1α), and their expression rates are denoted by μ1 and μ4, respectively. Z1 and Z4 can reversibly bind to form a heterodimeric transcription factor, which positively actuates the regulated network via the production of the input species X1. The production of the second split intein IntN, denoted by Z2, is driven by the regulated output XL at a rate θ2xL to encode for the “sensing" reaction. Controller species containing DDs undergo reversible homo- or hetero-dimerization reactions with association and dissociation rates of ai and di. Here, only Z1 and Z2 can directly undergo the intein-splicing reaction (at a rate η), because Z1 is the only species that contains an active IntC segment not bound to the inactive IntN segment. The control action u is mathematically expressed as a (Hill-type) function of the repressors and activators depicted in the dashed bubbles. Every reaction is labeled from 1 to 6 according to the permitted reaction rules stated in Fig. 2. The entire charge matrix can be viewed in the blue shaded box where, additionally, all controller species have been grouped into the three classes, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{C}}}}}}}}$$\end{document}C-class, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{N}}}}}}}}$$\end{document}N-class and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathcal{S}}}}}}}}$$\end{document}S-class, according to the species rules of Fig. 2. Since all the Species and Reaction Rules of Fig. 2 are respected, then by Theorem 1, we conclude that this controller ensures RPA with a setpoint of μ1/θ2 and is thus interestingly independent of μ4. b Experimental demonstration of RPA. The performance was tested using the same setup as in Fig. 4(a). The only difference here is that the IntN segment (Gp41-1) is replaced by an orthogonal IntN (NrdJ-1) for the open-loop setting, and thus no intein-splicing reaction can occur. The results are demonstrated in a fashion similar to that of Fig. 4. c Reduced model. Unlike the three-dimensional reduced models in Fig. 6 that are obtained by directly applying Theorem 2, the reduced model here is four dimensional because it was necessary to introduce the dynamics of an additional state variable z⋆. The functions ψ and ϕ are given implicitly in Supplementary Information Section 3.E. Note that the cartoon describing the reduced model is non-physical because the mathematical equations do not satisfy the structure of a simple motif like the models that satisfy Theorem 2. d Simulation Results. A closed-loop system is simulated for four increasing setpoints, where a model of a gene expression network is controlled by the inactive-intein controller. The simulations results demonstrate that the reduced model indeed accurately captures the dynamics of the original full model. The numerical values are provided in Supplementary Information Section 3.E.
Although the model reduction recipe provided in Theorem 2 cannot be applied here, one can still invoke singular perturbation theory to this particular controller circuit to obtain the reduced mathematical model depicted in Fig. 7(c). The model reduction here assumes, once again, that the reversible binding reactions are fast. Observe that, unlike the previous controllers, the reduced model is four dimensional. Intuitively, this is a result of an additional conservation law imposed by the inactive inteins which introduce an additional (fourth) vector q⋆ required to carry out the state transformation. Hence, the reduced mathematical model is described by the set of four ODEs for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({z}^{+},{z}^{-},{z}^{0},{z}^{\star }\right)$$\end{document}z+,z−,z0,z⋆ shown in Fig. 7(c) where the functions ϕ and ψ are implicitly given in Supplementary Information Section 3.E. A “fictitious network” describing the ODEs is also depicted in Fig. 7(c) to emphasize that the reduced model is mainly mathematical and cannot be easily translated to a simple motif. This highlights that controller circuits not adhering to the conditions of Theorem 2 fail to reduce to the simple motif given in Fig. 2. The reduced model is validated by the simulation results shown in Fig. 7(d) for four different setpoints and by applying a disturbance.
## Discussion
In this paper, we introduced a theoretical and experimental framework to design, build and analyze a broad class of biomolecular integral feedback controllers that achieve RPA. The framework is based on custom-built split inteins that are shown to be capable of realizing the sequestration reaction — the heart of the basic antithetic integral feedback motif — via protein splicing. The sequestration reaction in previously proposed20,37,39,40,52 and built6,7,9,13 integral controllers, whether in vivo, or in vitro, relies on the complete stoichiometric annihilation of two controller species (see Z1 and Z2 in Fig. 1(a)). Here, we relax this requirement by establishing that the sequestration reaction does not have to fully annihilate the participating controller species, and, in fact, it suffices to stoichiometrically annihilate sub-components within these two controller species. Indeed, this is precisely what intein-splicing reactions do: active split inteins inserted in two target proteins are inactivated by undergoing the splicing reaction. While the function of the active split inteins is indeed annihilated, the spliced target proteins are still allowed to have specific functions. In fact, we showed that one can harness the function of the spliced proteins to augment the standalone integral controller with a filtered proportional component to yield a PI controller. We previously computationally demonstrated (see38) that the resulting filtered PI controller adds an extra degree of freedom which enables the enhancement of the transient performance and the reduction of cell-to-cell variability while maintaining the RPA property. However, it is left for future work to back up this theory with experimental demonstrations. It is worth to mention that the realization of a molecular PI controller in mammalian cells is not new. Ideally, a proportional component can be theoretically achieved by appending the integral controller with an instantaneous negative feedback from the regulated output species XL onto the input species X1 (see e.g.37,40). This requires the output species XL to have multiple functions including the production of Z2 for sensing and the inhibition of the input species X1 to realize the proportional component. In practice this might not be possible as the output species is determined by the biological application. In7, this was circumvented by introducing additional genetic parts to express a proxy to the regulated output upon which the proportional control action is based on. Here, in contrast, the design flexibility and modularity offered by inteins allowed us to implement PI controllers by simply choosing an actuator and a suitable insertion site of the split-intein (see Fig. 3 and 6) without adding additional genetic parts and without requiring the regulated output XL to have multiple functions.
The simple antithetic integral feedback control topology was first introduced in20, and more recently a generalized antithetic topology was introduced in6 which characterizes all RPA-achieving controllers involving exactly one sensing and one setpoint-encoding reaction. This characterization lead to simple algebraic conditions that enable RPA and are expressed in terms of quantities that are referred to as “charges”. *The* general charge analogy borrowed from electronics was made due to the lack of biological parts capable of respecting the algebraic conditions. This is exactly where inteins came in, because they naturally satisfy the RPA algebraic conditions and act as “charges” neutralizing each other via the intein-splicing reactions. Indeed, split inteins are typically charged at the locations where they interact56. This makes the charge analogy biologically suitable. In fact, Theorem 1, which is a direct application of Supplementary Theorem 1 (tailored towards inteins), is a generalization of the RPA sufficiency result of6 such that multiple sensing and setpoint-encoding reactions are now allowed. Theorem 1 facilitates the screening of controller circuit designs for RPA. Furthermore, we went one step further here, beyond establishing RPA, to provide an easy-to-apply recipe for model reduction. The recipe is given in Theorem 2 which is, once again, a direct application of Supplementary Theorem 2 tailored towards inteins (see Box Box 3 for an application example of these theorems in a purely mathematical and more general context, that is, without an intein-based interpretation). The model reduction result presented here exploits the time-scale separation imposed by fast reversible binding and conversion reactions and is established by invoking singular perturbation theory54 and the deficiency zero theorem53 to prove structural (rate-independent) stability of the slow manifold.
The five controller circuit implementations presented in this paper (see Fig. 3 and 7) are based on the widely used DNA binding domains TetR, ZF and Gal4. For the experimental verification of RPA, we used a simple regulated network (see Fig. 4(a)) that resulted in a two (resp. three) plasmid closed-loop system depicted in Fig. 4 (resp. 7). The regulated network was intentionally chosen to be simple here, in order to minimize possible cross talks which might emerge from larger networks (e.g. burden)48. This allowed us to focus our study on the controllers themselves instead of possible undesirable behaviors incurred by larger networks — an important topic that is not within the scope of the current study and is left for future work. Note that with this experimental setup, we were not able to directly detect the regulated output which is an mRNA (see Fig. 4(a)). To circumvent this, we used a fluorescent reporter which, unlike the regulated (mRNA) species, is not robust to translational burden. This implies that although RPA is not observed at high setpoints by the reporter, it may actually be achieved by the mRNA.
The controller circuits that are designed, built and analyzed in this paper are all based on controller species generated using TFs. However, split inteins can also be introduced in other protein classes such as proteases (Supplementary Information, Fig. 18) and receptors (Supplementary Information, Fig. 19). Split inteins can be even introduced in endogenous proteins to convert them into controller species. This has an attractive advantage of exploiting parts of the regulated network to realize the controller and, as a result, requiring less to no additional genes. From a protein engineering point of view, such designs may be more challenging than designs based on the well-characterized TFs used in this study. Besides tinkering with insertion sites, linker lengths and split-intein pairs, it is also possible to use more systematic approaches like transposon screens with inteins as performed by Ho et al.57 or computationally-guided optimizations by Dolber et al.58.
The remarkable flexibility offered by inteins for building integral controllers opens the doors to many possible future research directions. For instance, it is easy to think of regulated networks with negative gain, in other words, producing more input species X1 leads to a lower concentration of the regulated output species XL. For example, producing more insulin leads to a lower concentration of glucose in the blood. As a result, to realize an overall negative feedback, the actuation direction of the controller species Z1 would have to be flipped, that is instead of having Z1 upregulating X1 (like in Fig. 1(a) and, in fact, all previously built antithetic integral controllers), Z1 would have to downregulate X1 (see Supplementary Information, Fig. 18). Intein-based realizations of such “negative actuation” mechanisms can be easily carried out using repressors or proteases. Furthermore, n inteins (with $$n = 1$$, 2, 3,⋯) can be embedded sequentially in a single controller species leading to the scaling of the setpoint by an integer n (see Supplementary Information, Fig. 11 and 12). Note that other functional domains can be placed between inteins to alter the functionality of the various spliced products (see Supplementary Information, Fig. 13 and 14). The flexibility offered by inteins also allows us to freely design the (multi)functionality of the spliced products as activators and/or repressors (e.g. Supplementary Information, Fig. 15, 16 and 17).
Another possible future direction is intein-based implementations of more advanced controllers. For example, one can easily add functional domains to the controller species Z2, which was comprised of a standalone IntN segment in all the controller circuits proposed here. These added domains enable the implementation of the rein controller introduced in59 which is capable of enhancing the overall performance. Another example is the implementations of more advanced biomolecular Proportional-Integral-Derivative (PID) controllers37 that are capable of shaping the transient response and reducing cell-to-cell variability. In particular, the wide library of orthogonal split inteins51 allows one to implement the fourth order PID controller37 that is comprised of two antithetic motifs: antithetic integrator and antithetic differentiator.
In conclusion, rather than providing another way of implementing antithetic integral controllers, we propose here a systematic (theoretical and experimental) approach of designing, building and analyzing a broad class of biomolecular integral controllers that are capable of achieving RPA. The key of our approach is the exploitation of the splicing reactions that occur between split inteins. Due to their simplicity, modularity, irreversibility, lack of side effects and applicability across species, we believe that inteins will revolutionize biomolecular controllers and partake in filling the gap between theory and experiments.
## Plasmid construction
All plasmids were generated with a mammalian adaptation of the modular cloning (MoClo) yeast toolkit standard60. All individual parts were generated by PCR amplification (Phusion Flash High-Fidelity PCR Master Mix; Thermo Scientific) or synthesized with Twist Bioscience. PCR primers were obtained from Sigma-Aldrich and Integrated DNA Technologies. The parts were then assembled with golden gate assembly. All enzymes for plasmid construction were obtained from New England Biolabs (NEB). Constructs were chemically transformed into E. coli Top10 strains (Invitrogen). The plasmid list and protein sequences can be found in Supplementary Information Section 9. DNA and oligo sequences can be found in the Data Source file.
## Cell culture
All experiments were performed with HEK293T cells (ATCC, strain number CRL-3216, LGC standards). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco) supplemented with 10 % FBS (Sigma-Aldrich), 1x GlutaMAX (Gibco), 1 mm Sodium Pyruvate (Gibco), penicillin (100U/μL), and streptomycin (100 μg/mL) (Gibco) at 37∘ with 5 % CO2. The cell culture was passaged into a fresh T25 flask (Axon Lab) every 2 to 3 days. Upon detachment some part of the cell suspension was used for the transfection.
## Transfection
All plasmids were isolated using ZR Plasmid Miniprep-Classic (Zymo Research). The plasmids were introduced to the HEK293T cells via suspension transfection. A transfection solution in Opti-MEM I (Gibco) was prepared using Polyethylenimine (PEI) “MAX” (MW 40000; Polysciences, Inc.) at a 1:3 (μg DNA to μg PEI) ratio while the culture was detached with Trypsin-EDTA (Gibco). The cell density was assessed with the automated cell counter Countess II FL (Invitrogen). 100 μL of culture with 26’000 cells was transferred in each well of the plate Nunc Edge 96-well plate (Thermo Scientific). The transfection mixture was added to the cells once it has incubated for approximately 30 min. All transfection tables can be found in Supplementary Information Section 9.
## Flow cytometry
The cells were detached approximately 48 h after transfection on the Eppendorf ThermoMixer C at 25 ∘C at 700 rpm with 53 μL Accutase solution (Sigma-Aldrich) per well for 20 min. The fluorescence data was collected on the Beckman Coulter CytoFLEX S flow cytometer with the 488 nm excitation with a $\frac{525}{40}$+OD1 bandpass filter and the 638 nm excitation with a $\frac{660}{10}$ bandpass filter. All data was processed with the CytExpert 2.3 software. A representative example of the gating strategy can be found in Supplementary Information, Fig. 28. The data was visualized with GraphPad 8.2.0.
## Numerical simulations and visualizations
All simulations are carried out in MATLAB R2021a (academic use). Stochastic simulations shown in the supplementary information file are carried out on the Euler cluster (https://scicomp.ethz.ch/wiki/Euler). Manuscript figures were structured and formatted on Illustrator (2022 26.5), MATLAB and TexStudio (v3.1.1, open source).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36863-9.
## Source data
Source Data
## Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
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|
---
title: Downregulation of PSAT1 inhibits cell proliferation and migration in uterine
corpus endometrial carcinoma
authors:
- Min Wang
- Song Yue
- Zhu Yang
journal: Scientific Reports
year: 2023
pmcid: PMC10008565
doi: 10.1038/s41598-023-31325-0
license: CC BY 4.0
---
# Downregulation of PSAT1 inhibits cell proliferation and migration in uterine corpus endometrial carcinoma
## Abstract
Phosphoserine aminotransferase 1 (PSAT1) has been associated with the occurrence and development of various carcinomas; however, its function in uterine corpus endometrial carcinoma (UCEC) is unknown. We aimed to explore the relationship between PSAT1 and UCEC using The Cancer Genome Atlas database and functional experiments. PSAT1 expression levels in UCEC were employed using the paired sample t-test, Wilcoxon rank-sum test, the Clinical Proteomic Tumor Analysis Consortium database, and the Human Protein Atlas database, while survival curves were constructed using the Kaplan–Meier plotter. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore the possible functions and related pathways of PSAT1. Furthermore, single-sample gene set enrichment analysis was performed to detect the relationship between PSAT1 and tumor immune infiltration. StarBase and quantitative PCR were used to predict and verify the interactions between miRNAs and PSAT1. The Cell Counting Kit-8, EdU assay, clone formation assay, western blotting and flow cytometry were used to evaluate cell proliferation. Finally, Transwell and Wound healing assays were used to assess cell invasion and migration. Our study found that PSAT1 was significantly overexpressed in UCEC, and this high expression was associated with a worse prognosis. A high level of PSAT1 expression was associated with a late clinical stage and, histological type. In addition, the results of GO and KEGG enrichment analysis showed that PSAT1 was mainly involved in the regulation of cell growth, immune system and cell cycle in UCEC. In addition, PSAT1 expression was positively correlated with Th2 cells and negatively correlated with Th17 cells. Furthermore, we also found that miR-195-5P negatively regulated the expression of PSAT1 in UCEC. Finally, the knockdown of PSAT1 resulted in the inhibition of cell proliferation, migration, and invasion in vitro. Overall, PSAT1 was identified as a potential target for the diagnosis and immunotherapy of UCEC.
## Introduction
Uterine corpus endometrial carcinoma (UCEC) is the most common cancer of the female reproductive organs1 in the developed world2–4, and its incidence and mortality rate are increasing annually, while the age of onset is decreasing. Many UCEC patients can recover with active treatment5; however, a small number of patients miss the optimum treatment window due to late diagnosis6, which eventually leads to poor prognosis7. An effective diagnostic marker could help with early disease detection and, thus timely treatment measures, thereby improving therapeutic efficacy and survival. Therefore, it is crucial to identify differential molecular markers of UCEC to increase the survival rate of UCEC8–13 patients.
Phosphoserine aminotransferase 1 (PSAT1) is a pivotal enzyme that governs the production of two metabolites14, serine and α-ketoglutarate, which are involved in carbon metabolism and the tricarboxylic acid cycle, respectively15. In many tumors,14,16–19 PSAT1 plays a key role in its progression. PSAT1 enhances cell proliferation in estrogen receptor-negative breast cancer20,21, while its overexpression promotes the metastasis of lung adenocarcinoma22. Moreover, PSAT1 is a promising prognostic marker for lower-grade gliomas23. PSAT1 is also associated with the growth and prognosis of epithelial ovarian cancer18; however, it’s still unclear how PSAT1 contributes to UCEC24.
According to our study, we speculated that PSAT1 could have an important function in UCEC and aimed to explore its efficacy as a marker in the diagnosis and prognosis of UCEC.
## Expression profiles of PSAT1 in pan cancer analysis and UCEC
The study process is shown in Fig. 1. The Cancer Genome Atlas (TCGA) was used to determine the mRNA expression levels of PSAT1 in different cancers. Among the 33 cancer types we evaluated, PSAT1 was significantly highly expressed in 18 cancers, especially UCEC (Fig. 2A). A significant increase in PSAT1 expression was observed in 552 UCEC tissues compared to 35 normal endometrial tissues (Fig. 2B). And the expression levels of PSAT1 were also higher in the 23 tumor tissues compared to the paired normal tissues (Fig. 2C). PSAT1 protein expression was much higher in UCEC than in normal tissues, as demonstrated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database (Fig. 2D). Furthermore, results from the Human Protein Atlas (HPA) database further confirmed that PSAT1 was significantly overexpressed in UCEC at the protein expression level. ( Fig. 2E,F).Figure 1Study flow-chart. Figure 2Expression Profiles of PSAT1 in Pan cancer and UCEC. ( A) PSAT1 expression levels in Pan cancer and matched normal tissues. ( B) PSAT1 expression levels in UCEC and normal endometrial tissues. ( C) PSAT1 expression levels in UCEC and paired normal endometrial tissues. ( D) The protein expression levels of PSAT1 in UCEC and normal endometrial tissues based on CPTAC database. Immunohistochemical results of PSAT1 in normal endometrial tissues (E) and UCEC tissues (F) from the HPA database. * $p \leq 0.05$, ***$p \leq 0.001$, ns, $P \leq 0.05.$
## The prognostic and diagnostic value of PSAT1 expression in UCEC
The Kruskal–Wallis rank-sum test revealed a strong correlation between the expression level of PSAT1 and the clinical stage, age, histological type and weight (Fig. 3A–D). Furthermore, high-PSAT1 expression was significantly correlated with overall survival (Fig. 3E), progression free interval (Fig. 3F), and disease specific survival (Fig. 3G) in UCEC. In addition, the diagnostic ROC curve showed that the area under the curve of PSAT1 was 0.839 (Fig. 3H), indicating that PSAT1 may be a promising tumor diagnostic marker for UCEC.Figure 3The prognostic and diagnostic value of PSAT1 expression in UCEC. Association of PSAT1 expression level with clinical stage (A), age (B), histological type (C) and weight (D) in UCEC. Survival curves of OS (E), PFI (F) and DSS (G). ( H) A diagnostic ROC curve analysis evaluating the performance of PSAT1 for UCEC diagnosis. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ns, $P \leq 0.05.$
## Identification of differentially expressed genes (DEGs) between high- and low- PSAT1 expression groups, functional enrichment analysis, and predicted signaling pathways
A total of 148 DEGs were identified between the high- and low- PSAT1 expression groups, including 32 upregulated and 116 downregulated genes in the high-expression group (Fig. 4A). To better understand the functional implications of PSAT1 in UCEC among the 148 DEGs, Gene Ontology (GO) enrichment analysis25,26 was performed. The most relevant GO enrichment functions were metabolic, immune system, reproductive, and growth processes. ( Fig. 4B–D). In addition, we identified critical pathways associated with PSAT1 in UCEC using Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis27–29. The results revealed that the cell cycle and cellular senescence were significantly enriched (Fig. 4E). Some studies reported the important role of PSAT1 in the regulation of cell cycle30, and the results of KEGG enrichment analysis showed the highest correlation between the cell cycle pathway and the expression of PSAT1 in UCEC. Therefore, we speculated that PSAT1 might be involved in the regulation of cell cycle in UCEC.Figure 4DEGs and enrichment analysis. ( A) Volcano Plot of differentially expressed genes (DEGs). ( B–D) GO enrichment analysis performed by the Metascape database 2.0. ( E) KEGG enrichment analysis to identify the key pathways related to PSAT1 in UCEC.
## Correlation between PSAT1 expression and immune infiltration
Considering that PSAT1 may be involved in the immune system response according to GO enrichment analysis, a single-sample gene set enrichment analysis (ssGSEA) was used to examine the relationship between PSAT1 mRNA expression levels and immune cell infiltration. As shown in Fig. 5A,B, PSAT1 mRNA expression correlates with immune cell infiltration. The results revealed that the PSAT1 mRNA expression levels were positively related to the infiltration of type-2 T helper (Th2) cells and negatively related to the infiltration of type-17 T helper (Th17) cells. Figure 5ssGSEA of PSAT1 in UCEC. ( A) Correlation between the relative abundances of 24 immune cells and PSAT1. The size of dots represents the absolute value of Spearman R. (B) The immune cells with clear correlation were visualized in the form of a chord diagram.
## Prediction and verification of miRNAs upstream of PSAT1
We used StarBase to predict possible miRNAs upstream of PSAT1 in UCEC. Five possible miRNAs were predicted, including hsa-miR-195-5P, hsa-miR-497-5p, hsa-miR-145-5P, hsa-miR-424-5P and hsa-miR-139-5P. The results showed that the expression levels of these five miRNAs were lower in UCEC compared to those in normal endometrial tissues (Fig. 6A). Moreover, we analyzed the association of these five miRNAs with UCEC prognosis (Fig. 6B); however, only two miRNAs (hsa-miR-195-5P and hsa-miR-497-5p) were strongly negatively correlated with the prognosis of patients with UCEC. We analyzed the correlation between these miRNAs and PSAT1 in UCEC and demonstrated that hsa-miR-195-5P and hsa-miR-497-5p levels were negatively correlated with PSAT1 expression (Fig. 6C). Subsequent transfection experiment of a miR mimic/inhibitor showed that PSAT1 was negatively regulated by miR-195-5P, whereas miR-497-5P had no significant regulatory effect on Ishikawa or HEC-1-A cells—human endometrial cancer cell lines (Fig. 6D–G). Therefore, we speculated that miR-195-5P is an upstream regulatory gene of PSAT1 in UCEC.Figure 6Prediction and analysis of miRNAs upstream of PSAT1. ( A) The expression level of has-miR-195-5P, hsa-miR-497-5P, hsa-miR-145-5P, hsa-miR-424-5P and hsa-miR-139-5P in UCEC. ( B) *Survival analysis* of hsa-miR-195-5P, hsa-miR-497-5P, hsa-miR-145-5P, hsa-miR-424-5P and hsa-miR-139-5P in UCEC. ( C) *Correlation analysis* between hsa-miR-195-5P, hsa-miR-497-5P and PSAT1. ( D) The transfection efficiency of miR-195-5P mimic/inhibitor in Ishikawa and HEC-1-A cells. ( E) The transfection efficiency of miR-497-5P mimic/inhibitor in Ishikawa and HEC-1-A cells. ( F) The relative mRNA expression level of PSAT1 after Ishikawa and HEC-1-A cells were transfected with miR-195-5P mimic/inhibitor. ( G) The relative mRNA expression level of PSAT1 after Ishikawa and HEC-1-A cells were transfected with miR-497-5P mimic/inhibitor. * $p \leq 0.05$, **$P \leq 0.01$, ***$p \leq 0.001$, ****$P \leq 0.0001$, ns, $P \leq 0.05.$
## PSAT1 promotes Ishikawa and HEC-1-A cells proliferation and migration in vitro
To further validate the role of PSAT1 in UCEC, we performed several functional experiments. qPCR and western blotting confirmed that PSAT1 was successfully knocked down in Ishikawa and HEC-1-A cells. After knocking down PSAT1, the protein expression level of proliferating nuclear antigen (PCNA) decreased (Fig. 7A,B). The CCK8, EdU and clone formation assays confirmed that PSAT1 knockdown inhibited proliferation in Ishikawa and HEC-1-A cells (Fig. 7C–E). According to flow cytometry, the number of Ishikawa and HEC-1-A cells in the S and G2 phases decreased, and more cells aggregated in the G1 phase after PSAT1 knockdown (Fig. 7F). In addition, the results of western blotting in Fig. 7B showed that the protein expression levels of CyclinD1 and CyclinE1 decreased after PSAT1 was knocked down. CyclinD1 and CyclinE1 are two key proteins that regulate G1 phase and the transition from G1 phase to S phase of cells31. These results suggested that PSAT1 might mediate the G1 phase arrest of cells in UCEC. These experiments demonstrated that PSAT1 plays a pro-proliferative role in UCEC. PSAT1 knockdown also reduced cell migration and invasion in Wound healing and Transwell assays (Fig. 7G,H). Epithelial-mesenchymal transition (EMT) plays an important role in the migration and invasion of epithelial tumor cells32. We detected the protein expression levels of EMT-related makers (N-cadherin and Vimentin) in Ishikawa and HEC-1-A cells by western blotting. The results showed that the protein expression levels of N-cadherin and Vimentin were reduced after PSAT1 was knocked down (Fig. 7B). These results suggested that PSAT1 might promote cell migration and invasion through EMT in UCEC. Overall, PSAT1 can influence the progression of UCEC by regulating cell proliferation, migration, and invasion. Figure 7PSAT1 promotes Ishikawa and HEC-1-A cells proliferation and migration. ( A) qPCR and (B) western blotting showed the PSAT1 knockdown efficiency and the protein expression level of PCNA, CyclinD1, CyclinE1, N-cadherin and Vimentin in Ishikawa and HEC-1-A cells. Three independent experiments were performed; representative results were shown. The blots were cut prior to hybridization with antibodies according to the molecular size of the target gene. PSAT1 protein was detected around 40 kDa. PCNA protein was detected below 40 kDa and above 35KDa. CyclinD1 protein was detected below 40 kDa and above 25KDa. CyclinE1 protein was detected below 55 kDa and above 40KDa. N-cadherin protein was detected below 150 kDa and above 100KDa. Vimentin protein was detected below 70 kDa and above 55KDa. Data normalized using β-actin. β-actin protein was detected below 55 kDa and above 40 KDa. Original blots were presented in Supplementary Fig. 1. ( C) CCK8, (D) EdU and (E) clone formation assays demonstrated that PSAT1 promotes Ishikawa and HEC-1-A cells proliferation. ( F) Cell cycle analysis by flow cytometry. ( G) Wound healing assay of Ishikawa and HEC-1-A cells. ( H) Transwell assay to detect the migration and invasion of Ishikawa and HEC-1-A cells. Scale bar: 100 μm. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001.$
## Discussion
UCEC is one of the major causes of poor health in women. The prognosis of early-stage UCEC is often optimistic, but that of most advanced is poor and can even result in death33. Therefore, it is important to identify early diagnostic markers for UCEC34,35. There has evidence that PSAT1 acts as an oncogene in many tumors36–38 and that it plays an important role in the development of various cancers39. In our study, PSAT1 was significantly overexpressed in UCEC from TCGA. Furthermore, UCEC patients with higher expression of PSAT1 had a worse prognosis than those with lower expression of PSAT1, and the upregulated expression of PSAT1 was related to the clinical stage, age, histological type, and weight of UCEC. In the ROC curve analysis of PSAT1, the AUC value for the diagnosis of UCEC was high. PSAT1 may therefore prove useful as a potential biomarker for the diagnosis of UCEC.
To further explore the reasons for the differences in UCEC prognosis, the DEGs was analyzed between patients with high and low PSAT1 expression. The results revealed that in UCEC patients with high PSAT1 expression, 32 genes were up-regulated, and 116 genes were down-regulated. Furthermore, GO enrichment analysis was performed on the 148 DEGs, and the functions were mainly concentrated in metabolic processes, immune system processes and growth. UCEC patients are often obese and have metabolic abnormalities40,41. The results of GO enrichment analysis were consistent with this. These results showed that PSAT1 may participate in the metabolic regulation of UCEC. In addition, KEGG enrichment analysis was used to identify the crucial pathways associated with PSAT1 in UCEC revealing a significant enrichment of cell cycle and cellular senescence. From these results, we speculated that PSAT1 might affect the prognosis of UCEC by affecting the cell cycle, cell growth, and immune system processes; indeed, several of our in vitro functional experiments demonstrated that PSAT1 affects UCEC progression by regulating cell proliferation, migration and invasion.
Tumor progression is often closely associated with the surrounding immune microenvironment42–47, and abnormal immune infiltration in patients with UCEC has been widely reported1,48–50. GO enrichment analysis revealed that PSAT1 might be involved in immune infiltration; therefore, we conducted ssGSEA to explore the relationship between PSAT1 and the immune cells. Th2 cells promote macrophage differentiation into M2 macrophages, which are a pro-tumor subtype48. Astragaloside-IV can slow down the progression of lung cancer by partially blocking the M2 polarization of macrophages through the AMPK signaling pathway51. In our study, there was a positive correlation between PSAT1 mRNA expression levels and Th2 cell infiltration. This may explain the poor prognosis of patients with UCEC with high expression levels of PSAT1. In addition, our research found a negative correlation between PSAT1 mRNA expression levels and the infiltration of Th17 cells, which can recruit and activate neutrophils and are associated with improved survival52,53. According to these results, PSAT1 might affect the prognosis of patients with UCEC by affecting immune infiltration of Th2 and Th17 cells. Therefore, PSAT1 may be a potential target for immunotherapy in UCEC.
We predicted the possible upstream miRNAs of PSAT1 in UCEC using the StarBase database to further explore the regulatory mechanism of PSAT1. The results revealed that PSAT1 expression was significantly negatively correlated with two miRNAs (miR-195-5P and miR-497-5p) and PSAT1 expression, where the expression patterns of these two miRNAs in UCEC were opposite to that of PSAT1. This was consistent with the mechanism whereby miRNAs regulate target genes54–56. Furthermore, survival analysis showed that the expression levels of hsa-miR-195-5P and hsa-miR-497-5p were significantly correlated with the prognosis of UCEC. In order to clarify which miRNAs regulate the function of PSAT1, we transfected a miR mimic/inhibitor into Ishikawa and HEC-1-A cells to achieve the effect of miRNA overexpression or knockdown. The results showed that PSAT1 expression was negatively regulated by miR-195-5P but not by miR-497-5P. These results suggest that miR-195-5P is an upstream regulator of PSAT1.
Several studies revealed the crucial role of transcription factors in cancers57,58. We used AnimalTFDB 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/#/) to predict the transcription factors that bind to PSAT1. The spearman results of transcription factors with $p \leq 0.05$ and cor > 0.3 are shown in the Supplementary Table 1. These transcription factors with significant correlation with PSAT1 may have significant implications for the mechanism by which PSAT1 regulates UCEC progression. We will conduct a more in-depth study of these transcription factors in the future.
This study had several limitations. First, abnormal immune cell infiltration in UCEC requires further verification. Furthermore, the mechanism by which the miR-195-5P/PSAT1 axis regulates UCEC prognosis requires further investigation. We aim to conduct relevant research in the future to address these limitations.
## Conclusions
Our study showed that PSAT1 was significantly overexpressed in UCEC and was closely related to its prognosis. Moreover, PSAT1 could be used as an independent predictor for UCEC diagnosis and affect UCEC prognosis by regulating cell proliferation and migration. Furthermore, we found that miR-195-5P is the upstream regulatory gene of PSAT1. The prognosis of UCEC is also influenced by immune infiltration by Th2 and Th17 cells. Thus, PSAT1 may be an effective target for UCEC diagnosis and immunotherapy in the future.
## Study design and samples acquisition
We downloaded RNA sequence data from TCGA (https://portal.gdc.cancer.gov/). Level 3 HTSeq-FPKM data were converted to TPM for subsequent analysis, and a log2 conversion was performed. UCEC patients were classified into two groups, high- and low-PSAT1 expression patients according to their median expression levels of PSAT1. Unpaired samples were analyzed using Wilcoxon rank-sum test, while paired samples were analyzed using paired sample t-test. The Kaplan–Meier plotter was used to construct the survival curves. The CPTAC and the HPA databases were used to explore the expression of PSAT1 in UCEC versus normal endometrial tissue at the protein level.
## Analysis of DEGs between the high- and low-PSAT1 expression groups
DEGs between high- and low-PSAT1 expression patients from TCGA datasets were identified using the limma package in the R software. The adjusted P-value < 0.05 and |Fold Change|> 1.5 were considered as the threshold for the differential expression of mRNAs.
## GO enrichment analysis
The Metascape database2.0 and online tool (http://metascape.org) were used for functional enrichment analysis. In this study, significance was defined as an enrichment factor > 1.5, a minimum count of 3, and a P-value < 0.01.
## KEGG enrichment analysis
The clusterProfiler, enrichplot, and ggplot2 R packages were used for KEGG enrichment analysis. A P-value < 0.05 was regarded as significantly enriched.
## Immune infiltration analysis by ssGSEA
To elucidate the correlation between PSAT1 and the level of immune cell infiltration, ssGSEA from the GSVA package [version 1.34.0] was used59. The markers of the 24 types of immune cells originated from a previous immune article60. The correlation between the immune cell infiltration level and PSAT1 was determined using Spearman’s rank correlation.
## Candidate miRNA prediction
Interactions between miRNAs and PSAT1 were predicted using StarBase (http://starbase.sysu.edu.cn/). StarBase was also used to conduct a correlation analysis of miRNA-PSAT1 in UCEC. The expression levels of candidate miRNAs in UCEC were also analyzed. Statistical significance was set at $p \leq 0.05.$
## Cell culture
The two human endometrial cancer cell lines, Ishikawa and HEC-1-A were purchased from Shanghai Jinyuan Biotechnology Co., Ltd. and Procell (Wuhan, China), respectively. DMEM basic (GIBCO, USA) medium containing $10\%$ fetal bovine serum and $1\%$ penicillin and streptomycin was used for the culture of Ishikawa cells, and McCoy's 5A medium containing $10\%$ fetal bovine serum and $1\%$ penicillin and streptomycin was used for the culture of HEC-1-A cells in a 37 °C cell incubator with $5\%$ carbon dioxide.
## MiR mimic/inhibitor and siRNA transfection
We purchased the miR-195-5P mimic/inhibitor and miR-497-5P mimic/inhibitor from RiboBio (Guangzhou, China), and Tsingke Biotechnology Co., Ltd designed and synthesized the siRNAs. The miR mimic/inhibitor and siRNA sequences are listed in Supplementary Table 2. Lipofectamine 2000 Reagent (Invitrogen, USA) was used for miR mimic (50 nM), miR inhibitor (100 nM), and siRNA (50 nM) transfection in Ishikawa and HEC-1-A cells. At 48–96 h after transfection, transfection efficiency was measured. All operations were performed according to the manufacturer’s instructions.
## Real-time quantitative PCR
Total RNA was extracted using an RNA-Quick Purification Kit (ESscience, China). The PrimeScript™ RT Reagent Kit with a gDNA Eraser (Takara, Japan) and SYBR Green™ Premix Ex Taq™ II (Takara, Japan) were used for reverse transcription and real-time PCR. Primer sequences are shown in Supplementary Table 2.
## Western blot
RIPA lysis buffer (Beyotime, China) containing a protease and phosphatase inhibitor mixture was used to extract proteins from Ishikawa and HEC-1-A cells. A BCA Protein Assay Kit (Solarbio, China) was used to detect protein concentrations. We separated proteins with sodium dodecyl sulfate–polyacrylamide gel (SDS-PAGE) and then transferred them to PVDF membranes. After blocking with $5\%$ non-fat milk for 1.5 h at room temperature, the membranes were incubated with the primary antibody overnight at 4 °C. We used the following primary antibodies, anti-β-actin (Servicebio, China), anti-PCNA (Proteintech, China), anti-PSAT1 (Proteintech, China), anti-N-cadherin (Proteintech, China), anti-Vimentin (Proteintech, China), anti-CyclinD1 (ZenBio, China) and anti-CyclinE1 (ZenBio, China). After washing the membranes with TBST three times, membranes were incubated with a secondary antibody (ZSGB Bio, China) for 1.5 h at room temperature, and then a chemiluminescence imaging system (Clinx S6, China) was used to visualize the protein bands.
## Cell proliferation assays
About 3000 cells per well were seeded in a 96-well plate, and cell proliferation was evaluated using the CCK-8 (APExBIO, USA) assay according to the manufacturer’s protocol. Two hours after incubation with CCK-8, the absorbance was measured at OD450 using a Multifunctional Microplate Reader (Thermo Fisher Scientific, USA).
## EdU assay
A Cell-Light EdU Apollo In Vitro Kit (RiboBio, China) was used according to the manufacturer’s protocol. The cells were then observed and photographed using a fluorescence microscope (Nikon ECLIPSE Ti, Japan).
## Clone formation assays
About 1000 cells per well were seeded in a 6-well plate, and after 10–14 d of culture, the cells were fixed with $4\%$ paraformaldehyde (Servicebio, China) and stained with crystal violet (Solarbio, China). Finally, the clones were photographed and counted.
## Flow cytometry
Forty-eight hours after the cells were transfected in a 6-well plate, they were collected from each well. After washing the cells twice with PBS, $75\%$ alcohol was added slowly to fix the cells. The tube was shaken while alcohol was added to prevent cell aggregation. Finally, the cells were stained with PI (BD Biosciences, USA) for 10 min and subjected to flow cytometry for cell cycle detection.
## Wound healing assay
A 6-well plate was used to culture cells and the cells were scratched using a 10 μL pipette tip when the cell density reached approximately $90\%$. The cell culture medium was then replaced with a serum-free basal medium. The scratches were photographed at 0 and 48 h, and Image J software was used for image quantification.
## Transwell assay
Transwell assays were performed using 8-µm Transwell chambers (Biofil, China) in 24-well plates. In migration assay, lower chambers were filled with 500 μL medium containing $10\%$ serum, while upper chambers were seeded with approximately 40,000 cells resuspended in 200 μL of serum-free basal medium. Regarding invasion assay, before adding cells to the transwell chambers, they were evenly covered with Matrigel (Corning, USA) (50 μL per chamber). Then the plates were placed in a 37 ℃ incubator and incubated for 2 h. Subsequent steps are the same for migration assay and invasion assay. After incubating for 24 h, cells that migrated to the outside of the chamber were fixed with $4\%$ paraformaldehyde for 20 min and stained with crystal violet (Beyotime, China) for 30 min. The cells were then photographed and counted.
## Statistical analysis
We used the paired sample t-test and Wilcoxon rank-sum test to determine the expression levels of PSAT1 in paired and non-paired samples. The Kaplan–Meier plotter was used to construct the survival curves. We used the ROC curve to explore the diagnostic value of the expression level of PSAT1 based on the pROC package. The p values of all results were bilateral, with a significance level of 0.05. Data analysis was performed using R version 3.6.3 and GraphPad Prism 8.0.2. All data in this study are presented as means ± SD, and at least three independent replicates were performed for all experiments.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31325-0.
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|
---
title: DNA-dependent protein kinase catalytic subunit (DNA-PKcs) drives chronic kidney
disease progression in male mice
authors:
- Yunwen Yang
- Suwen Liu
- Peipei Wang
- Jing Ouyang
- Ning Zhou
- Yue Zhang
- Songming Huang
- Zhanjun Jia
- Aihua Zhang
journal: Nature Communications
year: 2023
pmcid: PMC10008567
doi: 10.1038/s41467-023-37043-5
license: CC BY 4.0
---
# DNA-dependent protein kinase catalytic subunit (DNA-PKcs) drives chronic kidney disease progression in male mice
## Abstract
Kidney injury initiates epithelial dedifferentiation and myofibroblast activation during the progression of chronic kidney disease. Herein, we find that the expression of DNA-PKcs is significantly increased in the kidney tissues of both chronic kidney disease patients and male mice induced by unilateral ureteral obstruction and unilateral ischemia-reperfusion injury. In vivo, knockout of DNA-PKcs or treatment with its specific inhibitor NU7441 hampers the development of chronic kidney disease in male mice. In vitro, DNA-PKcs deficiency preserves epithelial cell phenotype and inhibits fibroblast activation induced by transforming growth factor-beta 1. Additionally, our results show that TAF7, as a possible substrate of DNA-PKcs, enhances mTORC1 activation by upregulating RAPTOR expression, which subsequently promotes metabolic reprogramming in injured epithelial cells and myofibroblasts. Taken together, DNA-PKcs can be inhibited to correct metabolic reprogramming via the TAF7/mTORC1 signaling in chronic kidney disease, and serve as a potential target for treating chronic kidney disease.
Kidney injury leads to fibrosis during the progression of chronic kidney disease. Here the authors report that the DNA-dependent protein kinase catalytic subunit (DNA-PKcs) drives chronic kidney disease progression in a study with male mice, potentially via TAF7/RAPTOR/mTORC1 signaling.
## Introduction
Chronic kidney disease (CKD) inflicts on $10\%$ of the adults worldwide1. CKD poses a huge burden on global public health, as it has a high likeliness to progress into end-stage renal disease (ESRD), which requires dialysis or kidney transplantation1,2. Renal interstitial fibrosis is one of the major pathological characteristics of CKD2–4. Many of the pathophysiological features of kidney fibrosis are shared by other fibrotic diseases, such as liver cirrhosis and cardiomyopathies5. There are no effective treatments for CKD, highlighting an urgent need for a better understanding of the pathological mechanisms underlying CKD.
In injured kidneys, interstitial fibrosis involves the abnormal expression of profibrotic factors, such as transforming growth factor-beta 1 (TGF-β1), epithelial dedifferentiation and myofibroblast activation2. As a key mediator of interstitial fibrosis, TGF-β1 not only activates the expression of fibrotic genes, including α-smooth muscle actin (α-SMA), fibronectin (FN) and collagens, but also promotes Warburg effect-like metabolic reprogramming of kidney cells6,7. Recently, emerging evidence has demonstrated the relationship between metabolic dysregulation and interstitial fibrosis, revealing that metabolic reprogramming occurs in kidney cells (renal tubular epithelial cells8,9 and myofibroblasts10) during kidney injury and contributes to the development of CKD. Metabolic reprogramming in kidney cells causes a drastic reduction in fatty acid oxidation (FAO) and a metabolic shift to glycolysis, contributing to immune cell infiltration and interstitial fibrosis7,11,12. Furthermore, clinical data have revealed that metabolism and inflammation pathways are dysregulated in CKD patients7. Restoring FAO or inhibiting glycolysis through genetic or pharmacological methods attenuates fibrosis in various animal models of renal fibrosis11,13–15.
In the molecular mechanisms of metabolic reprogramming, mammalian target of rapamycin (mTOR), which is a cytoplasmic serine/threonine protein kinase, acts to modulate cell metabolism and maintain metabolic homeostasis16,17. Two distinct functional complexes of mTOR have been defined according to its binding proteins. MTOR complex 1 (mTORC1) interacts with its specific regulator RAPTOR and is sensitive to rapamycin18,19. mTOR complex 2 (mTORC2) is regulated by RICTOR20. Chronic mTORC1 activation promotes metabolic reprogramming in various pathological conditions, including renal interstitial fibrosis9,21–23, but the mechanisms that regulate this activation, especially in interstitial fibrosis, remain largely unknown.
DNA-dependent protein kinase (DNA-PK), a trimeric complex composed of a catalytic subunit (DNA-PKcs) and a Ku$\frac{70}{80}$ heterodimer, is activated by DNA double-stranded breaks (DSBs)24,25 or ROS26. A well-known function of DNA-PK is mediating nonhomologous end joining (NHEJ), which joins programmed DSBs created during V(D)J recombination and plays a key role in the recombination of lymphocytes25. As a result, mutations in DNA-PKcs arrest the development of T and B lymphocytes, due to the deficiency of V (D) J recombination27,28. However, the growth retardation or high frequency of T cell lymphoma development did not appear in DNA-PKcs knockout mice28. Although it is a DNA DSB sensor, DNA-PKcs has some unusual properties, such as accumulating in cells to a level far above what is likely needed for NHEJ29. Moreover, DNA-PKcs is located both in the nucleus and the cytoplasm30. In agreement with its unusual properties, recent evidence indicates that DNA-PKcs has additional functions other than NHEJ31. For example, DNA-PKcs-mediated phosphorylation of the transcription factor USF-1 promotes fatty acid synthesis induced by insulin32, and DNA-PKcs promotes metabolic decline during aging33. Research on the non-NHEJ functions of DNA-PKcs, such as metabolism and aging, has just budded, and a number of questions remain unanswered31. Additionally, as one of the phosphoinositide 3-kinase (PI3K)-related kinases, DNA-PK had been found to regulate mTOR activation34.
In this work, our results show that DNA-PKcs mediates the activation of RAPTOR/mTORC1 signaling through phosphorylation of TATA-box binding protein associated factor 7 (TAF7) in CKD. Inhibition of DNA-PKcs corrects metabolic reprogramming in injured epithelial cells and myofibroblasts in CKD. DNA-PKcs may serve as a new potential target for treating CKD.
## DNA-PKcs is increased in the kidneys of CKD patients and in mice with kidney fibrosis
The activity of DNA-PKcs was analyzed by immunohistochemical staining of S2056 autophosphorylation in DNA-PKcs (p-DNA-PKcs) in healthy and CKD human kidney tissues. The nontumor portions of nephrectomy samples were used as “healthy” kidney tissues, which were collected from patients with renal cell carcinoma. The results showed that p-DNA-PKcs was almost undetectable in these normal kidney tissues, but the protein levels of p-DNA-PKcs were markedly increased in CKD human kidney tissues (Fig. 1a). To determine the relevance of DNA-PKcs activity and renal fibrosis in vivo, expression of fibronectin (FN), a crucial gene related to kidney fibrosis, was analyzed by immunohistochemical staining, and Sirius red staining was also performed to analyze the degree of interstitial fibrosis in CKD patients (Fig. 1a). Our results revealed a positive association between the protein levels of p-DNA-PKcs and the degree of interstitial fibrosis ($$P \leq 0.005$$, Fig. 1b, c). Additionally, expression levels of DNA-PKcs were also analyzed using previously published human CKD microarray datasets (Nephroseq). We found that mRNA levels of DNA-PKcs were significantly increased by more than 1.6-fold in renal biopsy tissues of 48 CKD patients compared to controls (Supplementary Fig. 1a), exhibiting a significant positive correlation with the degree of kidney fibrosis in patients (Supplementary Fig. 1b). Furthermore, the results of immunofluorescence co-staining of p-DNA-PKcs with *Lotus tetragonolobus* lectin (LTL) (a renal tubule marker) or α-SMA (a marker of myofibroblasts) showed that DNA-PKcs was upregulated in both tubular epithelial cells and myofibroblasts of the kidney tissues from CKD patients (Fig. 1d, e). Analysis of an available online renal single-cell RNA sequencing database (http://humphreyslab.com/SingleCell/)35 revealed that DNA-PKcs mRNA was widely expressed in kidney cells and upregulated in human diabetic kidneys compared to control kidneys (Supplementary Fig. 1c).Fig. 1DNA-PKcs is increased in the kidneys of CKD patients and mice with kidney fibrosis.a Immunohistochemical staining of p-DNA-PKcs (indicated by red arrow), FN and Sirius red staining in kidney tissues of normal ($$n = 4$$) and CKD ($$n = 15$$) patients, scale bars: 20 μm. Bars represent quantification results and data are shown as the mean ± SD. Two-tailed unpaired t-test was used to determine the p-values. b Pearson’s r correlation analysis between FN and p-DNA-PKcs protein levels in CKD patients ($$n = 15$$) with $95\%$ confidence interval from 0.7701 to 0.9733. c Pearson’s r correlation analysis between collagens deposition (Sirius red) and p-DNA-PKcs protein levels in CKD patients ($$n = 15$$) with $95\%$ confidence interval from 0.4062 to 0.9159. d Immunofluorescence co-staining of p-DNA-PKcs with a renal tubule marker, *Lotus tetragonolobus* lectin (LTL) in kidney tissues of normal ($$n = 4$$) and CKD ($$n = 4$$) human study participants, red: p-DNA-PKcs, green: LTL, bule: DAPI, scale bars: 20 μm. Tubule is indicated by arrow. e Immunofluorescence co-staining of p-DNA-PKcs with α-SMA in kidney tissues of normal ($$n = 4$$) and CKD ($$n = 4$$) human study participants, red: p-DNA-PKcs, green: a-SMA, bule: DAPI, scale bars: 20 μm. Myofibroblast is indicated by arrow. f Immunohistochemical staining of p-DNA-PKcs (indicated by red arrow) in kidney tissues of UUO mice, scale bars: 20 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). One-way ANOVA with Tukey’s multiple comparisons test was used to determine the p-values. g Western blot analysis of DNA-PKcs in kidney tissues of UUO mice ($$n = 6$$ mice of each group). h Immunohistochemical staining of p-DNA-PKcs (indicated by red arrow) in kidney tissues of UIR mice (day 21), scale bars: 20 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). Two-tailed unpaired t-test was used to determine the p-values. Source data are provided as a Source Data file.
To further identify the association between DNA-PKcs activity and kidney fibrosis development, we next examined the expression and activity of DNA-PKcs in two mouse models of kidney fibrosis, unilateral ureteral obstruction (UUO) and unilateral ischemia-reperfusion (UIR). Immunohistochemical staining of p-DNA-PKcs indicated that p-DNA-PKcs was markedly induced on day 3 compared to sham controls in the UUO model, and protein levels of p-DNA-PKcs were further increased as kidney fibrosis developed from day 3 to day 14 (Fig. 1f). Western blot and qRT-PCR results showed that the total protein and mRNA levels of DNA-PKcs were also significantly upregulated in kidney tissues of UUO mice compared to sham controls (Fig. 1g and Supplementary Fig. 1e). Similar results were observed in UIR mice (Fig. 1h and Supplementary Fig. 1d, f). The potential link between DNA-PKcs and CKD prompted us to further investigate the role and mechanistic implications of DNA-PKcs in CKD.
## DNA-PKcs-mediated progression of CKD in mice
Next, we created DNA-PKcs knockout mice in which the DNA-PKcs-encoded gene Prkdc was knocked out using CRISPR/cas9 as described in the Methods (Supplementary Fig. 2a). DNA-PKcs heterozygous (+/−) and homozygous (−/−) knockout and wild-type (WT) mice were distinguished by genotyping, as shown in Supplementary Fig. 2a. Prkdc mRNA (Supplementary Fig. 2c) and DNA-PKcs protein levels (Fig. 2e) were not detected in kidney tissues of DNA-PKcs−/− mice, indicating successful knockout of DNA-PKcs in mice. To evaluate the effect of the DNA-PKcs knockout strategy on renal damage and fibrosis, adult (6–8 weeks old) DNA-PKcs−/− and WT control male mice were subjected to UUO and euthanized 7 days after surgery. PAS staining revealed that the degree of tubular atrophy and dilatation induced by UUO was markedly ameliorated in DNA-PKcs−/− mice compared to WT controls (Fig. 2a). Renal fibrosis was also greatly ameliorated in DNA-PKcs−/− mice compared to WT mice, as observed, and quantified by Masson staining (Fig. 2b). Images of immune labeling for interstitial collagen I deposition and α-SMA showed that both classical profibrotic markers were significantly reduced in the UUO kidneys of DNA-PKcs−/− mice compared to WT controls (Fig. 2c, d). Protein levels of profibrotic markers, including FN and α-SMA, were also significantly reduced in the UUO kidneys of DNA-PKcs−/− mice, as analyzed by Western blot (Fig. 2e). QRT-PCR analysis showed that knocking out DNA-PKcs in mice markedly reduced expression levels of crucial kidney fibrosis genes, including Col1a1, Col3a1, Fn1, and Acta2, which were greatly upregulated in UUO kidneys. Consistent with reduced fibrosis, DNA-PKcs knockout also reduced mRNA levels of the kidney injury marker KIM-1 induced by UUO (Supplementary Fig. 2c). Moreover, inflammation is a major hallmark of kidney fibrosis and plays an important role in the pathogenesis of renal fibrosis. Thus, we analyzed inflammation-related markers, including the cytokines Il1β, Il6, Tnfα, and Mcp1, by qRT-PCR. In the UUO model, we found that upregulation of inflammatory cytokines induced by UUO was markedly reduced by DNA-PKcs knockout (Supplementary Fig. 2c). As both clinical studies and animal models exhibit a strong correlation between macrophages and the extent of renal fibrosis, infiltrated macrophages in UUO kidney tissues were assessed by immunohistochemical staining for the macrophage marker F$\frac{4}{80.}$ The images revealed many infiltrated macrophages in obstructed kidneys of WT mice 7 days after UUO, which were markedly decreased by DNA-PKcs knockout (Fig. 2f). Interesting, DNA-PKcs knockout didn’t aggravate DSBs induced by UUO indicated by lower levels of γH2AX (Supplementary Fig. 2d).Fig. 2Deletion of DNA-PKcs attenuates the progression of CKD in UUO mice.a PAS staining of kidneys of WT and DNA-PKcs−/− mice subjected to UUO (day 7), scale bars: 50 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). b Masson staining of kidneys of WT and DNA-PKcs−/− mice subjected to UUO (day 7), scale bars: 50 μm. c Immunofluorescence staining of COL1A1, scale bars: 20 μm, MFI mean fluorescence intensity. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). d Immunohistochemical staining of α-SMA in kidneys of WT and DNA-PKcs−/− mice subjected to UUO (bar: 20 μm). Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). e Protein levels of DNA-PKcs, FN and α-SMA in kidneys of WT and DNA-PKcs−/− mice subjected to UUO. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). f Immunohistochemical staining of F$\frac{4}{80}$ in kidneys of WT and DNA-PKcs−/− mice subjected to UUO (bar: 20 μm). Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). g Protein levels of DNA-PKcs, FN, and α-SMA in kidneys of tubular epithelial cells (TEC) specific DNA-PKcs−/− and control mice subjected to UUO (day 7). Bars represent quantification results (mean ± SD, $$n = 5$$ mice of each group). h Immunofluorescence staining of DNA-PKcs and LTL in kidneys of DNA-PKcs TEC KO and control mice subjected to UUO (day 7, $$n = 5$$ mice of each group), scale bar: 20 μm. Two-tailed unpaired t-test was used to determine the p-values for b–d. One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values for a, f. Two-way ANOVAs followed by Šídák’s multiple comparisons test were used to determine the p-values for e, g. Source data are provided as a Source Data file.
To further assess the impact of renal tubular DNA-PKcs on renal fibrosis, we constructed proximal renal tubular epithelial cell with specific DNA-PKcs knockout in vivo by using CRISPR/cas9 knockin mice (Supplementary Fig. 2e, f). After subcapsular injection of adeno-associated virus (AAV) containing guide RNA (gRNA) for DNA-PKcs for three weeks, the proximal renal tubular cell-specific DNA-PKcs knockout mice were subjected to UUO and euthanized at 7 days after surgery. The immunofluorescence staining of DNA-PKcs showed an absence of DNA-PKcs in renal tubular cells (Fig. 2h) but it was still expression in α-SMA positive myofibroblasts (Supplementary Fig. 2g) of cas9 mice injected with AAV-gRNA, compared to those in cas9 mice injected with an AAV without gRNA for DNA-PKcs. Western blot analysis showed that the protein levels of DNA-PKcs and profibrotic markers, including FN and α-SMA, were also significantly reduced in the UUO kidneys of cas9 mice injected with AAV-sgRNA, compared to those in the control group (Fig. 2g). Similar to the UUO model, DNA-PKcs knockout mice were also protected from UIR-induced CKD (Supplementary Fig. 2h–m). Together, these results suggest that deletion of DNA-PKcs attenuates the progression of CKD in mice.
To determine whether overexpression of DNA-PKcs in the kidney is sufficient to drive kidney fibrosis, human DNA-PKcs overexpression plasmids (Addgene: 83317) were delivered into mouse kidneys through a tail vein high-pressure injection method as described in several previous studies36. Thirty-six hours after DNA-PKcs plasmid injection, the mice were administered UUO surgery for 7 days. Collectively, our results indicate that ectopic expression of human DNA-PKcs is sufficient to drive the progression of renal fibrosis in vivo (Supplementary Fig. 3).
## Inhibition of DNA-PKcs activity attenuates the development of CKD in mice
These findings led us to consider the possibility that inhibitors of DNA-PKcs could be used to protect against renal fibrosis. To test our hypothesis, adult (6–8 weeks old) WT male mice were treated daily with the highly specific DNA-PKcs inhibitor NU7441 beginning 1 day after UUO surgery. NU7441-treated and control mice were euthanized 7 days after UUO surgery, DNA-PKcs activity was analyzed by immunohistochemical staining, and the results showed that NU7441 treatment markedly inhibited the activation of DNA-PKcs in UUO kidney tissues (Fig. 3a). The degree of tubular atrophy and dilatation induced by UUO was greatly improved after treatment with NU7441 compared to the vehicle control, as shown by PAS staining (Fig. 3b). Histopathological analyses of Masson staining revealed an approximately $50\%$ reduction in renal interstitial fibrosis in the obstructed (UUO) kidneys of NU7441-treated mice compared to vehicle control UUO kidneys (Fig. 3c). Images of immunofluorescence staining showed that the deposition of interstitial collagen I induced by UUO was markedly reduced by NU7441 treatment (Fig. 3e). The protein levels of profibrotic markers, including FN and α-SMA, analyzed by Western blot were also significantly reduced in the UUO kidneys of NU7441-treated mice compared to vehicle controls (Fig. 3f). QRT-PCR analysis showed that NU7441 treatment markedly reduced expression levels of crucial kidney fibrosis genes, including Col1a1, Col3a1, Fn1, and Acta2, in kidney tissues of UUO mice compared to vehicle controls (Supplementary Fig. 4a). Additionally, the infiltration of macrophages in kidney tissues of UUO mice was greatly reduced after treatment with NU7441 compared to vehicle controls (Fig. 3d). NU7441 also decreased expression levels of inflammation-related markers, including the cytokines Il1β, Il6, Tnfα, and Mcp1, in kidney tissues of UUO mice, as analyzed by qRT-PCR (Supplementary Fig. 4a). Furthermore, DNA-PKcs knockout mice were also treated with NU7441 to examine the possible off-target of NU7441. Our results showed NU7441 had no obvious anti-fibrotic effect in DNA-PKcs knockout mice (Fig. 3g, h), suggesting a specific action of NU7441 on DNA-PKcs in this experimental setting. Fig. 3Inhibition of DNA-PKcs activity attenuates the development of CKD in UUO mice.a Immunohistochemical staining of p-DNA-PKcs in kidneys of mice treated with NU7441 (40 mg/kg) or vehicle subjected to UUO (day 7), scale bars: 20 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). b PAS staining of kidneys of mice treated with NU7441 or vehicle subjected to UUO, scale bars: 50 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). c Masson staining of kidneys of mice treated with NU7441 or vehicle subjected to UUO, scale bars: 50 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). d Immunohistochemical staining of F$\frac{4}{80}$ in kidneys of mice treated with NU7441 or vehicle subjected to UUO, scale bars: 20 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). e Immunofluorescence staining of COL1A1 in kidneys of mice treated with NU7441 or vehicle subjected to UUO, scale bars: 20 μm. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). f Protein levels of FN and α-SMA in kidneys of mice treated with NU7441 or vehicle subjected to UUO. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). g Protein levels of FN and α-SMA in kidneys of DNA-PKcs−/− or WT mice treated with NU7441 or vehicle subjected to UUO. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). h Sirius red staining of kidneys of DNA-PKcs−/− or WT mice treated with NU7441 or vehicle subjected to UUO. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). Two-tailed unpaired t-test was used to determine the p-values for a, c, e. One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values for b, d, h. Two-way ANOVAs followed by Šídák’s multiple comparisons test were used to determine the p-values for f, g. Source data are provided as a Source Data file.
The protective effect of NU7441 against UUO-induced renal injury and renal fibrosis was extended to UIR-induced CKD. As expected, NU7441 treatment also protected against UIR-induced renal injury and fibrosis compared to vehicle controls (Supplementary Fig. 4b–h). Together, these results indicate that inhibition of DNA-PKcs activity by NU7441, attenuates CKD progression in mice.
## Inhibition of DNA-PKcs preserves the tubular epithelial cell phenotype and regulates interstitial fibroblast activation in vitro
Epithelial injury and myofibroblast activation are central events in the pathogenesis of CKD2. Immunostaining revealed that p-DNAPKcs were obviously induced by TGFβ1 stimulation in both HK-2 cells (Fig. 4a) and primary tubular epithelial cells (Supplementary Fig. 5a). Western blot results showed that the phosphorylation and total protein levels of DNA-PKcs were also significantly upregulated in TGFβ1 or H2O2 treated HK-2 cells (Fig. 4i and Supplementary Fig. 5b). Additionally, DNA-PKcs knockout HK-2 cells were generated using CRISPR–Cas9, and WT and DNA-PKcs−/− mouse primary tubular epithelial cells were cultured. Successful construction of DNA-PKcs knockout HK-2 cells was confirmed by Western blotting (Supplementary Fig. 5c). Our results showed that knockout of DNA-PKcs or inhibition of DNA-PKcs by NU7441 ameliorated the dedifferentiation of renal epithelial cells, as shown by lower levels of FN and Col1A1 production after exposure to TGFβ1 in DNA-PKcs knockout or NU7441-treated cells (Fig. 4b–d, k, l). However, KU80 knockout boosted FN production induced by TGFβ1 in tubular epithelial cells (Fig. 4m). Like UUO model, DNA-PKcs knockout didn’t aggravate DNA breaks in HK-2 cells induced by H2O2 (Supplementary Fig. 5d). KU$\frac{70}{80}$ first recognizes DNA broken ends at DSB then recruits DNA-PKcs to form DNA-PK which is necessary in the repair of DSB via NHEJ37. These results suggested that DNA-PKcs enhanced the profibrotic action of renal epithelial cells in CKD independent of its DSB repair function. Fig. 4Blockade of DNA-PKcs abolishes TGFβ1-induced tubular epithelial cell dedifferentiation and fibroblast activation.a Representative immunofluorescence images of p-DNA-PKcs in DNA-PKcs−/− and NC (negative control) HK-2 cells treated with or without TGFβ1 (5 ng/ml) for 24 h, scale bars: 20 μm, $$n = 3$$ biologically independent experiments. b Representative immunofluorescence images of COL1A1 in HK-2 cells, primary tubular epithelial cells (c) and NU7441 (0.1 μM) or vehicle-treated HK-2 cells (d) treated with or without TGFβ1 (5 ng/ml) for 24 h, scale bars: 20 μm. $$n = 3$$ biologically independent experiments. e Representative immunofluorescence images of p-DNA-PKcs, COL1 (f), α-SMA (g) and EdU (h) in NU7441 (0.1 μM) or vehicle-treated NRK-49F cells treated with or without TGFβ1 (5 ng/ml) for 24 h, scale bars: 20 μm, $$n = 3$$ biologically independent experiments. i Protein levels of p-DNA-PKcs and DNA-PKcs in HK-2 cells treated with or without TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ biologically independent experiments). j Protein levels of p-DNA-PKcs and DNA-PKcs in NRK-49F cells treated with or without TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ biologically independent experiments). k Protein levels of FN in DNA-PKcs−/− or NU7441-treated (l) HK-2 cells analyzed by Western blot. Bars represent quantification results (mean ± SD, $$n = 3$$ biologically independent experiments). m Protein levels of FN and KU80 in KU80−/− and NC HK-2 cells treated with or without TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ biologically independent experiments). n Western blot analysis of protein levels of FN in NU7441 (0.1 μM) or vehicle-treated NRK-49F cells. Bars represent quantification results (mean ± SD, $$n = 3$$ biologically independent experiments). One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values for k–n. Two-way ANOVAs followed by Šídák’s multiple comparisons test were used to determine the p-values for i, j. NC negative control. Source data are provided as a Source Data file.
To investigate whether DNA-PKcs is involved in the activation and proliferation of interstitial fibroblasts, NRK-49F rat renal fibroblasts were cultured. Western blot results showed that the phosphorylated and total protein levels of DNA-PKcs were also significantly upregulated in TGFβ1 treated NRK-49F cells (Fig. 4j). Immunostaining revealed that the activity of DNA-PKcs was markedly induced by TGFβ1 treatment in NRK-49F cells (Fig. 4e) but was markedly inhibited by NU7441 treatment. Treatment of NRK-49F cells with TGFβ1 induced fibroblast activation and myofibroblast differentiation with upregulation of COL1, α-SMA and FN, whereas pretreatment with NU7441 greatly blunted the profibrotic phenotype (Fig. 4f, g, n). Moreover, NU7441 treatment significantly inhibited NRK-49F cell proliferation induced by TGFβ1 compared to vehicle controls, as indicated by EdU staining (Fig. 4h). Taken together, these data indicate that inhibition of DNA-PKcs preserves the tubular epithelial cell phenotype and regulates interstitial fibroblast activation induced by TGFβ1 in vitro.
Moreover, we also examined the mechanism of TGFβ1-induced DNA-PKcs expression. First, we found multiple possible binding sites of SMAD2 in the promoter region of DNA-PKcs through JASPAR transcription factor binding profiles analysis38. Second, luciferase assay results showed that overexpression of SMAD2 increased transcriptional activation of DNA-PKcs (Supplementary Fig. 5e). Additionally, the effect of DNA-PKcs on the activation of SMAD signaling was also analyzed. The results showed DNA-PKcs knockout inhibited the activation of SMAD2/SMAD3 in UUO mouse model (Supplementary Fig. 5f). These results suggested SMAD2/SMAD3 upregulated DNA-PKcs expression, while DNA-PKcs activated SMAD2/SMAD3 (Supplementary Fig. 5g), forming a positive loop.
## DNA-PKcs-mediated phosphorylation of TAF7 aggravates renal fibrosis
To further dissect the possible molecular mechanisms by which DNA-PKcs regulates the development of CKD, phosphoproteomics was performed to reveal possible substrates of DNA-PKcs. As shown in Fig. 5a, most of the differentially phosphorylated proteins were nuclear proteins. The phosphorylation levels of only 6 proteins of these proteins were significantly decreased in kidney tissues of DNA-PKcs−/− mice compared to WT mice, as shown by the volcano plots (Fig. 5a). Heatmap analysis of differentially phosphorylated proteins between DNA-PKcs−/− mice and WT controls showed that phosphorylation levels of the indicated sites of Slc4a1, Ampd2, Hbb-b1, Gas2, Ank1 and TAF7 were significantly decreased after DNA-PK knockout (Fig. 5a). Through subcellular localization analysis and knockdown of these proteins, we found that DNA-PKcs mediates renal fibrosis, perhaps through phosphorylation of TAF7, which is a subunit of TFIID. Coimmunoprecipitation studies revealed a possible direct interaction between DNA-PKcs and TAF7 (Fig. 5b). Protein-Protein docking between DNA-PKcs and TAF7 was analyzed through an online database ClusPro39,40. The docking results showed a possible direct interaction between DNA-PKcs and TAF7, and the 213-site serine of TAF7 is one of the nearest sites perhaps binding to DNA-PKcs (Supplementary Fig. 6a). An in vitro kinase assay was performed to analyze whether TAF7 was phosphorylated by DNA-PKcs directly. The products of kinase assay were analyzed by mass spectrometry. The results showed the S213, T137, Y24, and S171 sites of human TAF7 were phosphorylated by DNA-PKcs (Supplementary Fig. 6b). Furthermore, our results revealed a positive association between the protein levels of p-DNA-PKcs and TAF7 in human CKD kidney tissues via immunofluorescence co-staining of p-DNA-PKcs with TAF7 (Fig. 5c).Fig. 5DNA-PKcs-mediated phosphorylation of TAF7 aggravates renal fibrosis.a Phosphoproteomics profiling of WT and DNA-PKcs−/− kidney tissues. Differentially expressed statistical analysis, including subcellular localization, volcano plot and heat-map analysis, is shown ($$n = 3$$ mice of each group). b the interaction between Flag-tagged TAF7 and endogenous DNA-PKcs visualized by immunoprecipitation with an anti-DNA-PKcs antibody and immunoblotting with an anti-Flag antibody ($$n = 3$$ independent experiments). c Immunofluorescence co-staining of p-DNA-PKcs with TAF7 in kidney tissues of normal and CKD human study participants, red: p-DNA-PKcs, green: TAF7, bule: DAPI, scale bars: 20 μm. Pearson’s r correlation analysis between TAF7 and p-DNA-PKcs protein levels in CKD patients ($$n = 8$$) with $95\%$ confidence interval from 0.4997 to 0.980. d Protein levels of TAF7 in DNA-PKcs−/− or WT mice subjected to UUO (day 7) or UIR (day 21) (e); NU7441 (40 mg/kg) or vehicle treatment mice subjected to UIR (f), bars represent quantification results (mean ± SD, $$n = 6$$ mice); DNA-PKcs−/− HK-2 cells treated with TGFβ1 (5 ng/ml) for 24 h (g) and NU7441 (0.1 μM) treatment HK2 (h) or NRK-49F(i) cells treated with TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ independent experiments). j Protein levels of FN in TAF7−/− and NC mPTCs treated with TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ independent experiments). k Protein levels of FN in TAF7−/− and NC NRK-49F treated with TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ independent experiments). l Immunofluorescence images of COL1A1 in TAF7−/− and NC mPTCs treated with TGFβ1 (5 ng/ml) for 24 h, scale bars: 20 μm ($$n = 3$$ independent experiments). m Immunofluorescence images of COL1A1, α-SMA (n) and EdU (o) in TAF7−/− and NC NRK-49F cells treated with TGFβ1 (5 ng/ml) for 24 h, scale bars: 20 μm ($$n = 3$$ biologically independent experiments). Two-tailed unpaired t-test was used to determine the p-values for j. One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values for d–i, k. Source data are provided as a Source Data file.
A previous study found the calculated molecular weight of TAF7 is about 40 kDa, but phosphorylated TAF7 is approximately 55 kDa41. Western blot and immunohistochemical staining results showed that levels of TAF7 were greatly induced by UUO in the kidney tissues of WT controls (Fig. 5d and Supplementary Fig. 6d). DNA-PKcs knockout inhibited the phosphorylation of TAF7 in both the baseline and UUO models compared to WT controls (Fig. 5d). The phosphorylation of TAF7 in UUO mice was also confirmed by Phos-tag SDS-PAGE42 (Supplementary Fig. 6c). Similar results were also observed in UIR model (Fig. 5e). Treatment with the DNA-PKcs inhibitor NU7441 also inhibited the upregulated phosphorylation of TAF7 induced by UIR in mice (Fig. 5f). Additionally, protein levels of TAF7 analyzed by Western blot showed that both DNA-PKcs knockout in HK-2 cells (Fig. 5g) and treatment with NU7441 in HK2 (Fig. 5h) or NRF-49F cells (Fig. 5i) inhibited the upregulated phosphorylation of TAF7 induced by TGFβ1 in vitro.
Since DNA-PKcs is involved in the phosphorylation of TAF7 in renal fibrosis, we examined whether TAF7 deficiency may affect the dedifferentiation of renal epithelial cells or the activation and proliferation of interstitial fibroblasts induced by TGFβ1. TAF7 knockout mPTC cells and NRK-49F cells were generated using CRISPR/Cas9, and successful construction of TAF7 knockout cells was confirmed by Western blot (Supplementary Fig. 6e). Our results showed that TAF7 knockout ameliorated the dedifferentiation of renal epithelial cells, as shown by low levels of FN and collagen I production after exposure to TGFβ1 (Fig. 5j, l). Similarly, TAF7 knockout blunted the profibrotic phenotype of NRF-49F cells induced by TGFβ1, as shown by low levels of FN production (Fig. 5k), collagen I (Fig. 5m), α-SMA (Fig. 5n) as well as low levels of EdU staining (Fig. 5o) in TAF7−/− cells. To determine whether overexpression of TAF7 in renal epithelial cells is sufficient to drive a profibrotic phenotype, HK-2 cells or NRK49F cells were transfected with a human TAF7 or its mutant’s overexpression plasmids. Western blot results showed that the profibrotic phenotype of HK-2 or NRF49F cells was indeed enhanced by overexpression of TAF7 or its mutants (Supplementary Fig. 6h,j). However, the profibrotic effects of TAF7 were almost entirely blocked by DNA-PKcs knockout in HK-2 cells (Supplementary Fig. 6i). Furthermore, we examined whether TAF7 deficiency may affect the profibrotic effects of DNA-PKcs. Interestingly, TAF7 deficiency inhibited the profibrotic effects of DNA-PKcs overexpression (Supplementary Fig. 6f) and blocked the anti-profibrotic effects of NU7441 in mPTC cells (Supplementary Fig. 6g). Recently, Wang S, et al. found cytoplasmic DNA-PKcs were increased and interacted with Fis1 and phosphorylated it at Thr34 in its TQ motif, which increased the affinity of Fis1 for Drp1 and induced mitochondrial fragmentation in AKI43. In our study, nuclear extraction protein analysis results showed DNA-PKcs was mainly localized and increased in the nucleus in kidneys of UUO mice which was different from AKI model (Supplementary Fig. 6k). And knocking down Fis1 promoted the profibrotic response in renal tubular epithelial cells challenged with TGFβ1, suggesting that Fis1 did not mediate the profibrotic role of DNA-PKcs in CKD (Supplementary Fig. 6l). Taken together, these data indicated that DNA-PK mediated phosphorylation of TAF7 aggravates renal fibrosis.
## TAF7 promotes mTORC1 activation by upregulating expression of RAPTOR
To investigate the mechanisms of the profibrotic effects of TAF7, RNA-Seq was performed to analyze transcriptomic changes in NC and TAF−/− mPTC cells treated with or without TGFβ1. RNA-Seq results showed that TAF7 knockout ameliorated TGFβ1-induced fibrosis-associated gene expression in mPTCs (Supplementary Fig. 7a, b). KEGG pathway enrichment analysis showed that the mTOR signaling pathway was the top pathway between TAF−/− and NC mPTC cells without TGFβ1 treatment (Fig. 6a). Gene set enrichment analysis (GSEA) revealed a significant upregulation of the mTOR signaling pathway after treatment with TGFβ1, which was significantly decreased after TAF7 knockout in mPTC cells compared to NC control cells (Fig. 6a). Heatmap analysis of mTOR signaling demonstrated that RPTOR (RAPTOR), a positive regulator of mTORC144,45, was increased by treatment with TGFβ1, which was inhibited by knockout of TAF7 in mPTC cells (Fig. 6b).Fig. 6DNA-PKcs-mediated phosphorylation of TAF7 promotes mTORC1 activation by upregulating RPTOR expression.a RNA-seq showing that mTOR signaling (indicated by red font) was the top differentially expressed signaling factor between the TAF7−/− and NC groups. GSEA showing that mTOR signaling was upregulated in the NC + TGFβ1 group and suppressed in the TAF7−/− + TGFβ1 group ($$n = 2$$), TGFβ1 (5 ng/ml) treatment for 24 h. b Heatmap image showing that Rptor (indicated by red font) was decreased in the TAF7−/− groups compared to the NC groups with or without TGFβ1 (5 ng/ml) treatment for 24 h ($$n = 2$$ of each group). c Protein levels of RPTOR, mTOR and phosphorylated mTOR analyzed by Western blot in TAF7−/− and NC mPTCs treated with TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ independent experiments). d Luciferase reporter assay in HK-2 cells with TGFβ1 (5 ng/ml) for 24 h. Bars represent results of 3 independent experiments (mean ± SD). e ChIP assay was performed to analysis TAF7 binds to the promoter of Rptor. Bars represent quantification results (mean ± SD, $$n = 3$$ independent experiments). f Protein levels of RPTOR, mTOR and phosphorylated-mTOR were analyzed by Western blot in kidney tissues of DNA-PKcs−/− and WT control mice subjected to UUO (day 7) or UIR (day 21) (g), NU7441 (40 mg/kg) and vehicle treatment mice subjected to UUO (h) or UIR (i), in DNA-PKcs−/− or NC HK-2 cells (j), NU7441 (0.1 μM) or vehicle treatment NRK-49F cells (k) treated with TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group and $$n = 3$$ independent experiments for cell models). One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values for d, e. Two-way ANOVAs followed by Šídák’s multiple comparisons test were used to determine the p-values for c, f–k. Source data are provided as a Source Data file.
Protein levels of RPTOR and phosphorylation of mTOR were both significantly increased after treatment with TGFβ1 in mPTC cells as analyzed by Western blot. However, TAF7 deficiency decreased the protein levels of RPTOR and the phosphorylation of mTOR induced by TGFβ1 in mPTC cells compared to the NC controls (Fig. 6c). MRNA levels of mTOR and its protein partners, including Raptor, Rictor, and Mlst8, were analyzed by qRT-PCR, and the results showed that TGFβ1 treatment upregulated the mRNA levels of Rptor, Rictor, and Mlst8 but not mTOR. TAF7 deficiency significantly decreased the mRNA levels of Raptor both with and without TGFβ1 treatment in mPTC cells compared to NC control cells but not Rictor, Mlst8, or mTOR (Supplementary Fig. 7c). Similarly, DNA-PKcs deficiency or inhibition significantly decreased mRNA levels of Raptor in kidney tissues of UUO mice and UIR mice (Supplementary Fig. 7d, e). Additionally, we constructed two different residue 213 serine site mutants of TAF7, TAF7-S213D, and TAF7-S213A to analyze the phosphorylation of serine 213 of TAF7 on transcriptional activation of human Raptor induced by TGFβ1 in HK-2 cells. The luciferase assay results showed that overexpression of all these TAF7 mutants increased transcriptional activation of human Raptor, although the relative luciferase activity of the TAF7-S213A group was lower than that of the TAF7-WT and TAF7-S213D groups (Fig. 6d). Moreover, ChIP assay results showed that TAF7 could bind to Rptor promoter directly (Fig. 6e). These results indicated that TAF7 mediates transcriptional activation of RPTOR induced by TGFβ1, perhaps not only through phosphorylation of residue 213. Furthermore, our results showed that protein levels of RPTOR and phosphorylation of mTOR were both increased in kidney tissues of UUO and UIR mice and were significantly decreased after DNA-PKcs knockout in both baseline and model mice (Fig. 6f–i). Similar results were also observed in response to DNA-PKcs knockout HK-2 cells and NU7441 treatment in NRF-49F cells (Fig. 6j, k). Additionally, Western blot results showed that protein levels of RPTOR and phosphorylation of mTOR were increased after overexpression of either DNA-PKcs or TAF7 (Supplementary Fig. 7f,g). Moreover, knockdown of RPTOR markedly inhibited the phosphorylation of mTOR induced by TGFβ1 (Supplementary Fig. 7h). Taken together, these results indicate that DNA-PKcs-mediated phosphorylation of TAF7 promotes mTORC1 activation by upregulating the expression of RPTOR.
## Inhibition of DNA-PK corrects metabolic reprogramming in vivo and in vitro
According to several previous studies9,46, aberrant activation of mTORC1 promotes the progression of renal fibrosis by mediating metabolic reprogramming in fibrotic kidneys. Thus, we examined whether inhibition of DNA-PK or TAF7 corrects metabolic reprogramming in fibrotic kidneys. Based on RNA-*Seq analysis* of transcriptomic changes in TGFβ1-treated NC and TAF−/− mPTC cells, KEGG pathway enrichment analysis showed that the metabolic signaling pathway was one of the most differentially expressed pathways between TGFβ1-treated TAF−/− and NC mPTC cells (Fig. 7a). Furthermore, GSEA revealed significant upregulation of oxidative phosphorylation and the fatty acid oxidation (FAO) pathway after treatment with TGFβ1 in TAF7 knockout mPTC cells compared to NC control cells (Fig. 7a). The glycolysis pathway was significantly decreased when TAF7 was knocked out in mPTCs compared to the NC control (Fig. 7a). These results indicated that TAF7 deficiency corrects TGFβ1-induced metabolic reprogramming in mPTC cells. Fig. 7Inhibition of DNA-PK or TAF7 corrects metabolic reprogramming.a RNA-seq showing that metabolic signaling (indicated by red font) was the top differentially expressed signaling pathway between TAF7−/− + TGFβ1 and NC + TGFβ1. GSEA revealed significant upregulation of oxidative phosphorylation and FAO but inhibition of glycolysis in TGFβ1-treated TAF7 knockout mPTCs compared to NC controls, $$n = 2$$ for each group. b Protein levels of ACOX1 and CPT1α were analyzed in kidney tissues of DNA-PKcs−/− and WT control mice subjected to UUO (day 7) or UIR (day 21) (c). Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). d Protein levels of LDHA and HK2 were analyzed in kidney tissues of DNA-PKcs−/− and WT control mice subjected to UUO (day 7) or UIR (day 21) (e). Bars represent quantification results (mean ± SD, $$n = 6$$ mice of each group). f Electron microscopy showing that UUO (day 7)-induced mitochondrial damage in proximal tubule cells of WT mice was almost eliminated by DNA-PKcs knockout, scale bars: 1 μm. Bars represent quantification results (mean ± SD, $$n = 3$$ mice of each group). g ECAR of TGFβ1-treated (5 ng/ml, 24 h) DNA-PKcs−/− HK-2 cells, NU7441 (0.1 μM)-treated NRK-49F cells (h) and TAF7−/− mPTCs (i) were analyzed using Seahorse XF96 cell culture microplates to record baseline (before glucose), glycolysis rate (after glucose), glycolytic capacity (after oligomycin), and glycolytic reserve (after 2-DG). Bars represent the rate of glycolysis (mean ± SD, $$n = 6$$ sample of each group). j Lactate production in the medium of DNA-PKcs−/− HK-2 cells and NU7441 (0.1 μM)-treated NRK-49F cells (k) treated with TGFβ1 (5 ng/ml) for 24 h. Bars represent quantification results (mean ± SD, $$n = 3$$ independent experiments). Two-tailed unpaired t-test was used to determine the p-values for f. One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values for g–k. Two-way ANOVAs followed by Šídák’s multiple comparisons test were used to determine the p-values for b–e. Source data are provided as a Source Data file.
To understand the role of DNA-PKcs in regulating metabolic reprogramming in fibrotic kidneys. First, the protein and mRNA levels of key enzymes involved in fatty acid oxidation (CPT1, CPT2, ACOX1, and ACOX2) were analyzed by Western blot and qRT-PCR. The results showed that expression of these enzymes was significantly reduced in the kidneys of WT mice under UUO or UIR conditions, but their expression was significantly restored in DNA-PKcs knockout mice (Fig. 7b, c and Supplementary Fig. 8). Second, mitochondrial morphology and function are necessary for oxidative phosphorylation, and electron microscopy revealed obvious fragmentation of the mitochondria in kidney tubule cells of WT mice induced by UUO, which was significantly improved in kidney tubule cells of DNA-PKcs−/− mice (Fig. 7f). These results revealed that the abnormal oxidative phosphorylation and fatty acid metabolism induced by UUO in WT mice is improved by DNA-PKcs knockout.
Glycolysis is a feature of renal fibrosis and promotes the development of renal fibrosis. Protein levels of hexokinase 2 (HK2) and lactate dehydrogenase A (LDHA), which are rate-limiting enzymes for glycolysis, were analyzed by Western blot. The results showed that levels of HK2 and LDHA were significantly upregulated in kidney tissues of UUO mice and UIR mice, indicating increased glycolytic activity in fibrotic kidneys. In contrast, glycolysis was significantly inhibited in kidney tissues of DNA-PKcs-deficient mice compared to WT mice in both the UUO and UIR models, as indicated by lower levels of HK2 and LDHA (Fig. 7d, e). Extracellular acidification rate (ECAR) assays were performed to analyze glycolytic activity in TGFβ1-treated DNA-PKcs−/− HK-2 cells and NU7441-treated NRK-49F cells in vitro. Both knockout of DNA-PKcs and treatment with NU7441 significantly decreased the ECAR, which was upregulated in TGFβ1-treated cells compared to control cells (Fig. 7g–h). Knockout of TAF7−/− also significantly decreased the upregulation of ECAR in mPTCs induced by TGFβ1 (Fig. 7i). Additionally, extracellular lactate levels in the culture medium of DNA-PKcs−/− and NU7441-treated cells were also decreased (Fig. 7j, k).
To understand how metabolic reprogramming was improved in DNA-PKcs−/− mice, we used metabolomics profiling to analyze metabolite changes following UUO surgery (Fig. 8a). UUO induced impaired glucose metabolism in the kidneys of WT mice, as evidenced by the downregulation of glucose metabolites, such as pyruvate, in WT UUO kidneys, which was restored by knockout of DNA-PKcs (Fig. 8a). Additionally, increased accumulation of lactate in the kidneys of WT mice subjected to UUO was also observed, indicating a metabolic shift from oxidative phosphorylation to enhanced glycolysis in kidney cells. However, in the injured kidneys of DNA-PKcs−/− mice, glycolysis was significantly inhibited (Fig. 8a). Moreover, accumulation of carnitine-conjugated long-chain fatty acids, including lauroyl carnitine (Fig. 8a) and myristoyl carnitine (Fig. 8a), was observed in the kidneys of WT UUO mice, suggesting defects in fatty acid metabolism. In contrast, DNA-PKcs knockout largely corrected all these abnormalities induced by UUO. Taken together, these results indicate that inhibition of DNA-PK or TAF7 corrects metabolic reprogramming induced by injury or TGFβ1 in vivo and in vitro. Fig. 8Deletion of DNA-PK corrects metabolic reprogramming.a *Metabolomics analysis* of kidney tissues from each group as indicated. Heatmap image showing relative levels of metabolites in the glycolysis pathway, fatty acid metabolism and Krebs cycle in kidneys from each group ($$n = 4$$). Bars represent statistical analysis of representative metabolites in kidneys of each group (mean ± SD, $$n = 4$$ mice of each group). One-way ANOVA followed by Tukey’s multiple comparisons test was used to determine the p-values. b Working model (the template was created with BioRender.com) illustrating in which DNA-PKcs mediates activation of Raptor/mTORC1 signaling through phosphorylation of TAF7 and promotes metabolic reprogramming in injured epithelial cells and myofibroblasts.
## Discussion
Epithelial dedifferentiation and myofibroblast activation and proliferation, all initiated by cellular injury, promote the progression of CKD2. Although the DNA damage response (DDR) often correlates with renal epithelial injury47, little is known about its potential role in epithelial dedifferentiation and myofibroblast activation in progressive CKD. In this study, our results revealed that the expression of DNA-PKcs, a DNA-dependent protein kinase catalytic subunit, increased in fibrotic kidneys and promotes the development of CKD. Although DNA-PKcs is activated by DNA double-stranded breaks (DSBs)24,25 or ROS26, our results showed DNA-PKcs expression was also activated by TGFβ1-SMAD signaling, while DNA-PKcs knockout inhibited SMAD2/SMAD3. These results suggest a new possible pathway mediating TGFβ1-SMAD signaling activation in fibrosis.
Next, we generated global prkdc gene-knockout mice. Our results revealed that deletion of DNA-PKcs attenuated renal tubular injury and the progression of renal interstitial fibrosis in both UUO and UIR mouse models. We cannot conclude that DNA-PK promotes renal fibrosis independent of lymphocytes, as the most prominent phenotype of DNA-PKcs−/− mice is lymphocyte deficiency27,48. To exclude the potential effect of lymphocyte deficiency, we constructed proximal renal tubular epithelial cell with specific DNA-PKcs knockout in vivo by using CRISPR/cas9 knock in mice. Our results revealed that renal tubular specific deletion of DNA-PKcs also hampered the progression of renal interstitial fibrosis in UUO. Additionally, our results also showed that DNA-PKcs deficiency preserved the tubular epithelial cell phenotype and regulated interstitial fibroblast activation in vitro. These results indicate that DNA-PKcs mediates epithelial dedifferentiation and myofibroblast activation, probably without any direct relationship with lymphocyte deficiency. Additionally, we investigated the antifibrotic effects of a highly specific DNA-PKcs inhibitor, NU7441, and the results showed that NU7441 treatment markedly attenuated the progression of renal interstitial fibrosis in both UUO and UIR mouse models. NU7441 is a partial inhibitor of DNA-PK at physiological doses. A previous study indicated that NU7441 treatment did not significantly affect B cell function in adult mice33. Moreover, we confirmed the specific action of NU7441 on DNA-PKcs by treating DNA-PKcs knockout mice with NU7441 in UUO mouse model. These results suggest potential transformation of NU7441 for the clinical treatment of CKD, although further research is needed.
Our results showed DNA-PKcs deficiency did not aggravate DSBs in vivo and in vitro, suggesting that DNA-PKcs mediates renal injury and renal interstitial fibrosis, probably not through its role in DDR. Phosphoproteomics analysis showed that phosphorylation of TAF7 was decreased in the kidney tissues of DNA-PKcs−/− mice. TAF7 deficiency inhibited the profibrotic phenotype of renal epithelial cells and fibroblasts induced by TGFβ1. The profibrotic effects of TAF7 were almost entirely blocked by DNA-PKcs deficiency in renal epithelial cells. Our results indicate that TAF7 is a substrate for DNA-PKcs kinase activity and that DNA-PKcs-mediated phosphorylation of TAF7 aggravates renal fibrosis.
RNA-Seq results showed that TAF7 promotes mTORC1 activation by upregulating the expression of Raptor, which is a positive regulator of mTORC149,50. Phosphoproteome results showed that phosphorylation of TAF7 on serine-213 was significantly decreased in the kidneys of DNA-PKcs−/− mice, compared to that in WT controls. Mass spectrometry analysis indicated the S213, T137, Y24, and S171 sites of human TAF7 were phosphorylated by DNA-PKcs in vitro. Moreover, transfection of different TAF7 mutants, including TAF7-WT, TAF7-S213D, and TAF7-S213A, increased transcriptional activation of human Rptor in HK-2 cells. These results indicate that TAF7 mediates transcriptional activation of Rptor induced by TGFβ1, probably not only through phosphorylation of its 213-site. TAF7 is not a core transcription factor but interacts with other TAFs, such as TAF141,51, and regulates the enzymatic activities of transcription factors, which could explain why overexpression of TAF7 alone did not activate the transcription of human RPTOR in HK-2 cells without TGFβ1 treatment. Several serine residues of TAF7 can be phosphorylated to activate or inhibit transcription41. For instance, a previous study found that phosphorylation of TAF7 on serine 264 disrupted TAF7 binding to TAF1, resulting in transcriptional upregulation of cyclin D141. In addition to TAF1-dependent transcription, TAF7 also regulates TAF1-independent transcription51. In this study, ChIP assay results demonstrated that TAF7 could bind to Rptor promoter directly, suggesting TAF7 perhaps collaborates with other transcription factors to initiate Rptor expression. Although the mechanisms of TAF7-mediated transcriptional upregulation of Rptor need further research, our results indicate that DNA-PKcs mediates the phosphorylation of TAF7 to promote mTORC1 activation by upregulating the expression of Rptor.
Several previous studies have reported that mTORC1 signaling is activated in dedifferentiated epithelial cells and myofibroblasts from fibrotic kidneys9,52. Although DNA-PK has also been reported to phosphorylate and activate AKT/mTOR signaling in tumor cells in response to DNA breaks53, the precise mechanism is unknown. Our results revealed that DNA-PKcs-mediated phosphorylation of TAF7 promotes mTORC1 activation by upregulating the expression of Raptor, providing a possible mechanism of mTORC1 activation in fibrotic kidneys. Chronic activation of mTORC1 promotes metabolic reprogramming in many diseases, including CKD9,54. Recent studies have highlighted that both renal tubular cells and fibroblasts alter metabolic phenotypes in response to CKD9,10,13. The shift from fatty acid oxidative pathways and oxidative phosphorylation to glycolysis is the primary characteristic of metabolic reprogramming9,10. As mTOR plays a central role in maintaining metabolic homeostasis, inhibition of mTOR displays side effects55,56. Moreover, long-term inhibition of TGF-β signaling is also associated with unacceptable adverse effects57. In this study, we found that the activity of DNA-PKcs was almost undetectable in normal kidneys, in contrast to its robust enhancement under CKD. Thus, inhibition of DNA-PKcs corrects metabolic reprogramming in fibrotic kidneys.
In summary, we found that DNA-PKcs mediates the activation of RAPTOR/mTORC1 signaling through phosphorylation of TAF7 and corrects metabolic reprogramming in injured epithelial cells and myofibroblasts (Fig. 8b). DNA-PKcs may serve as a potential target for treating chronic kidney disease.
## Ethical approval
All animal procedures were approved by the Institutional Animal Care and Use Committee of Nanjing Medical University. The protocol concerning the use of human kidney biopsy samples in this study was approved by the Committee on Research Ethics of Children’s Hospital of Nanjing Medical University. The written, informed consent to participate was obtained from all study participants (or their parents/legal guardians). All the human study participants agreed to participation for free.
## Human kidney biopsy sample collection and immunostaining
In this study, injured kidney samples obtained from patients with renal fibrosis (clinical parameters of the patients are listed in Supplementary Table 1) and healthy kidney samples used for immunostaining studies were obtained from the Children’s Hospital of Nanjing Medical University, China. Healthy control samples were nondiseased portions of tissue from renal cell carcinoma patients who had undergone surgery to remove tumor tissues.
## DNA-PKcs−/− mice, Cas9 mice and animal models
DNA-PKcs knockout (DNA-PKcs−/−) mice on a BALB/c background were generated and purchased from GemPharmatech (Nanjing, China). Briefly, a 561 bp sequence of prkdc (the DNA-PKcs-encoded gene) between exon 6 and exon 7 was deleted using the CRISPR/Cas9 method. DNA-PKcs−/− mice and wild-type (WT) littermates were bred in the Laboratory Animal Center of Nanjing Medical University (Nanjing, China) and genotyped using PCR (primers are listed in Supplementary Table 2). Mice were maintained in a standard specific pathogen-free (SPF) animal room free access to food and water with a light/dark cycle of 12 h with room temperature at 21 ± 2 °C and humidity between 45 and $65\%$. To construct proximal renal tubular epithelial cells with specific Cas9 transgenic mice, Rosa26-LSL-Cas9 knockin mice (purchased from GemPharmatech, C57BL/6 J background), as described in a previous study58, were bred with Kap-Cre mice (purchased from Jackson). Kap+cas9+ mice were genotyped using PCR (primers are listed in Supplementary Table 2). *To* generate renal tubular specific knockout of DNA-PKcs, Male Kap+Cas9+ mice (6–8 weeks old) were given subcapsular injection of adeno-associated virus (AAV8) containing guide RNA (gRNA) for DNA-PKcs (5 × 1011 genome copies per mouse according to a previous study59). Kap+Cas9+ mice treated with AAV8 carrying gRNA empty vectors were used as controls. AAV8 carrying gRNA (U6 promoter) for mouse DNA-PKcs (listed in Supplementary Table 2) and empty vectors were purchased from WZ Biosciences Inc (Jinan, China). Mouse renal fibrosis models were generated as previously reported60. Briefly, mice were anesthetized using isoflurane ($3\%$ for induction and $1.5\%$ for maintenance). For the unilateral ureteral obstruction (UUO) model, DNA-PKcs−/− and WT male (male, 6–8 weeks old) were divided into four groups: the UUO and sham control groups of DNA-PKcs−/− and WT mice. For UUO surgery, a flank incision was made on the left side of the abdomen, and the left ureter was tied off using two 4.0 surgical silk ties below the renal pelvis. For the sham groups, the surgery was performed as in the UUO group but without ureteral ligation. For unilateral ischemia–reperfusion surgery (UIR), DNA-PKcs−/− and WT mice (male, 6–8 weeks old) were divided into four groups: the UIR and sham control groups of DNA-PKcs−/− and WT mice. The renal pedicle of the left kidney was clamped using a nontraumatic microaneurysm clamp (RWD Life Science, Shenzhen, China) for 30 min, and the color change of the kidney confirmed successful ischemia–reperfusion. Body temperatures of the mice were maintained at 36.0–38 °C throughout the surgery using a heating instrument.
To examine the effects of a DNA-PK inhibitor (NU7441) in the UUO and UIR mouse models, 6- to 8-week-old male C57BL/6 J mice were purchased from GemPharmatech (Nanjing, China). For the UUO model, one day after surgery, mice oral gavage with or without 40 mg/kg/d NU-7441 in $10\%$ PEG400 in saline until euthanasia once daily. For the UIR model, three days after surgery, mice were dosed once daily by oral gavage with or without 40 mg/kg/d NU-7441 until euthanasia. Mice were euthanized on days 3, 7, or 14 after UUO and on day 21 after UIR. Kidney samples were collected and stored at −80 °C for further analysis. Animal procedures were approved by the Institutional Animal Care and Use Committee of Nanjing Medical University.
## Cell culture and treatment
Human renal tubular epithelial cells (HK-2), mouse renal proximal tubular cells (mPTCs), normal rat kidney interstitial fibroblasts (NRK-49F) and Human Embryonic Kidney 293 (HEK293T) were obtained from American Type Culture Collection (The catalog number of all cell lines are listed in Supplementary Table 3). HK-2 and mPTCs were cultured in DMEM/F-12 medium (Gibco, 319-075-CL), NRK-49F and HEK293T cells were cultured in DMEM. All media were supplemented with $10\%$ fetal bovine serum (FBS, GIBCO), penicillin (100 U/mL) and streptomycin (100 μg/mL) and maintained at 37 °C and $5\%$ CO2 in a humidified incubator. DNA-PKcs or TAF7 knockout cells were constructed using CRISPR/Cas9 methods. Briefly, the sgRNAs targeting DNA-PKcs, TAF7, or KU80 were cloned into pSpCas9 (BB)−2A-Puro (PX459) v2.0, which was a gift from Feng Zhang (Addgene plasmid # 62988) as described in a previous study61. Sequences are listed in Supplementary Table 2. The sequenced CRISPR/Cas9 plasmids were transfected into cells using PolyJet™ DNA transfection reagent (SignaGen, SL100688), and puromycin (2 μg mL) was used to select positive cells prior to clonal expansion. Cells transfected with PX459 were used as a negative control. For DNA-PKcs or TAF7 overexpression in cells, human DNA-PKcs plasmids were obtained from Addgene (Addgene plasmid # 83317), and FLAG-tagged human TAF7 plasmids were constructed in this study. All plasmids were transfected into cells using PolyJet. To knock down RPTOR expression in HK2 cells, short interfering RNA (siRNA) of RPTOR was transfected with Lipofectamine2000 (Thermofisher). Cells were treated with human recombinant TGF-β1 (100-B-010-CF, R&D Systems) in serum-free medium for 24 h or the indicated time.
## Primary renal epithelial cell isolation
Primary kidney epithelial cells were isolated from DNA-PKcs−/− mice or WT littermates as previously reported13. Briefly, male mice (3 to 5 weeks old) were euthanized, and the kidneys were immediately collected and placed in cold HBSS with $1\%$ penicillin and streptomycin. Then, the kidneys were minced into pieces of approximately 1 mm3 and digested in 5 ml HBSS containing 2 mg/mL collagenase I for 30 min at 37 °C. The supernatants were strained through a 100 μm nylon mesh and then centrifuged for 10 min at 900 g and 4 °C. The primary cells were cultured with RPMI 1640 supplemented with $10\%$ FBS, 20 ng/mL EGF (Sigma, St. Louis, MO) and 100 units/mL penicillin and 100 μg/mL streptomycin maintained at 37 °C and $5\%$ CO2 in a humidified incubator. Cells were used after 7 days of culture.
## Western blot analysis
Total proteins from kidneys or cultured cells were extracted in RIPA lysis buffer (Beyotime, A0181, China) containing 1× protease inhibitor cocktail (Roche, 04693132001) for 30 min on ice. The lysates were collected after centrifugation at 13,800 g for 15 min at 4 °C. Protein concentrations were determined using the BCA Protein Assay Kit (Beyotime, P0012), and equal total protein (30–50 μg) of each sample was analyzed using a standard western blot assay. Briefly, protein samples were separated on 4–$20\%$ polyacrylamide separating gels, and separated proteins were electroblotted onto PVDF membrane. The membranes were blocked in $5\%$ nonfat milk for 1 h at RT (room temperature) followed by incubation with specific primary antibodies (1:1000 dilution) overnight at 4 °C. The next day, membranes were incubated with peroxidase-conjugated goat anti-rabbit (Beyotime; A0208, 1:1000 dilution) or anti-mouse (Beyotime; A0216, 1:1000 dilution) at RT for 1 h. The bands were visualized using an enhanced chemiluminescence detection system (Bio–Rad, Hercules, CA, USA). Protein band densitometry analysis was performed using ImageJ (Wayne Rasband National Institutes of Health, USA), and relative protein expression levels were normalized to GAPDH. Phos-tag SDS-PAGE was performed as previously reported62,63. Briefly, 100 μM Phosbind (Apexbio) was added to $8\%$ (w/v) polyacrylamide separating gels before polymerization, according to the manufacturer’s instruction, to separate phosphorylated isoforms of TAF7 (all the antibodies, reagents, and their dilution are listed in Supplementary Table 3).
## Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted from cultured cells or kidney tissues using TRIzol reagent (TAKARA, Dalian, China; 9108). Subsequently, total RNA (1 μg) was reverse-transcribed to cDNA using a reverse transcriptase M-MLV kit (TAKARA, 2641A). Quantitative real-time PCR amplification (qRT–PCR) was performed using SYBR Green master mix (Vazyme, Nanjing, China; q111-$\frac{02}{03}$) in a 96-well QuantStudio 3 Real-time PCR System (Applied Biosystems, Foster City, CA, USA). 2−ΔΔCt method was used to calculate relative expression levels of messenger RNA (mRNA) as previously study described64. Relative expression levels of the target genes were normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) levels. Primer sequences (listed in Supplementary Table 2) were designed and synthesized by Tsingke (Nanjing, China).
## Luciferase reporter assay
The promoter sequences of human DNA-PKcs and Raptor were amplified and cloned into the pGL3 basic vector (Promega Corporation) using a ClonExpress Ultra One Step Cloning Kit (Vazyme, China, C115-01). The primer sequences are listed in Supplementary Table 2. Raptor luciferase reporter plasmids, PRL (Renilla luciferase) and two mutants at position 213 of TAF7 (TAF7-WT, TAF7-S213D, and TAF7-S213A, respectively) plasmids were co-transfected into HK-2 cells with PolyJet™ DNA transfection reagent. After transfection for 24 h, cells were treated with or without TGF-β1 (5 ng/ml) for another 24 h. The samples were subsequently harvested, and luciferase activity was measured using a Dual-Luciferase Reporter Assay System (Promega Corporation). Relative luciferase activity was normalized to renilla luciferase activity of the sample. To analyze transcriptional activation of DNA-PKcs by SMAD2, DNA-PKcs promoter plasmids and human SMAD2 plasmids (GeneCopoeia) were co-transfected into HEK293T cells for Dual-Luciferase Reporter Assay analysis.
## ChIP assay
ChIP assay was performed by using a SimpleChIP® Enzymatic Chromatin IP Kit (9003, CST) according to the manufacturer’s instruction. Briefly, HEK293T cells were transfected with FLAG-tagged TAF7 for 24 h and cross-linked with $37\%$ formaldehyde solution for 10 min at RT. Then, the chromatin extract was digested with micrococcal nuclease and incubated with anti-FLAG ChIP antibody (CST) overnight at 4 °C. The sequence containing the TAF7-binding site in the promoter of human RPTOR was amplified by PCR using ChIP products as template, and the relative signal was normalized to the level of input (total chromatin extract) by using the same primers (Supplementary Table 2) targeting RPTOR promoter. Histone H3 antibody was used as a positive control and rabbit IgG isotype as a negative control.
## Immunoprecipitation
The immunoprecipitation assay was performed as previously described65. Briefly, after human DNA-PKcs plasmids and TAF7-FLAG plasmids were cotransfected into HK-2 cells using PolyJet™ DNA transfection reagent for 36 h, the cell lysates were prepared and incubated with the indicated antibodies overnight at 4 °C with gentle rotation, followed by incubation with protein A&G beads (Santa Cruz) for 2 h with gentle rotation. The beads were washed with cold 1× lysis buffer for three times, and the immunoprecipitation complexes were collected by centrifugation and subjected to Western blot analysis.
## Measurement of ECAR using an XF96 Flux Analyzer
The extracellular acidification rate (ECAR) of the cultured cells was analyzed using a Seahorse XF96 Extracellular Flux Analyzer (Seahorse Bioscience, Copenhagen, Denmark). Briefly, cells were seeded into XF96 Cell Culture Microplates (Seahorse Bioscience) at a density of 5000 cells per well. Twenty-four hours later, the cells were treated with or without NU7441 (0.1 μM) and stimulated with TGF-β1 (5 ng/ml) for 24 h in serum-free medium. Real-time ECAR was analyzed as follows: basal ECAR was recorded for 16 min, followed by sequential injections with glucose (10 mM), oligomycin (5 μg/ml), and 2-DG (50 mM).
## Extracellular lactate measurement
Briefly, cells were seeded into 6-well plates until $70\%$ confluence, treated with or without NU7441 (0.1 μM) and stimulated with TGF-β1 (5 ng/ml) for 24 h in serum-free medium. Extracellular levels of lactate were measured using a lactate assay kit (E-BC-K044-M, Elabscience, China) according to the manufacturer’s instructions.
## Histological examination of kidney tissue
Kidney tissues were fixed in $4\%$ paraformaldehyde (PFA) for 48 h at RT. Kidney sections (4 μm thick) were prepared for periodic acid-Schiff (PAS) staining and Masson’s trichrome staining, kidney sections (6 μm thick) for Sirius red staining. PAS, Masson, and Sirius red staining were performed following standard protocols as previously described8. The kidney injury index was evaluated by calculating the percentage of damaged renal tubules that exhibited cell lysis or loss of the brush border via PAS staining. The pathological damage of kidneys was scored from 0 to 4: 0, normal; 1, <$25\%$ damaged renal tubules; 2, 25–$50\%$ damaged renal tubules; 3, 50–$75\%$ damaged renal tubules; 4, >$75\%$ of damaged renal tubules66. The PAS-stained images of each sample were captured using an Olympus BX51 microscope (Olympus, Center Valley, PA), and three random visual fields of each sample were selected and quantitated. For analysis of the fibrotic area, Masson staining images were captured using an Olympus BX51 microscope, and quantitative evaluation was performed using ImageJ (Wayne Rasband National Institutes of Health, USA). The collagen-stained area was calculated as a percentage of the total area, and at least five randomly chosen kidney cortex images (×400) were examined from each mouse.
## Immunohistochemistry (IHC) and immunofluorescence (IF) staining
IHC staining was performed on kidney paraffin sections as previously described67. Briefly, 4 μm paraffin-embedded kidney sections on slides were deparaffinized, followed by antigen retrieval and rehydration. Then, the sections were blocked in $3\%$ H2O2 for 15 min, washed with TBST buffer, and blocked in $10\%$ normal goat serum for 1 h before incubation with the indicated primary rabbit antibody (listed in Supplementary Table 3) overnight at 4 °C. After washing three times with TBST buffer, the sections were incubated with SignalStain® Boost IHC Detection Reagent (CST, 8114) for 1 h. Finally, the peroxidase conjugates were stained using a DAB kit (ZLI-9018, Zsbio, China), and images were captured with an Olympus BX51 microscope. The relative positive areas of IHC images were analyzed using ImageJ. For immunofluorescence staining performed on paraffin-embedded kidney sections, the deparaffinization, antigen retrieval and rehydration steps were the same as for IHC staining. The sections were incubated with the indicated primary rabbit antibody overnight at 4 °C. The next day, goat anti-rabbit IgG (H + L) cross-adsorbed secondary antibody, Alexa Fluor 488 (Thermo Fisher Scientific, A-11008), was used as a secondary antibody according to the manufacturer’s instructions. Nuclei were counterstained with DAPI (Beyotime, P0131). Images were obtained using an LSM710 confocal microscope (CarlZeiss, Germany). For IF staining, cells were seeded onto polylysine-coated glasses. After treatment, the cells were fixed in $4\%$ paraformaldehyde for 10 min, permeabilized and blocked with $0.1\%$ TritonTM X-100 dissolved in $1\%$ BSA for 1 h, and the remaining steps were the same as IF staining of paraffin-embedded kidney sections. Quantification was performed using ImageJ.
## RNA sequencing analysis
RNA samples were collected from TAF7 knockout (TAF7−/−) or control mPTCs treated with or without TGFβ1 (5 ng/ml) for 24 h. RNA isolation, library construction, and sequencing were performed by BGI (Beijing Genomic Institution, www.genomics.org.cn, BGI) using a BGISEQ-500 RNA-seq platform. Through the BGI bioinformatics platform, differentially expressed genes among the four groups, NC (negative control), NC + TGFβ1, TAF7−/− and TAF7−/− + TGFβ1, were analyzed using several bioinformatics methods, including gene set enrichment analysis (GSEA), KEGG pathway enrichment analysis and heatmap analysis.
## Phosphoproteomics analysis
Phosphoproteomics analysis was performed by PTM Biolabs (PTM Biolabs, Hangzhou, China). Briefly, kidney protein samples from DNA-PKcs−/− and WT mice were prepared and ground in liquid nitrogen into powder and then prepared in protein solution for trypsin digestion. A bio-material-based method was used for phosphopeptide enrichment. Briefly, peptide mixtures were first incubated with IMAC microspheres suspension in loading buffer ($50\%$ acetonitrile/$6\%$ trifluoroacetic acid) with shaking gently. The IMAC microspheres with enriched phosphopeptides were collected by centrifugation, and the supernatant was discarded. Next, the IMAC microspheres were sequentially washed with $50\%$ acetonitrile/$6\%$ trifluoroacetic acid and $30\%$ acetonitrile / $0.1\%$ trifluoroacetic acid to remove nonspecifically adsorbed peptides. At last, elution buffer containing $10\%$ NH4OH was added to elute the enriched phosphopeptides from the IMAC microspheres with vibration. The enriched phosphopeptides supernatant was collected and lyophilized for LC-MS/MS analysis. The phosphopeptides were loaded to a nitrogen solubility index (NSI) source followed by tandem mass spectrometry (MS/MS) in Q Exactive (Thermo Fisher Scientific, San Jose, CA, USA) coupled to an online ultra-performance liquid chromatography (UPLC) system. The MS/MS data were analyzed using the MaxQuant search engine (v.1.5.2.8), carbamidomethylated cysteine residues (C, + 57.0340 Da) as a fixed modification, Oxidation (M, + 15.9949 Da), phosphorylated serine (S), threonine (T) and tyrosine (Y) (+79.9663 Da) and Acetyl (Protein N-term, +42.011 Da) as variable modifications. FDR was adjusted to <$1\%$, and the minimum score for modified peptides was set >40. Bioinformatics methods, including GO Annotation, Motif Analysis, Functional Enrichment, Enrichment-based Clustering and Protein–protein Interaction Network, were used to analyze differentially expressed modified proteins between DNA-PKcs−/− and WT mice.
## In vitro kinase assay and mass spectrometry analysis
To examine whether TAF7 could be phosphorylated by DNA-PKcs directly, an in vitro kinase assay was performed. First, human DNA-PKcs was purified from TGFβ1 treated HK2 cells by immunoprecipitation with a DNA-PKcs antibody (Abcam) and IgG was used as a negative control. The purified proteins and human GST-TAF7 (purchased from Proteintech) were incubated in kinase assay buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 10 mM MgCl2, and 1 mM MnCl2) supplemented with 200 μM ATP in 50 μl reactions for 30 min at 30 °C. Then, phosphorylation of TAF7 was analyzed by mass spectrometry (MS), which was performed by BIOTREE (Shanghai, China). MS analysis was performed as described in a previous study68. Proteome Discoverer (PD) software and the built-in Sequest HT search engine were used to process raw MS files. MS spectra lists were searched against their UniProt FASTA databases (Homo sapiens-9606-2021-8. fasta), carbamidomethylated cysteine residues (C, + 57.0340 Da) as a fixed modification, Oxidation (M, + 15.9949 Da), phosphorylated serine (S), threonine (T) and tyrosine (Y) (+79.9663 Da) and Acetyl (Protein N-term, +42.011 Da) as variable modifications of peptides.
## Metabolomics analysis
Metabolomics analysis of kidney tissues was performed by BioNovogene (Suzhou, China). Briefly, kidney tissues were prepared for LC–MS detection following a standard method by BioNovogene. Chromatographic separation was performed in a thermo vanquish system equipped with an ACQUITY UPLC® HSS T3. The MS experiments were executed on a Thermo Q Exactive Focus mass spectrometer with spray voltages of 3.5 kV and −2.5 kV in positive and negative modes, respectively. Differentially abundant metabolites were analyzed using bioinformatics methods, and all procedures were performed by BioNovogene (Suzhou, China).
## Statistical analysis
Data are shown as the mean ± standard deviation (SD) and were analyzed using GraphPad Prism 9.0. Statistical significance between two groups was determined using unpaired Student’s t test. When more than two groups were compared, one-way or two-way ANOVA followed by Tukey’s or Šídák’s multiple comparison test was used to analyze differences between two groups of interest. A P value less than 0.05 was considered statistically significant.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37043-5.
## Source data
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## Peer review information
Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
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---
title: The structure of the Canadian packaged food and non-alcoholic beverage manufacturing
and grocery retailing sectors through a public health lens
authors:
- Alexa Gaucher-Holm
- Benjamin Wood
- Gary Sacks
- Lana Vanderlee
journal: Globalization and Health
year: 2023
pmcid: PMC10008568
doi: 10.1186/s12992-023-00917-w
license: CC BY 4.0
---
# The structure of the Canadian packaged food and non-alcoholic beverage manufacturing and grocery retailing sectors through a public health lens
## Abstract
### Background
Corporate power has been recognized as an important influence on food environments and population health more broadly. Understanding the structure of national food and beverage markets can provide important insight into the power held by leading corporations. This study aimed to descriptively analyze the structure of the Canadian food and beverage manufacturing and grocery retailing sectors as of $\frac{2020}{21.}$
### Methods
Packaged food manufacturers, non-alcoholic beverage manufacturers and grocery retailers with ≥ $1\%$ market share in $\frac{2020}{21}$ in Canada as per Euromonitor International were identified and characterized. Proportion of market share held by public vs private, multinational vs national, and foreign multinational companies was assessed for the 3 sectors. The concentration of 14 packaged food, 8 non-alcoholic beverage and 5 grocery retailing markets was assessed using the Herfindahl–Hirschman Index (HHI) and the four firm concentration ratio (CR4) (HHI > 1800 and CR4 > 60 suggest high market concentration). Company ownership structure was also assessed, including common ownership of public companies by three of the largest global asset managers using data from Refinitiv Eikon, a financial market database.
### Results
The Canadian non-alcoholic beverage manufacturing sector, and, to a lesser extent, the packaged food manufacturing sector were dominated by foreign multinational companies, in contrast with the grocery retailing sector which was dominated by national companies. Market concentration varied across sectors and markets but was substantially greater within the retailing (median CR4 = 84; median HHI = 2405) and non-alcoholic beverage sectors (median CR4 = 72; median HHI = 1995) compared to the packaged food sector (median CR4 = 51; median HHI = 932). There was considerable evidence of common ownership across sectors. Overall, the Vanguard Group Inc owned at least $1\%$ of shares in $95\%$ of publicly listed companies, Blackrock Institutional Trust Company $71\%$, and State Street Global Advisors (US) $43\%$.
### Conclusions
The Canadian packaged food and non-alcoholic beverage manufacturing and grocery retailing sectors include several consolidated markets, with a high degree of common ownership by major investors. Findings suggest that a small number of large corporations, particularly in the retailing sector, have extensive power to influence Canadian food environments; their policies and practices warrant substantial attention as part of efforts to improve population diets in Canada.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12992-023-00917-w.
## Introduction
The global rise in obesity and associated non-communicable diseases (NCDs) has coincided with important shifts in global food systems and environments, including changes to the food supply, distribution systems and marketing pathways [1]. Current food systems are unsustainable for both human and planetary health, and their transformation will require substantial change from governments, food companies, and other stakeholders including institutional investors and civil society groups [2, 3].
Food and beverage industries have an important influence on the healthfulness of food environments through the production, pricing, and marketing of their products, as well as their corporate political practices. A substantial amount of public health research has demonstrated the use of corporate strategies, such as the pervasive use of marketing tactics targeting children and adolescents that undermine health [4, 5] and the intense political lobbying used by corporations to strategically shape food policies in their favor [6–8]. More recently, the dominance of major food and beverage companies within global markets has garnered increasing interest from public health scholars, with greater attention being paid to ways in which excessive market power of large food companies may influence the characteristics of food environments and population health, through practices such as mergers and acquisitions, licensing arrangements, and research and development practices [9–11].
It is increasingly recognized that concentrated market power in local and global food supply chains has the potential to undermine the food systems transformations necessary to address current rates of NCDs [12, 13]. The International Panel of Experts on Sustainable Food Systems recently stated that “Dominant firms have become too big to feed humanity sustainably, too big to operate on equitable terms with other food system actors, and too big to drive the types of innovation we need’’ [14]. Indeed, several scholars have noted that the creation of healthy and sustainable food systems will require increased attention to complex challenges such as the highly integrated nature of a food system increasingly governed by multinational corporations and international investors, the rise in multi-stakeholder partnerships and market-oriented forms of governance, as well as the intensification of market concentration and power along food chains [14–16].
Corporate market power can be assessed through analyses of three interrelated concepts of market: 1) structure; 2) conduct; and 3) performance. The structure of an industry or market (e.g., the concentration of a market and degree of common shareholder ownership) can both influence and be influenced by the conduct of firms (i.e., firm behavior and strategy such as the pursuit of mergers and acquisitions), which can in turn influence or be influenced by industry or firm performance (e.g., profits and shareholder returns) [10]. Market structure analyses are an integral step in market power analyses and can provide important insight into the power held by leading corporations; however, there has been only limited analysis of market structure from a public health perspective to date. Most relevant studies pertaining to market structure have focused on supranational markets or analyses within the agricultural sector only [14, 16–19].
Research has demonstrated that Canadian food environments could be more conducive to healthy dietary patterns necessary for NCD prevention. For example, diets of higher nutritional quality that meet national dietary guidelines may be more expensive compared to less healthy counterparts [20, 21]. Moreover, the promotion of unhealthy food and beverages to children is widespread in the country [22] and industry compliance with national sodium reduction targets has been limited [23]. Better understanding structural factors that may facilitate and/or hinder positive changes to Canadian food environments is necessary.
This study aimed to descriptively analyze the structure of the Canadian food and beverage manufacturing and grocery retailing sectors as of $\frac{2020}{21.}$ The objectives were to identify the leading food and beverage manufacturing and grocery retailing companies in Canada, and understand the structure and competitive landscape of the markets in which these firms operate using a public health lens.
## Methods
This paper drew upon a theoretical framework developed to understand corporate market power from a public health perspective using the structure-conduct-performance model [10], and an applied market structure analysis which aimed to compare differences and similarities in market structure across European countries and potential implications for food environment policy [18]. The current study adapted the methods applied in Europe [18] for a national-level market structure analysis.
## Assessing the size of relevant markets
The most recent market size data (as off-trade/retail value retail selling price (RSP), which represents sales from retail settings excluding the sales tax) were obtained from Passport by Euromonitor International [24] for packaged food and non-alcoholic beverage manufacturing and modern grocery retailing (hereafter referred to as ‘grocery retailing’) sectors, including disaggregated data for 16 packaged food product markets (‘confectionery’, ‘ice cream and frozen desserts’, ‘savory snacks’, ‘sweet biscuits, snack bars and fruit snacks’, ‘ready meals’, ‘sauces, dressings and condiments’, ‘soups’, ‘sweet spreads’, ‘dairy’, ‘baked goods’, ‘breakfast cereals’, ‘processed fruits and vegetables’, ‘processed meat, seafood and alternatives’, ‘rice, pasta and noodles’, ‘edible oils’, and ‘baby foods’), 8 non-alcoholic beverage product markets (‘carbonates’, ‘fruit and vegetable juice’, ‘bottled water’, ‘concentrates’, ‘ready-to-drink (RTD) tea’, ‘RTD coffee’), and 5 types of grocery retailers (‘hypermarkets’, ‘supermarkets’, ‘discounters’, ‘forecourt retailers’, ‘convenience stores’) from 2012 to 2021 (all historical data available for download on Passport were included).
The relative size of each market was calculated in terms of the percent contribution of each market to their sector as of 2021:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Relative\ market\ size}= \frac{\mathrm{value\ of\ a\ market}}{\mathrm{value\ of\ a\ sector}}$$\end{document}Relativemarketsize=valueofamarketvalueofasector For example, the relative size of the ‘carbonates’ market would be calculated as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Relative\ size\ of\ 'carbonates'\ { market}}= \frac{\mathrm{Off}-\mathrm{trade\ value\ of\ 'carbonates'\ market}}{\mathrm{Off}-\mathrm{trade\ value\ of\ non}-\mathrm{alcoholic\ beverage\ sector}}$$\end{document}Relativesizeof′carbonates′market=Off-tradevalueof′carbonates′marketOff-tradevalueofnon-alcoholicbeveragesector The change in relative market size since 2012 was calculated to assess changes over time as: relative market size in 2021 – relative market size in 2012.
As aggregated data were only available for the packaged food sector from 2016–2020, the retail value of the packaged food sector was calculated as the sum of the retail values of all ($$n = 16$$) packaged food product markets. Analyses were conducted for all markets described above with the exception of ‘edible oils’, and ‘baby foods’, the two smallest packaged food product markets, each accounting for less than $1\%$ of sales within the packaged food sector.
## Identifying and describing relevant companies
The most recent national (i.e., ‘national brand owner’) and global (i.e., ‘global brand owner’) company share data were obtained from Passport by Euromonitor International [24] for the sectors and markets described above, including historical data going back 10 years (2012–2021). National brand owners (i.e., producers or distributors of a brand at the national level [25]) with ≥ $1\%$ of shares within the packaged food manufacturing, non-alcoholic beverage manufacturing and/or grocery retailing sector(s) according to the most recent data ($\frac{2020}{21}$) were first identified as the largest contributors to Canadian sectors and markets. Company characteristics were collated from Refinitiv Eikon, a financial market database [26], and supplemented with targeted online searches on company websites, and/or information from MarketLine, a commercial intelligence database which profiles companies and markets [27]. Characteristics included company type (i.e., ‘public’ or ‘private’ company), headquarter location (i.e., ‘Canada’ or ‘foreign’ headquarters), as well as the name, type, and headquarter location of the associated parent company if applicable (e.g., if the national brand owner was a subsidiary of another company). Companies were also identified as being either ‘multinational’ companies, if they or their parent company had operations (e.g., production or retail facilities) in > 1 country, or ‘national’ companies.
Descriptive statistics examined the proportion of the market share held by public (vs private), multinational (vs national) and foreign multinational companies by sector.
## Measuring market concentration
Market concentration was then assessed. Market concentration, referring to the extent to which market shares are concentrated between firms active in the market in question, is often used as an indicator for the level of competition within a market, such that higher concentration suggests lower levels of competition within a market [28, 29]. The 4-firm concentration ratio (CR4) and the Herfindahl Hirschman Index (HHI) are commonly used market concentration metrics [30]. The CR4 is measured by adding the market shares of the top 4 firms in a market [31]. A value below 40 can be considered as being suggestive of a competitive market, whereas a value above 60 suggestive of a market dominated by either one or a few firms (referred to as a monopolistic or oligopolistic market) [30]. The HHI is calculated by summing the square of the market shares of all companies within a market [32], and therefore takes into account the distribution of market shares [31]. Various HHI thresholds have been developed to classify the degree of concentration in a market. According to the US Department of Justice’s Horizontal Merger Guidelines, an HHI value above 2500 would be suggestive of a highly concentrated market, an HHI between 1500 and 2500 a moderately concentrated market, and an HHI below 1500 an unconcentrated market [33]. The European Central Bank uses lower thresholds, considering values below 1000 to be indicative of unconcentrated markets, while those above 1800 to be indicative of highly concentrated markets [34]. Thresholds of 1000 and 2000 have also been used to assess the concentration of the European food and beverage market [18], values which were based on the European Union’s merger regulations (2004/C $\frac{31}{03}$) [35]. In Canada, the Competition Bureau’s Merger Enforcement Guidelines do not establish HHI thresholds, however, a post-merger CR4 ≥ 65 is generally challenged in light of the potential threat to competition [36]. Market concentration is a more meaningful indicator when applied to a specific market of substitutable goods (e.g., ‘breakfast cereals’ rather than all ‘packaged foods’) within constrained geographical boundaries; market concentration levels may be underestimated when applied too broadly.
Market concentration was assessed for sectors and markets described above using both ‘national brand owner’ and ‘global brand owner’ market share data over 10 years (2012–2021). Metrics included: 1) the number of brand owners with ≥ $1\%$ market share, 2) the 4-firm concentration ratio (CR4), and 3) the Herfindahl Hirschman Index (HHI) (using data for companies with ≥ $1\%$ market share). Integrated thresholds based on aforementioned values were used to interpret HHI values, such that an HHI > 2500 was deemed suggestive of very high market concentration, 1800–2499 high concentration, 1500–1799 moderate concentration, 1000–1500 moderate-low concentration, and < 1000 low concentration.
## Assessing company ownership
Company shareholder ownership was subsequently assessed. Ownership data for all identified publicly listed companies were downloaded from Refinitiv Eikon in March 2022 [26]. If ownership data were not available for the national brand owner (i.e., if the national brand owner was privately owned by a publicly listed parent company), ownership data were downloaded for the identified publicly listed parent company.
First, the top 3 investors (i.e., with the largest percent shareholder ownership) for each company were identified, including their name and percent shareholder ownership, and the total shares owned by the top 3 investors were assessed for each company, to capture the diversity of investors, and range in percent shareholder ownership held by top investors. Overall mean and median shares held by the top 3 investors were assessed.
Then, ownership by three of the largest global asset managers (i.e., Blackrock, The Vanguard Group Inc, and State Street Global Advisors [37, 38]) was assessed. Percent shareholder ownership by Blackrock Institutional Trust Company (a subsidiary of Blackrock), The Vanguard Group Inc and State Street Global Advisors (US) was extracted if the asset manager owned ≥ $1\%$ of the public company’s shares.
The proportion of all publicly listed companies in which each of the three aforementioned asset manager owned ≥ $1\%$ was calculated. Ownership networks were also mapped for each sector, using a scheme adapted from research conducted within the US seed industry [39], including shareholder ownership by Blackrock Institutional Trust Company, The Vanguard Group Inc and State Street Global Advisors (US).
## Size of relevant markets
Relative market size is presented in Table 1. As of 2021, the largest packaged food market was the 'dairy' product market, followed by the 'baked goods', 'processed meat, seafood and alternatives' and 'ready meals' product markets. The largest non-alcoholic beverage market was the 'fruit and vegetable juice' product market, followed by the 'carbonates' and 'bottled water' product markets. Within the grocery retailing sector, 'supermarkets', followed by 'hypermarkets' and 'discounters' constituted the largest markets. Table 1Size of packaged food and non-alcoholic beverage manufacturing and grocery retailing markets relative to their sectorMarketRelative size of market as of 2021(change since 2012) (%)Packaged food manufacturingaDairy23 [-1]Baked goods13 [0]Processed meats, seafood and alternatives12 [1]Ready meals11 [1]Savory snacks8 [1]Confectionery7 [0]Sauces, dressings and condiments6 [0]Sweet biscuits, snack bars and fruit snacks4 [0]Processed fruits and vegetables4 [0]Rice, pasta and noodles3 [0]Ice cream and frozen desserts3 [0]Breakfast cereals2 [-1]Sweet spreads1 [0]Soups1 [0]Non-alcoholic beverage manufacturingFruit and vegetable juice32 [-6]Carbonates23 [-5]Bottled water22 [3]RTD tea9 [4]Energy drinks7 [2]Sports drinks5 [0]Concentrates2 [0]RTD coffee1 [1]Grocery retailingSupermarkets43 [-5]Hypermarkets29 [5]Discounters23 [0]Forecourt retailers4 [0]Convenience1 [-1]aThe ‘Edible oils’ and ‘Baby foods’ packaged food product markets were excluded from analyses
## Leading food and beverage companies
A total of 34 national brand owners were identified as having ≥ $1\%$ of shares in the packaged food manufacturing ($$n = 19$$), non-alcoholic beverage manufacturing ($$n = 13$$), and/or grocery retailing ($$n = 6$$) sector(s) (see Table 2). Of these, 2 firms (Loblaw and Sobeys) held ≥ $1\%$ of shares in all 3 sectors. National brand owners included in this analysis accounted for $49\%$ of shares within the packaged food manufacturing sector, $75\%$ of shares within the non-alcoholic beverage manufacturing sector, and $86\%$ of shares within the grocery retailing sector. Table 2Ownership structure of packaged food and non-alcoholic beverage manufacturers and grocery retailersNational brand owner with ≥ $1\%$ market share in CanadaSector (share [%])aParent companyOwnership statusHeadquarter locationCompany typeA. Lassonde IncB (5.7)Lassonde Industries IncPublicCanadaMultinationalSun-Rype Products LtdB (1.9)Agropur CooperativePF (3.8)-PrivateCanadaMultinationalAlimentation Couche-Tard IncR (1.8)-PublicCanadaMultinationalBlueTriton Brands IncbB (7.3)-PrivateForeignMultinationalCadbury Adams Canada InccPF (1.2)Mondelez International IncPublicForeignMultinationalMondelez Canada IncPF (1.1)Campbell Company of CanadadB (1.0)Campbell Soup CoPublicForeignMultinationalCanada Bread Co (Bimbo Canada)PF (1.4)Grupo Bimbo SAB de CVPublicForeignMultinationalCanada Dry Motts IncB (5.5)Keurig Dr Pepper IncPublicForeignMultinationalSnapple Beverage CorpB (1.2)Catelli Foods CorpePF (1.0)Barilla GroupPrivateForeignMultinationalCoca-Cola LtdB (13.4)Coca Cola CoPublicForeignMultinationalMinute Maid Co of CanadaB (7.9)Danone Canada IncPF (1.5)Danone SAPublicForeignMultinationalFrito-Lay CanadafPF (3.4)PepsiCo IncPublicForeignMultinationalPepsiCo Beverages CanadafB (21.1)General Mills Canada CorpPF (2.4)General Mills IncPublicForeignMultinationalGeorge Weston LtdPF (1.5)-PublicCanadaMultinationalKellogg Canada IncPF (1.5)Kellogg CoPublicForeignMultinationalKraft Heinz Canada ULCPF (4.4)Kraft Heinz CoPublicForeignMultinationalLactalis Canada IncPF (3.7)Groupe LactalisPrivateForeignMultinationalLoblaw Cos LtdgPF (6.1), B (4.6), R (24.6)George WestonfPublicCanadaNationalMaple Leaf Foods IncPF (1.6)Maple Leaf Foods IncPublicCanadaMultinationalSchneider CorphPF (1.8)Metro IncR (12.2)-PublicCanadaNationalNestlé Canada IncPF (3.8)Nestlé SAPublicForeignMultinationalOcean Spray Cranberries IncB (1.4)-PrivateForeignMultinationalOverwaitea Food GroupR (2.8)Jim Pattison Group IncPrivateCanadaMultinationalRed Bull Canada LtdB (2.5)Red Bull GmbHPrivateForeignMultinationalSaputo IncPF (5.1)-PublicCanadaMultinationalSobeys IncPF (2.1), B (1.4), R (24.1)Empire Co LtdPublicCanadaNationalUnilever Canada IncPF (1.2)Unilever PLCPublicForeignMultinationalWal-Mart Canada IncR (20.6)Walmart IncPublicForeignMultinationalaSource: © Euromonitor International [24]. Abbreviations: B Non-alcoholic beverage, PF Packaged Food, R Grocery retailbNestlé Waters North America (formerly owned by Nestlé SA) changed its name to BlueTriton Brands after its acquisition by One Rock Capital Partners, LLC and Metropoulos & Co in March 2021 [40]cData for Mondelez Canada Inc and Cadbury Adams Canada are reported separately on Passport. Cadbury was acquired by Kraft Foods in 2010, and the plant became part of the Mondelez International group in 2012 [41]dData is reported for Campbell Soup Co on Passport; The Campbell Company of *Canada is* the Canadian subsidiary of Campbell Soup CoeIn 2021, Barilla acquired the Catelli dry pasta business, including the Catelli, Lancia, and Splendor brands and the production facilities in Montreal, Quebec [42]fPepsiCo *Canada is* composed of two business units: PepsiCo Beverages Canada and PepsiCo Foods Canada (which includes Frito Lay Canada) [43]gLoblaw Cos *Ltd is* a publicly owned Canadian company; information about the company is provided independently of its affiliation with George Weston Ltd. Nonetheless, Loblaw Cos *Ltd is* an operating segment of George Weston Ltd [44]hSchneider Corp was acquired by Maple Leaf Foods Inc in 2004 [45]. Data for Schneider and Maple Leaf are reported separately on Passport A total of 29 parent companies accounted for all national brand owners included in this analysis. Most parent companies were publicly listed; $83\%$ of sampled shares within the packaged food manufacturing sector, $85\%$ of sampled shares within the non-alcoholic beverage manufacturing sector, and $97\%$ of the sampled shares within the grocery retailing sector were accounted for by national brand owners which were or were owned by publicly listed parent companies. Multinational companies accounted for $83\%$ of the sampled shares within the packaged food manufacturing sector, $92\%$ of the shares within the non-alcoholic beverage manufacturing sector, and $29\%$ of the shares within the grocery retailing sector. Foreign multinational companies accounted for $55\%$ of the sampled shares held by packaged food manufacturers, $82\%$ of the shares held by non-alcoholic beverage manufacturers, and $24\%$ of the shares held by grocery retailers included in this analysis.
## Market concentration
Table 3 summarizes the level of concentration (CR4 and HHI) of Canadian packaged food and non-alcoholic beverage manufacturing and grocery retailing markets using national brand owner data as of 2021. Market concentration metrics over 10 years stemming from both national brand owner and global brand owner data (to support international comparisons) are presented in Supplementary Tables A1, A2 and A3.Table 3Concentration of Canadian packaged food and non-alcoholic beverage manufacturing and grocery retailing markets as of 2021, and change in value since 2012MarketFour-firm concentration ratio as of 2021(change since 2012)Herfindahl–Hirschman Index as of 2021(change since 2012)Packaged food manufacturingSoups81 [-2]3700 [-104]Ice cream and frozen desserts77 [-2]2041 [-409]Breakfast cereals77 [0]2005 [-163]Dairy63 [-1]1205 [2]Savory snacks52 [-1]1467 [-66]Rice, pasta and noodles52 [7]1017 [286]Sweet spreads52 [1]869 [21]Processed fruits and vegetables50 [5]721 [119]Sauces, dressings and condiments49 [6]995 [373]Confectionery48 [1]719 [47]Baked goods42 [-1]473 [-15]Processed meats, seafood and alternatives40 [-3]511 [-69]Sweet biscuits, snack bars and fruit snacks38 [-6]463 [-151]Ready meals34 [-2]458 [-48]Non-alcoholic beverage manufacturingSports drinks99 [1]a6483 [-88]Energy drinks84 [3]2440 [-79]Carbonates80 [1]2067 [-117]Concentrates74 [-6]2262 [-380]RTD tea70 [-8]1923 [-632]RTD coffee62 [-20]a1462 [-5279]Bottled water62 [-3]1364 [-137]Fruit and vegetable juice57 [4]999 [112]Grocery retailingHypermarkets100 [0]b5959 [747]Supermarkets87 [1]3219 [1018]Discounters84 [5]2405 [-249]Forecourt retailers62 [5]1728 [286]Convenience53 [1]a1576 [304]aInterpret with caution; data available for < 4 companies 1 or more years between 2012 and 2021b < 4 companies held $100\%$ of the market The Canadian packaged food sector was comprised of product markets of various sizes, and with varying levels of concentration. Analyses within specific product markets revealed some moderately concentrated markets (CR4 > 40 and HHI > 1000) (i.e., ‘dairy’, ‘savory snacks’, and ‘rice, pasta and noodles’), and some highly concentrated (CR4 > 60 and HHI > 1800) markets (i.e., ‘soups’, ‘ice cream and frozen desserts’ and ‘breakfast cereals’), consistent with a recent analysis of the European single market, where ‘soups’, ‘ice cream and frozen desserts’, and ‘breakfast cereals’ were found to be the most concentrated packaged food product markets [18]. Within the non-alcoholic beverage sector, many product markets (i.e., ‘carbonates’, ‘concentrates’, ‘energy drinks’, ‘RTD tea’ and ‘sports drinks’) were highly concentrated which was similarly seen in Europe within the ‘carbonates’, ‘energy drinks’, ‘sports drinks’ and ‘RTD tea’ product markets, and distinctively within the ‘RTD coffee’ product market [18]. Overall, these findings suggest important (although perhaps unsurprising) similarities in the structure of Western food and beverage manufacturing markets.
Minor variations and fluctuations in market concentration metrics were noted over the past ten years within the packaged food sector. Certain changes can be explained by horizontal mergers and acquisitions. For instance, in 2015, an increase in the HHI value (from 595 to 938) for the ‘sauces, dressings and condiments’ product market was seen, likely as a result of the merger between the Heinz Company of Canada Ltd, and Kraft Canada. In the non-alcoholic beverage sector, a steep decrease was seen in the HHI values for the ‘RTD coffee’ product market over time. Distinctively, this drop in concentration may be explained by the emergence of companies offering products within this market; as of 2012, a single company held over $80\%$ of the shares in the market, which decreased substantially over the following 2 years, in tandem with rapid market growth [24].
In contrast with the packaged food and non-alcoholic beverage sectors, the grocery retailing sector was dominated by national companies (with the exception of Wal-Mart). Four competing firms (i.e., Sobeys Inc, Metro Inc, Loblaw Co Ltd, and Wal-Mart Canada Inc) largely dominated the sector (median CR4 = $84\%$). Although not amongst the top 4 companies within the grocery retailing sector, Alimentation Couche Tard Inc was a leading company within the ‘Convenience’ and ‘Forecourt retail’ markets, and the geographic access to its outlets was greatest among all leading grocery retailers in 2021 [46]. While providing a substantially smaller volume of sales, the convenience and forecourt retailing sectors are of relevance to public health given they may have a product selection of poorer nutritional quality, compared to that of larger retailers such as supermarkets [47].
Taken together, these data suggest high market concentration and a lack of competition in the grocery retailing sector in Canada, as has been identified in other countries [18, 48]. These structural characteristics of the Canadian retail sector likely indicates that a small set of companies have extensive market power in this sector. For example, due to the oligopolistic nature of the markets in which they operate, retailers may have substantial buyer power over suppliers (e.g., manufacturers), and seller power over consumers [49]. This market power is likely exacerbated because many of the retailers are also highly vertically integrated and produce and sell their own brands. Excessive retailer market power may have important public health implications as retailers are gatekeepers of modern food systems [48]. In line with this finding, the Canadian Competition Bureau has recently launched an investigation into competition within the grocery retailing sector in light of rising food prices within the country [50].
Strong nutrition-related policies and action amongst the small number of leading Canadian grocery retail companies, including, to a lesser extent, convenience and forecourt retailers, would likely have an impact on a large number of consumers given that $73\%$ of Canadian food expenditures are spent in retail settings [51]. Opportunities to improve healthfulness within retailers could include policies that address the promotion of ‘less healthy’ foods, the availability of and access to ‘healthier’ and ‘less healthy’ foods (e.g., at check-out points), as well as the nutrition information provided in stores (e.g., for ready-to-eat foods and own-brand products) and online [52]. Implementation of such policies could be conducive to making healthier choices easier for consumers in environments where they make the majority of their food purchases. Similarly, strong nutrition-related action, such as addressing nutrients of concern in leading products, by both retailers that produce and/or distribute own-brand products and leading manufacturers in product markets that are concentrated and/or generate important sales revenues, could potentially have significant public health implications.
## Market concentration within packaged food and non-alcoholic beverage manufacturing sectors
As of 2021, CR4 values ranged from 34 to 81 across packaged food product markets (median CR4 = 51), while HHI values ranged from 458 to 3700 (median HHI = 932). CR4 values were < $40\%$ (low concentration) for 2 of 14, 40–$60\%$ for 8 of 14 (moderate concentration) and > $60\%$ (high concentration) for 4 of 14 packaged food product markets. Overall, 8 of 14 packaged food product markets had an HHI < 1000 (low concentration) and 3 of 14 1000- < 1500 (low-moderate concentration), while 2 of 14 had an HHI > 1800–2500 (high concentration) and 1 of 14 > 2500 (very high concentration). Substantial fluctuations in HHI values over the past 10 years were observed for certain packaged food product markets, particularly for the ‘sauces, dressings and condiments’ (+ 373), ‘ice cream and frozen desserts’ [-409], and ‘rice, pasta and noodles’ (+ 286) product markets.
As of 2021, CR4 values ranged from 57 to 99 across non-alcoholic beverage product markets (median CR4 = 72), while HHI values ranged from 999 to 6483 (median HHI = 1995). CR4 values were > $60\%$ for all non-alcoholic beverage product markets except for the ‘fruit and vegetable juice’ market. Overall, 1 of 8 product markets resulted in an HHI < 1000, 2 of 8 1000- < 1500, 4 of 8 > 1800–2500, and 1 of 8 > 2500. The largest fluctuations in HHI values over the past 10 years were observed for the ‘RTD coffee’ [-5279], followed by the ‘RTD tea’ [-632] product markets.
## Market concentration within the grocery retailing sector
As of 2021, CR4 values ranged from 53 to 100 across grocery retailing markets (median CR4 = 84), while HHI values ranged from 1576 to 5959 (median HHI = 2405). CR4 values for and within the Canadian grocery retailing sector were all ≥ $60\%$, while HHI values were all > 1500, with 1 of 5 > 1800–2500 (i.e., ‘discounters’), and 2 of 5 > 2500 (i.e., ‘hypermarkets’, ‘supermarkets’). A substantial rise in HHI values was observed within the grocery retailing sector between 2012 and 2013, specifically for ‘supermarkets’ (see Supplementary Table A3).
## Shareholder ownership of publicly listed companies by any investor
The percent shareholder ownership of each publicly listed national brand owner (or their publicly listed parent company) by investors varied substantially (see Table 4); the percent share owned by the top investor varied between $4.09\%$ (i.e., BMO Asset Management Inc’s shareholder ownership of Sobeys) and $53.56\%$ (i.e., Willard Galen Garfield’s shareholder ownership of George Weston Ltd). Total shares of companies owned by the top 3 investors ranged from $10.56\%$ to $58.69\%$ of total shares. Median shareholder ownership by the top 3 investors (for each publicly listed company) totaled $26.43\%$, while the mean totaled $31.76\%$.Table 4Shareholder ownership of publicly listed packaged food manufacturers, non-alcoholic beverage manufacturers and grocery retailers in CanadaNational brand owner with ≥ $1\%$ market share in CanadaParent companyTop 3 investors (percent ownership [%]) aPercent ownership by 3 large asset managers (%) bThe Vanguard Group, IncBlackRock Institutional Trust CompanyState Street Global Advisors (US)A. Lassonde IncLassonde Industries IncQV Investors Inc. (14.54)Fidelity Management & Research Company LLC (7.30)Tweedy, Browne Company LLC (4.59)---Sun-Rype Products LtdAlimentation Couche-Tard Inc-Développements Orano, Inc. (9.97)D'Amours (Jacques) (5.7)Fortin (Richard) (3.13)2.011.17-Cadbury Adams Canada IncMondelez International IncThe Vanguard Group, Inc. (8.18)State Street Global Advisors (US) (4.61)BlackRock Institutional Trust Company, N.A. (4.31)8.184.314.61Mondelez Canada IncCampbell Company of CanadaCampbell Soup CoMalone (Mary Alice D) (17.66)Dorrance (Bennett) (14.89)The Vanguard Group, Inc. (7.25)7.253.893.46Canada Bread Co (Bimbo Canada)Grupo Bimbo SAB de CVNormaciel, S.A. de C.V. (39.25)Promociones Monser, S.A. de C.V. (12.3)Philae, S.A. de C.V. (4.95)1.58--Canada Dry Motts IncKeurig Dr Pepper IncMaple Holdings BV (32.88)Mondelez International Inc (5.33)BDT Capital Partners, LLC (4.82)3.191.531.09Snapple Beverage CorpCoca-Cola LtdCoca Cola CoBerkshire Hathaway Inc. (9.23)The Vanguard Group, Inc. (7.89)BlackRock Institutional Trust Company, N.A. (4.12)7.894.123.93Minute Maid Co of CanadaDanone Canada IncDanone SABlackRock Institutional Trust Company, N.A. (6.15)MFS Investment Management (4.99)Artisan Partners Limited Partnership [Activist] (4.95)2.406.15-Frito-Lay CanadaPepsiCo IncThe Vanguard Group, Inc. (8.86)BlackRock Institutional Trust Company, N.A. (4.73)State Street Global Advisors (US) (4.26)8.864.734.26PepsiCo Beverages CanadaGeneral Mills Canada CorpGeneral Mills IncThe Vanguard Group, Inc. (8.41)State Street Global Advisors (US) (5.71)Capital International Investors (5.61)8.415.105.71George Weston Ltd-Weston (Willard Galen Garfield) (53.56)RBC Global Asset Management Inc. (3.68)CIBC Asset Management Inc. (1.45)1.33--Kellogg Canada IncKellogg CoKellogg W.K. Foundation Trust (17.12)The Vanguard Group, Inc. (8.40)Gund (Gordon) (6.39)8.405.194.19Kraft Heinz Canada ULCKraft Heinz CoBerkshire Hathaway Inc. (26.61)3G Capital Management, Inc. (15.14)The Vanguard Group, Inc. (4.57)4.572.582.61Loblaw Cos LtdGeorge WestonGeorge Weston Ltd (47.21)RBC Global Asset Management Inc. (1.60)TD Asset Management Inc. (1.41)1.29--Maple Leaf Foods IncMaple Leaf Foods IncMcCain (Michael Harrison) (39.04)RBC Global Asset Management Inc. (8.60)The Vanguard Group, Inc. (1.61)1.61--Schneider CorpMetro Inc-Fidelity Management & Research Company LLC (17.26)TD Asset Management Inc. (2.97)The Vanguard Group, Inc. (2.76)2.761.74-Nestlé Canada IncNestlé SABlackRock Institutional Trust Company, N.A. (5.04)Capital Research Global Investors (3.81)The Vanguard Group, Inc. (2.72)2.725.04-Saputo Inc-Jolina Capital, Inc. (31.40)Placements Italcan Inc (10.24)Beutel, Goodman & Company Ltd. (2.32)1.53--Sobeys IncEmpire Co LtdBMO Asset Management Inc. (4.09)CI Global Asset Management (3.53)Jarislowsky Fraser, Ltd. (2.94)2.651.22-Unilever Canada IncUnilever PLCBlackRock Institutional Trust Company, N.A. (6.57)The Vanguard Group, Inc. (3.17)Leverhulme Trust (1.83)3.176.57-Wal-Mart Canada IncWalmart IncWalton Enterprises, L.L.C. (46.59)The Vanguard Group, Inc. (4.51)BlackRock Institutional Trust Company, N.A. (2.25)4.512.252.19aData obtained from Refinitv Eikon in March 2022bPercent shareholder ownership if ≥ $1.00\%$ of outstanding shares in the publicly listed company
## Shareholder ownership of publicly listed companies by three major asset managers
Figures 1, 2 and 3 map networks of ownership by the Vanguard Group Inc, Blackrock Institutional Trust Company and State Street Global Advisors (US) within Canadian food and beverage sectors and markets. Overall, The Vanguard Group Inc, Blackrock Institutional Trust Company and State Street Global Advisors (US) owned shares in the majority of national brand owners (or their parent company), including many that operate within the same sectors and markets (see Table 4 and Figs. 1, 2 and 3). The Vanguard Group Inc owned ≥ $1\%$ of shares in $95\%$ of publicly listed companies, Blackrock Institutional Trust Company $71\%$, and State Street Global Advisors (US) $43\%$.Fig. 1Ownership networks within the Canadian packaged food manufacturing sector; Figure 1 maps ownership within the Canadian packaged food manufacturing sector including ownership by the Vanguard Group Inc, Blackrock Institutional Trust Company and State Street Global Advisors (US). A Shareholder ownership level: Dotted lines represent ≥ $1\%$ ownership while solid lines represent ≥ $5\%$ ownership by three major institutional investors; B Firm level: All national brand owners with ≥ $1\%$ of shares within the Canadian packaged food sector are represented. National brand owners are associated with their parent company (if applicable). * Loblaw Cos *Ltd is* a publicly listed company, however, George Weston *Ltd is* its parent company. C Product market level: All national brand owners are linked to a specific product market if they account for ≥ $1\%$ of shares within the product marketFig. 2Ownership networks within the Canadian non-alcoholic beverage manufacturing sector; Figure 2 maps ownership within the Canadian non-alcoholic beverage manufacturing sector including ownership by the Vanguard Group Inc, Blackrock Institutional Trust Company and State Street Global Advisors (US). A Shareholder ownership level: Dotted lines represent ≥ $1\%$ ownership while solid lines represent ≥ $5\%$ ownership by three major institutional investors; B Firm level: All national brand owners with ≥ $1\%$ of shares within the Canadian non-alcoholic beverage sector are represented. National brand owners are associated with their parent company (if applicable). * Loblaw Cos *Ltd is* a publicly listed company, however, George Weston *Ltd is* its parent company. C Product market level: All national brand owners are linked to a specific product market if they account for ≥ $1\%$ of shares within the product marketFig. 3Ownership networks within the Canadian grocery retailing sector; Figure 3 maps ownership within the Canadian grocery retailing sector including ownership by the Vanguard Group Inc, Blackrock Institutional Trust Company and State Street Global Advisors (US). A Shareholder ownership level: Dotted lines represent ≥ $1\%$ ownership while solid lines represent ≥ $5\%$ ownership by three major institutional investors; B Firm level: All national brand owners with ≥ $1\%$ of shares within the Canadian grocery retailing sector are represented. National brand owners are associated with their parent company (if applicable). * Loblaw Cos *Ltd is* a publicly listed company, however, George Weston *Ltd is* its parent company. C Market level: All national brand owners are linked to a specific disaggregated market if they account for ≥ $1\%$ of shares within that market
## Discussion
Overall, the Canadian packaged food and non-alcoholic beverage manufacturing sectors consisted of both oligopolistic and more competitive product markets with significant foreign multinational company presence, in contrast with the grocery retailing sector which was highly concentrated and dominated by national companies. There was considerable evidence of common ownership within and across all sectors.
## Company ownership
Ownership of packaged food and non-alcoholic beverage manufacturing and grocery retailing sectors was found to be highly complex and integrated. Most national brand owners, particularly within manufacturing sectors, were affiliated to a parent company, most often a foreign multinational.
In addition, many publicly listed companies had common investors as demonstrated by shareholder ownership by three large asset managers. Although more research is needed to fully understand the effect of common ownership on the level of competition, concerns exist over the potential for common ownership to reduce competition, particularly in concentrated markets [53]. The issue of common ownership from a public health perspective requires additional consideration, particularly in the concentrated grocery retailing sector, and in highly concentrated food and beverage product markets.
## Policy implications and areas for future investigation
These analyses underscore the globalized nature of modern Canadian food and beverage sectors, and the need for targeted and meaningful international efforts from the food industry to make positive changes that will support health, as called for by the World Health Organization and others [54, 55]. From a regulatory standpoint, while public health-related efforts from individual countries may help support changes within borders, cohesive and aligned policies across multiple countries are likely to have a greater impact, particularly when it comes to the packaged food and non-alcoholic beverage manufacturing sectors.
Research from various countries has shown different levels of commitments from food and beverage companies to support the transition towards healthier food environments [55–58]. However, research also suggests that voluntary company commitments to date have not necessarily translated into meaningful improvements or action in relation to marketing or the nutritional quality of the food supply (e.g., companies reporting stronger commitments regarding product (re)formulation have not further improved the healthfulness of their product portfolios compared to those with weaker commitments in Canada) [59–62]. Further investigation into the nutrition-related policies and actions of companies identified in this analysis is warranted, to increase the transparency and accountability of the private sector for their role in NCD prevention, identify areas for improvement, and/or draw attention to the need for further public sector policy action, as warranted [63].
Previous research has also identified that investors have significant potential to contribute to addressing nutrition-related challenges and increasing the accountability of food and beverage companies [3, 64–66]. For instance, following pressure from shareholders, Unilever recently committed to publicly reporting the healthfulness of its food sales using government-endorsed Nutrient Profile Models as well as internal metrics [67]. Although nutrition is only recently emerging as a potential focus area for responsible investment, it has been posited that investors would likely benefit from companies taking into account “nutrition-related risks and opportunities” such as the increasing demand for healthier products, the implementation of regulations pertaining to food composition, fiscal policies (e.g., taxes on sugary drinks) and the demand for product innovation, as these could influence their financial performance [64]. Nutrition-related considerations are garnering interest from institutional investors, however, targeted actions on these issues by institutional investors is still infrequent and inconsistent [3].
This study used a public health lens to better understand elements of market structure that may influence the healthfulness of food environments in Canada building on monitoring and accountability efforts as part of the International Network for Food and Obesity/non-communicable disease Research, Monitoring and Action Support (INFORMAS) [68]. Future market structure analyses may consider a multiple lens approach which incorporates health, environmental sustainability, equity and social justice, to further assess the suitability and effectiveness of current market regulations, and garner support for change where needed.
## Strengths and limitations
This study is the first investigation of food and beverage market structure from a public health perspective in Canada. It used a wide variety of indicators to assess market structure, including market size, number of active brand owners with a market share of ≥ $1\%$, level of market concentration, and company ownership. Nonetheless, further analyses of market structure could account for additional metrics such as the degree of vertical integration, barriers to market entry [10] and market dynamicity (i.e., the entry of new products within markets). For instance, certain manufacturers have operations upstream or downstream along the food chain, such as the Kraft Heinz company that not only produces packaged foods, but provides almost a third of processing tomato seeds across the globe [69]. Other companies are cooperatives and inherently operate along multiple segments along the value chain. For example, Agropur *Cooperative is* owned by 2908 dairy producers whose milk is used to produce a variety of dairy products [70]. As such, the Canadian food system is even more integrated than this analysis would suggest. Moreover, company and brand ownership are dynamic, with companies frequently and strategically selling or acquiring brands or companies, and monitoring the structure of leading companies can help understand how they maintain or gain market power.
Several of the analytical variables used in this paper have limitations. HHI values likely present an underestimation of the level of concentration within the Canadian packaged food and non-alcoholic beverage manufacturing and grocery retailing markets, for several reasons. First, certain product markets which were assessed constituted of multiple smaller product markets (e.g., the ‘dairy’ product market included products such as butter, drinkable yogurt and cheese). Next, national geographical boundaries were used to assess market concentration, however, while not available via Passport, by Euromonitor International, smaller geographical boundaries may have been relevant to define grocery retailing markets; for instance, Metro Inc was identified as a leading grocery retailer in Canada, yet only operates in Eastern Canada (i.e., in the provinces of Québec and Ontario). Lastly, metrics were assessed for brand owners with ≥ $1\%$ market share (as opposed to using data for those with even the smallest % market share), as information for all brand owners active within a market is not always available on Passport.
Finally, this analysis focused on market structure, however, structure, conduct and performance are inter-related concepts of corporate market power. Future work could examine the conduct of the identified companies (and investors) and their performance focusing on public health and sustainability outcomes, perhaps using or adapting INFORMAS protocols [68].
## Conclusion
An analysis of the Canadian food and beverage market demonstrated the globalized and integrated nature of the Canadian packaged food and non-alcoholic beverage manufacturing and grocery retailing sectors. Moderate to high levels of market concentration in numerous product markets and in the grocery retailing sector suggest that efforts by leading companies could significantly benefit the healthfulness of those product markets, and retail settings in which Canadians make the majority of their food selections. Better understanding market structure may help identify additional levers to improve the healthfulness of the Canadian food environment, and has the potential to guide further in-depth analyses pertaining to industry policies and practices related to obesity and NCD prevention, corporate governance and food system transformations in Canada and globally.
## Supplementary Information
Additional file 1: Table A1. Number of companies with ≥$1\%$ market share over 10 years calculated using both national brand owner (NBO) and global brand owner (GBO) market share data in Canada by sector and market. Table A2. Four-firm concentration ratios (CR4) over 10 years calculated using both national brand owner (NBO) and global brand owner (GBO) market share data for firms with ≥$1\%$ market share in Canada by market. Table A3. Herfindahl-Hirschman Index (HHI) over 10 years calculated using both national brand owner (NBO) and global brand owner (GBO) market share data for firms with ≥$1\%$ market share in Canada by market.
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63. Robinson E, Blake MR, Sacks G. **Benchmarking food and beverage companies on obesity prevention and nutrition policies: evaluation of the BIA-Obesity Australia Initiative, 2017–2019**. *Int J Health Policy Manag* (2020.0). DOI: 10.34172/ijhpm.2020.147
64. 64.Access to Nutrition Initiative. Investor Expectations on Nutrition, Diets & Health. 2020. Available from: https://accesstonutrition.org/app/uploads/2020/06/Investor-Expectations-on-Nutrition-Diets-and-Health-FINAL.pdf.
65. Sacks G, Robinson E. **Investing for health: potential mechanisms for the investment community to contribute to obesity prevention and improved nutrition**. *Curr Obes Rep* (2018.0) **7** 211-219. DOI: 10.1007/s13679-018-0314-y
66. Robinson E, Parker C, Carey R, Sacks G. **The Extent to Which Obesity and Population Nutrition Are Considered by Institutional Investors Engaged in Responsible Investment in Australia-A Review of Policies and Commitments**. *Frontiers Psychol.* (2020.0) **11** 577816. DOI: 10.3389/fpsyg.2020.577816
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|
---
title: Development and validation of a new analytical method for determination of
linagliptin in bulk by visible spectrophotometer
authors:
- Lujain Sahloul
- Maisam Salami
journal: Scientific Reports
year: 2023
pmcid: PMC10008578
doi: 10.1038/s41598-023-31202-w
license: CC BY 4.0
---
# Development and validation of a new analytical method for determination of linagliptin in bulk by visible spectrophotometer
## Abstract
A simple, economical, and specific analytical method has been developed for determining and validating linagliptin (LNG) in bulk. This method is based on a condensation reaction between a primary amine in LNG and an aldehyde group in P-dimethylaminobenzaldehyde (PDAB) to form the yellow Schiff base with a wavelength of 407 nm. The optimum experimental conditions for the formulation of the colored complex have been studied. The optimum conditions were 1 mL of $5\%$ w/v reagent solution with methanol and distilled water as a solvent for both PDAB, LNG respectively, also adding 2 mL of HCl as an acidic medium, heating to 70–75 °C on a water bath for 35 min. Furthermore, the stoichiometry of the reaction has been studied according to Job’s and Molar ratio method which was expressing 1:1 for LNG and PDAB. The researcher modified the method. The results show that the linearity in the concentration range (5–45 µg/mL) with correlation coefficient R2 = 0.9989 with percent recovery (99.46–$100.8\%$) and RSD was less than $2\%$, LOD and LOQ 1.5815 − 4.7924 μg/mL respectively. This method can show high quality and there is no significant interference with excipients and in pharmaceutical forms. None of the studies showed the development of this method before.
## Introduction
Linagliptin is a new dipeptidyl peptidase-4 (DPP-4) inhibitor, this enzyme is responsible for the downgrade of the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). So this action increase the insulin level in the blood and the level of glucagon will be decreased1. It is used in combination with diet and exercise in the therapy of type 2 diabetes, either alone or in combination with other oral hypoglycemic agents (Empagliflozin, Metformin)2. The drug received FDA approval in May 20111. As an oral antidiabetic agent, it has a xanthine-based structure, that may be a significant factor in the drug’s elimination half-life (more than 100 h). The long half-life of LNG may be more beneficial for patients who occasionally miss their doses of medication1. The chemical structure of LNG is 8-[(3R)-3-aminopiperidin-1-yl]-7-(but-2-ynyl-3-methyl)-1-[(4-methylquinazolin-2-yl)methyl]purine-2,6-dione. Molecular weight 472.5 g/mol (Fig. 1)2.Figure 1Chemical structure of linagliptin.
Chemical and Physical Properties of LNG: color and form: slightly hygroscopic, white to yellow solid. Melting Point: 190–195 °C. Solubility: In water, 3.33 mg/L at 25 °C; soluble in methanol; sparingly soluble in ethanol. Stability: *It is* stable if stored as directed; avoid strong oxidizing agents2.
Linagliptin isn’t available as a specific method for analyzing in *British pharmacopeia* (BP) or the United States Pharmacopeia (USP). The Review of Research Literature has shown several articles for the determination of LNG in pharmaceutical forms, including the spectrophotometer in UV3–5, also in VIS with chemical derivation using NQS (1,2-naphtho quinine 4-sulphonic acid sodium salt), vanillin6 and picric acid7 as a chromogenic reagents. Capillary Electrophoresis (CE)8, and with high-performance liquid chromatography (HPLC)9–13. Those methods are specificity and selectivity but they need more time, a lot of amount an expensive solvent and equipments.
While Vis *Spectrophotometer is* a simple, economical analytical method that it is used in multiple fields (Clinical biochemistry, chemistry…etc.). Also, it has high speed and doesn’t need extraction for detection of a small amount of material concentration compared to another method HPLC or CE. Due to LNG does not have a lot of chromophores, one chemical derivative method was used to develop a new spectrophotometer method for determination LNG in tablet products. This method needs less time-assuming, and has few solvents. Also, it has high precision and accuracy by using the derivation agent as PDAB that produces with the primary amine of LNG a yellow color of the complex having a maximum absorption at 407 nm.
## Materials and methods
The research method which is adopted for this paper was an experimental design that used an analytical approach to explore the research objectives.
## Instrumentation
UV–Visible Spectrophotometer T80 + PG Instruments Ltd—England. Sartorius-Germany analytical balance.water bath. Calibrated glass pipettes.
## Materials and reagents
Analytical grade Linagliptin, its purity was $99.25\%$, (Simson Pharma Limited—China),Methanol ($99.9\%$, ACROS).Para-Dimethylaminobenzaldehyde (PDAB) $5\%$ (w/v) (Scharlau-)Hydrochloride acid (HCl) $37\%$ (SCP science)
## Preparation of linagliptin standard stock solution
A stock solution (1000 µg/mL): was weighed 50mg of LNG in 50 mL of distilled water.
## Preparation of linagliptin working solution
A working solution (100 µg/mL): 10 mL of stock solution is diluted to 100 mL with distilled water.
## Preparation of ρDAB 5% (w/v) solution
ρDAB $5\%$ (w/v): 1.25 g was dissolved in 25 mL of methanol with good shaking, and was freshly prepared.
## Analytical procedure
Aliquots volume of working solution LNG were moved into series of 10 mL volumetric flasks to perform final concentrations of 5–45 ppm. To each flask was added 1 mL of ρDAB $5\%$ (w/v) and 2 mL of HCl $37\%$, then to the water bath at 70–75° for 35 min after closed and shaking very well, after those flasks were cooled and diluted to 10 mL by distilled water. The maximum absorption of the yellow color was 407 nm upon the blank. The amount of linagliptin was calculated from the calibration curve.
This research was approved by the Damascus University Faculty of Pharmacy deanship.
## Ethics approval and consent to participate
Our study protocol was reviewed and approved by the Damascus University Faculty of Pharmacy, Damascus, Syria.
## Results and discussion
The analysis of the results that were gathered in the course of the research paper was sustained on four levels: the absorption spectra, Optimization of Reaction conditions, Stoichiometry of reaction and Validation of the developed method.
## Absorption spectra
The absorption spectra of LNG (20 µg/mL) were recorded on a vis spectrophotometer in the wavelength region of 350–700 nm. which shows the absorption maxima curve at 407 nm in Fig. 2.Figure 2Absorption spectrum of the derivative product against a reagent blank (20 µg/mL LNG).
## Effect of diluting solvent
Many solvents such as distilled water, methanol, ethanol, and acetonitrile were chosen for solved LNG and PDAB as solvents for both LNG, and PDAB respectively, and as potential diluting media. Distilled water and methanol were found to be the optimum solvent for both LNG and PDAB respectively, and the highest absorbance values and the stability of the Schiff base formulated were obtained.
## Effect of PDAB concentration
The reaction between LNG and the increase of different PDAB concentrations (1–$7\%$ w/v) was studied. It was found that absorbance increases with increasing PDAB concentration and reaches its maximum value by using $5\%$ w/v of reagent shown in Fig. 3a. Figure 3Optimization of Reaction conditions. ( a) Effect of PDAB concentration (dashed line) and volume (dotted line) on the reaction of LNG with PDAB. ( b) Effect of temperature (dotted line) and time (dashed line) on the reaction of LNG with PDAB. ( c) Effect of HCl volume on the reaction between LNG (20 µg/mL) and PDAB $5\%$. ( d) Stability of the reaction product.
## Effect of PDAB volume
The researcher studied the suitable of PDAB volume in the range of 0.5–3 mL, to find the suitable volume of PDAB reagent ($5\%$ w/v). So, the result had shown that the highest absorption intensity was achieved at a PDAB volume of 1 mL then it decreased (Fig. 3b).
## Evaluation of HCl volume
The researcher tested different volumes of HCI $37\%$ (1–3 mL), to select the appropriate acidic medium volume for LNG and PDAB reaction. The best results were obtained with 2 mL of HCl $37\%$ (Fig. 3c).
## Selection of temperature
To choose the best temperature which can achieve the objectives of the research, many different temperatures range (25, 60–65, 70–75, 80–85 °C) in the water bath was checked. Thus, it is possible to determine the best temperature range was 70–75 °C (Fig. 3b).
## Select the optimum heating time
The effect of prolonged heating time on this reaction was controlled by monitoring the color development at different time intervals (10–60 min) at 70–75 °C. Maximum absorbance values were gained at 35 min (Fig. 3b).
## Stability of the reaction product
For stability of the derivative product, it was tested by time intervals (5–130 min). The results showed that the derivative compound needs 15 min to reach the great absorption value and then the values remain constant for an hour (Fig. 3d).
## Stoichiometry of reaction
The quantitative reaction rate was recorded by Job’s Method of Continuous Variations and the Molar ratio method14.
Preparation of Linagliptin Standard Solution (2 × 10–3 mol/l) by weighted 94.5 mg of LNG in 100 mL of distilled water.
Preparation of ρDAB (2 × 10–3 mol/l) solution by dissolving 29.8 mg in 100 mL of methanol with good shaking, was freshly prepared.
The continuous variation plot in Fig. 4a indicated that a molar fraction of 0.5 meant the ratio 1:1 of LNG: PDAB complex with 2 mL of $37\%$ HCl for all flasks. While the Molar ratio plot in Fig. 4b, the highest absorbance was with LNG: PDAB complex ratio = 1. So, 1 mol of LNG interacts with 1 mol of PDAB.Figure 4Stoichiometry of reaction LNG and PDAB. ( a) Job’s method of continuous variations between LNG and PDAB. [ LNG]: 2 × 10−3 M; [PDAB]: 2 × 10−3 M; [LNG] + [PDAB]: 4 mL + [HCl]: 2 mL. (b) Molar ratio method of stichometry of the reaction between LNG and PDAB. ( c): Proposed reaction pathway between LNG and PDAB.
The scheme of reaction between LNG and the reagent is shown in Fig. 4c.
## Validation of the developed method
The method was validated according to the procedures described in ICH guidelines, which included linearity, precision, accuracy quantitation limit, and detection limit15.
Linearity was evaluated after determining the optimum conditions, with Beer’s law used over the concentration, ranges 5–45 μg/mL, The calibration curve was formed by concentration versus absorbance, using linear regression analysis with an R2 value 0.9989 and regression equation was $y = 0.0265$x + 0.0602.
The limit of detection (LOD) and limit of quantitation (LOQ) were calculated according to the following formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{LOD }} = \frac{{3.3{ } \times {\text{SD}}}}{{\text{S}}},\;{\text{LOQ }} = \frac{{10{ } \times {\text{ SD}}}}{{\text{S}}} $$\end{document}LOD=3.3×SDS,LOQ=10×SDSwhere: SD is the standard deviation of the blank, and b is the slope. The LOD and LOQ were found to be 1.5815 and 4.7924 μg/mL, respectively (Table 1A)
## Precision and accuracy
The repeatability of the method was studied by measuring six replicate specimens of one concentration (20 µg/mL) of LNG within—day, with relative standard deviation RSD \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le \hspace{0.17em}2\%$$\end{document}≤$2\%$.
Furthermore, the intermediate precision was evaluated by analyzing three replicate solutions of LNG at three different concentrations (10–20–30 µg/mL) during the same day (intra-day) and over three successive days (inter-day), where no significant difference between intra- and inter-day precision values was observed and RSD % values were less than 2 (Table 1B).Table 1Validation of the developed method: (A) Parameters for the performance of the proposed method. ( B) Accuracy and precision of the proposed method for the determination of LNG.(A) Parameters for the performance of the proposed methodParameterValueMeasurement wavelength (nm)407Linear range (μg/mL)5–45Intercept (b)0.0602Slope0.0265Standard deviation of the blank0.012751595Correlation coefficient (R2)0.9989Limit of detection, LOD (μg/mL)1.5815Limit of quantification, LOQ (μg/mL)4.7924Molar absorbativity L mol−1 cm−119,500Sandell’s Sensitivity (µg/cm−2)0.0242(B) Accuracy and precision of the proposed method for the determination of LNGPrecisionConcentration (µg/mL)Recovery %RSDRepeatability* (inter-day)20100.770.56Intermediate precision** (intra-day)1099.660.64820100.590.73330100.70.455AccuracyConcentration (µg/mL)Recovery %Mean\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hspace{0.17em}\mp \hspace{0.17em}\mathrm{SD}$$\end{document}∓SD(inter-day)**1099.42100.156 ∓ 0.68620100.7830100.27*Recovery value is mean of 6 replicates; RSD: relative standard deviation.**Recovery values are mean of 3 replicates; SD: standard deviation.
About the accuracy that was checked by the percent mean recovery and RSD % between three concentrations of LNG (10–20–30 µg/mL). Table 1B.
## Sandell’s sensitivity (µg/cm−2)
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{Sandell}}\;{\text{Sensitivity }}(\upmu {\text{g}}/{\text{cm}}^{{ - 2}} {\text{) = S}}\;{\text{per}}\;{\text{0}}{\text{.001}}\;{\text{absorbance}}\;{\text{unit,}}\;{\text{S = Molecular}}\;{\text{weight/}}\varepsilon. $$\end{document}SandellSensitivity(μg/cm-2) = Sper0.001absorbanceunit,S = Molecularweight/ε. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\text{Molar}}\;{\text{ absorbativity}}\;{\text{L}}\;{\text{mol}}^{{{{- 1}}}} \;{\text{cm}}^{{{{ - 1}}}} {\text{:according}}\;{\text{to}}\;{\text{Beer-Lambert}}\;{\text{law;}}\;{\text{A}} = \varepsilon {\text{. b}}{\text{. M}}{\text{.}} $$\end{document}MolarabsorbativityLmol-1cm-1:accordingtoBeer-Lambertlaw;A=ε. b. M.
## Specificity
The researcher had checked the interference of excipients that might be formed the pharmaceutical dosage by using this method, in order to determine the specificity of proposed method.
Sample was prepared by mixing mannitol 20 mg, maize starch 30 mg, pregelatinized starch 30 mg, copovidone 5 mg and magnesium stearate 4 mg16. These excipients were analyzed by proposed method. The results were referred good recovery $101.157\%$ and RSD = $0.8387\%$. These means no interference between excipients and determination of LNG in pharmaceutical dosage by this derivative method.
## Robustness
The robustness of proposed method was examined, the results indicated that there were no bias in the experimental conditions (variation concentration of PDAB ∓ $0.2\%$ (w/v) also volume of PDAB and HCl ∓ 0.2 mL, the heating time ∓ 2 min, measurement wavelength (nm) ∓ 2 nm, while others parameter were constant so RSD was less than $2\%$.
## Conclusion
Linagliptin is a new oral antidiabetic agent. LNG isn’t available a specific analytical method in pharmacopeia. LNG has many articles that aim to assay LNG by HPLC, this method needs expensive solvents and specific equipments. So, this paper was described a new, simple analytical method depending in condensation reaction between LNG with PDAB as a chemical reagent for determination LNG in bulk and pharmaceutical dosage. The conditions of optimal conditions of this analytical research were studied and it was found that distilled water and methanol were the best solvents for both LNG and PDAB, 1 mL of PDAB $5\%$ as a derivative reagent with 2 mL of HCl $37\%$ as an acidic medium with heating to 70–75 °C on a water bath for 35 min to form the yellow Schiff base with a wavelength at 407 nm and the stability of Schiff base formed for one hour. Validation of the proposed method was showed that this reaction has linearity 5–45 μg/mL, according to the correlation coefficient R2 = 0.9989 with percent recovery (99.46–$100.8\%$) within an accepted criteria and RSD was less than $2\%$, so this proposed method has good accuracy and precision and high specificity.
Furthermore, the molar ratio of this reaction was selected between LNG and PDAB it was (1:1) complex depending on two methods the Job of continuous variations and molar ratio.
The proposed derivation method was characterized by the use of a PDAB reagent that is easy to apply and does not consume expensive materials compared to other analytical methods that require many devices and materials.
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|
---
title: Interpretable and context-free deconvolution of multi-scale whole transcriptomic
data with UniCell deconvolve
authors:
- Daniel Charytonowicz
- Rachel Brody
- Robert Sebra
journal: Nature Communications
year: 2023
pmcid: PMC10008582
doi: 10.1038/s41467-023-36961-8
license: CC BY 4.0
---
# Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve
## Abstract
We introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, interpretable, deep learning model to deconvolve cell type fractions and predict cell identity across Spatial, bulk-RNA-Seq, and scRNA-Seq datasets without contextualized reference data. UCD is trained on 10 million pseudo-mixtures from a fully-integrated scRNA-Seq training database comprising over 28 million annotated single cells spanning 840 unique cell types from 898 studies. We show that our UCDBase and transfer-learning models achieve comparable or superior performance on in-silico mixture deconvolution to existing, reference-based, state-of-the-art methods. Feature attribute analysis uncovers gene signatures associated with cell-type specific inflammatory-fibrotic responses in ischemic kidney injury, discerns cancer subtypes, and accurately deconvolves tumor microenvironments. UCD identifies pathologic changes in cell fractions among bulk-RNA-*Seq data* for several disease states. Applied to lung cancer scRNA-Seq data, UCD annotates and distinguishes normal from cancerous cells. Overall, UCD enhances transcriptomic data analysis, aiding in assessment of cellular and spatial context.
There is interest in measuring the influence of spatial cellular organization on pathophysiology, which is being accomplished through spatial transcriptomics. There the authors present UniCell Deconvolve, a pre-trained deep learning model that predicts cell identity and deconvolves cell type fractions using a 28 M cell database.
## Introduction
The ability to measure expression of the coding genome has revolutionized the study of human disease1. Recently, the appreciation of inter-patient cellular heterogeneity has led to methods such as single-cell RNA Sequencing (scRNA-Seq) being introduced to increase study resolution2. There is now interest in measuring the influence of spatial cellular organization on pathophysiology, which is being accomplished through Spatial Transcriptomics (ST). Broadly, ST platforms can be divided into two categories. Targetted, high-resolution approaches such as MERFISH3, split-FISH4, or OligoFISSEQ5 can profile tens to hundreds of genes using variations of nucleic-acid hybridization techniques at the subcellular level. Alternatively, whole-transcriptome, lower-resolution approaches such as Slide-Seq6, Visium7, DBiT-seq8, or Stereo-seq9 function via spatial-aware RNA capture and sequencing. The unbiased nature of whole-transcriptome approaches makes them appealing for early-stage discovery and hypothesis-generation.
Resolution of whole-transcriptome spatial platforms varies, ranging from 10 um for Slide-Seq to 55 um for Visium. While the density of capture arrays is increasing, spatial capture spots nevertheless contain RNA content eluted from several single cells. Differences in gene expression are driven in-part by varying cell type mixtures and levels of individual cell transcript expression. As such, it is essential to “deconvolve” cell type fractions for each spot to improve interpretability and analysis of differential gene expression patterns. Multiple machine learning methods addressing cellular deconvolution have been introduced. Earlier approaches focusing on bulk-RNA-Seq include methods such as DSA10, MuSiC11, CIBERSORT/CIBERSORTx12,13, Scaden14, DeconRNASeq15, and SCDC16. The emergence of ST has ushered in several next generation deconvolution algorithms, notably Cell2Location17, SPOTLight18, Stereoscope19, SpatialDWLS20, DSTG21, STDeconvolve22, and RCTD23.
A significant limitation of most approaches is the requirement for a reference profile of cell type expression. Meta-analyses of RNA-seq deconvolution algorithms have shown that choice of reference is more important than methodology in determining deconvolution performance24. The choice of cell types to include in a reference is not always apparent, and collecting matched samples for reference generation is not always possible. Furthermore, the use of general scRNA-Seq “atlases” as references may not be appropriate when transcriptional differences due to experimental or disease-related factors confound cell type expression patterns. It has been suggested that the integration of numerous studies with varying experimental conditions and technical platforms can improve the robustness and generalization of deconvolutions25.
To address these challenges, we introduce UniCell: Deconvolve Base (UCDBase), a pre-trained, context-free, deep learning foundation model for universal cell type deconvolution. UCDBase is trained using 10 million pseudobulk RNA mixtures generated from the world’s largest fully integrated scRNA-Seq database, comprising 28 million fully-annotated single cells representing 840 cell types collected from 899 uniformly preprocessed, validated, and published single-cell datasets. First, we describe the collection and integration strategy used to build training data for UCD, and then detail the architecture of our model.
We demonstrate how UCDBase performance compares favorably to existing reference-based approaches, with feature attribute analysis enabling orthogonal validation of predictions by associating gene expression with particular cell types. UCDBase can also be leveraged as a global cell type feature extractor for transfer learning given user-specified cell signatures, facilitating the rapid deployment of context-specific deconvolution “UCDSelect” models.
We highlight UCDBase’s ability to deconvolve changes to immune and stromal cell infiltrates in response to ischemic kidney injury, associating differentially active stress response genes to kidney epithelial cell types. Next, UCDBase applied to bulk-RNA-*Seq data* pinpoints specific losses in pancreatic beta cell and oligodendrocyte fractions in type 2 diabetes and multiple sclerosis, respectively. UCDBase also accurately differentiates between cancer subtypes across bulk, spatial and single-cell data. Lastly, UCDBase is used to annotate primary human lung cancer data, providing marker genes to corroborate predictions, and distinguishes normal from cancerous epithelial cells.
## Single-cell RNA-Seq simulated mixture benchmarking
We compared actual and predicted cell type fractions across simulated mixtures for our three benchmarking datasets comprising PBMC, Lung, and Retina tissues (see Fig. 1a, c, and e). For each mixture set, we compared actual and predicted cell type fractions across 500 simulated mixtures (see Fig. 1b, d, and f). To better evaluate the performance of UCDSelect, we performed an ablation study whereby transfer learning performance of UCDBase embeddings alone was compared with conventional gene feature extraction alone, as well as combined. Fig. 1Benchmarking UniCell deconvolution performance across tissue types.a UMAP visualization of human peripheral blood mononuclear cell (PBMC) single cells used to generate pseudobulk mixtures for deconvolution benchmarking, annotated by cell type. b Box plots of deconvolution performance for each cell type ($$n = 8$$) in the PBMC dataset, stratified by method (y-axis), as measured by concordance correlation coefficient (x-axis). c UMAP visualization of human lung tissue single cells used to generate pseudobulk mixtures for deconvolution benchmarking, annotated by cell type. d Box plots of deconvolution performance for each cell type ($$n = 19$$) in the lung dataset, stratified by method (y-axis), as measured by concordance correlation coefficient (x-axis). e UMAP visualization of human retina periphery single cells used to generate pseudobulk mixtures for deconvolution benchmarking, annotated by cell type. f Box plots of deconvolution performance for each cell type ($$n = 17$$) in the retina dataset, stratified by method (y-axis), as measured by concordance correlation coefficient (x-axis). g Spatial profile of murine hippocampal formation profiled using Slide-SeqV2 colored by individual cell type. h Spatial heatmaps representing a downsampled hippocampal dataset, where each spot represents the average gene expression profile obtained from multiple individual cells in close spatial proximity. The first column illustrates the ground truth fractions of three representative cell types comprising the downsampled spatial spots (with the scale ranging from 0 to 1 representing $0\%$ to $100\%$ of cells in that downsampled spatial spot corresponding to a given cell type). The middle column denotes cell fraction predictions for matched or related cell types given by UCD Base. The rightmost column denotes cell type predictions made by UCD Select trained on individual cell profiles from the source dataset. i Box plots of deconvolution performance for each cell type ($$n = 14$$) in the hippocampal dataset, stratified by method (y-axis), as measured by concordance correlation coefficient (x-axis). For boxplots in b, d, f and i, the center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. Individual data points are superimposed over each boxplot.
For PBMCs, our pre-trained UCDBase model obtained strong concordance correlation coefficient (CCC) values of 0.816 averaged across the eight cell types identified in our dataset, while UCDSelect achieved CCC of 0.864, 0.921 and 0.92 for deconvolution utilizing gene features only, embeddings only, and both sources, respectively. UCDBase performed comparably with current State of the Art methods such as Cell2Location (C2L) (see Fig. 1b top), despite the fact that C2L and competing algorithms were trained to exclusively consider the deconvolution of PBMCs. We note that in the PBMC task, the cell type categories used for comparison are distinct and well-defined, indicating that the corresponding cell types found in UCDBase training dataset are likely to be well-aligned with the labels assigned for this task. UCDSelect exhibited superior performance in this benchmarking task compared with all competing methods.
Results seen in Lung and *Retina data* highlight the importance of accounting for mismatch between UCDBase and target cell type annotations, and the relevance of UCDSelect as a transfer learning extension of UCDBase. We show that preliminary results indicated average concordance (CCC = 0.524 for Retina, CCC = 0.532 for Lung) with high variance when directly comparing annotated cell types from reference data with the corresponding cell types found in UCDBase’s 840 cell type output. We investigated these discrepancies in Supplementary Fig. 8, where we identified cell types with low initial concordance measurements in both Lung and Retina datasets (see Supplementary Fig. 8a, c). We select three low-performing cell types and performed cross-correlation with output vectors of all 840 UCDBase cell types, and plot pearson correlation between the ground-truth labeled cell type and top 16 highest correlated UCDBase outputs (see Supplementary Fig. 8b, d).The results strongly illustrate that UCDBase correctly identifies cellular state identity, albeit the annotation matched within UCDBase does not always perfectly align with those in the target dataset. For example in our lung mixture dataset, “endothelial cells”, which show a direct label matched correlation of effectively zero, are identified by UCDBase as correlating most closely with “lung endothelial cells” (pearson’s $R = 0.851$). Similar patterns are seen among other examined cell types, supporting the notion that UCDBase is correctly identifying cell types, however label mismatches make it difficult to discern true accuracy when working with benchmarking datasets relying on potentially flawed, user-defined cell types as ground truth labels. It further highlights the importance of detailed interpretation when analyzing the results of a global pre-trained deconvolution model.
UCDSelect however, represents a natural extension of UCDBase and a solution to the complexity of label mismatch. By aligning UCDBase’s feature vectors to a user-specified reference signature, we are effectively able to guide UCD to a solution within the parameter space defined by the user. For the Lung benchmark, UCDSelect achieves average CCC values of 0.832, 0.861, and 0.883 for features, embeddings, and both sources, respectively. The Retina benchmark saw average CCC values for UCDselect of 0.93, 0.97, and 0.972 for features, embeddings, and both sources, respectively. The strong performance on the Retina benchmark is unsurprising, given that unlike the PBMC and Lung datasets which featured mixture and reference data derived from different studies, the paired Retina reference and mixture data sources are both derived from two samples from the same study, which likely minimizes the batch and/or experimental related differences between common cell types in these samples.
## Downsampled spatial transcriptomic data
We measured the performance of UCDBase and UCDSelect in deconvolution of downsampled mouse hippocampal Slide-SeqV2 spatial transcriptomic data (Fig. 1g). We highlight strong visual concordance between three representative ground truth hippocampal cell type annotations and UCDBase / UCDSelect predictions in Fig. 1h. To quantify performance, we deconvolve downsampled mixtures using several comparator methods developed for spatial transcriptomics, and show that UCDSelect exhibits comparable deconvolution performance relative to state-of-the-art reference-based approaches, with average CCC values of 0.511, 0.532, and 0.561 for features, embeddings, and both sources, respectively (Fig. 1i). Stereoscope and Tangram showed the most consistent performance on this dataset, with average CCC of 0.616 and 0.588, respectively.
## Bulk RNA-Seq benchmarking
We compared the performance of UCDBase and UCDSelect in deconvolution of gold-standard bulk RNA-Seq mixture and reference profiles developed for the community DREAM bulk RNA-Seq deconvolution challenge with respect to the results obtained by submitted competitor methods (see Supplementary Fig. 9)26. UCDBase achieved a mean score (measured by pearson’s R) of 0.68 when deconvolving 96 cell mixtures, placing it in the top half of solutions. In contrast, UCDSelect achieved mean Pearson’s R scores across 11 compared cell subtypes of 0.793, 0.892, and 0.903 respectively, scoring considerably higher than competing approaches.
## Hyperparameter sensitivity analysis
UCDBase and UCDSelect were found to be robust to changes in mixture hyperparameters (see Supplementary Fig. 10) across our three synthetic mixture datasets. We saw a minimal linear decrease in mean performance as sample complexity (i.e. number of unique cell types) increased (see Supplementary Fig. 10b, e and h). Model performance was found to be consistent while varying the number of cells used to generate each mixture, with a slight reduction for lower total mixture cell counts, which we believe is caused by increased signal-to-noise ratio (see Supplementary Fig. 10c, f and i).
When perturbing gene dropout, we found that significant performance reductions were seen only after >$80\%$ of expressed genes in the benchmarking mixture samples were removed as inputs. This robustness to dropout suggests that UCDbase leverages nonlinear combinations of gene sets as the basis of cell type fraction predictions, and is resilient to the noise seen in transcriptomic data, especially at lower read depth. It nevertheless suggested that the current UCDBase architecture may not be appropriately tuned for use with technologies profiling smaller numbers of genes. To validate this important distinction, we obtained mixture and reference signatures generated by Li. et al. 2022 derived from the mouse visual cortex using the in-situ STARmap spatial transcriptomic technology (see Supplementary Fig. 11a, b)27. With an input of just 881 genes, we reasoned that UCDBase performance would be limited by such a degree of sparsity (~$97\%$) relative to the whole transcriptome input space it was trained on. Unsurprisingly, we see that UCDSelect achieves only modest deconvolution performance (CCC = 0.658, 0.567, and 0.64 for features, embeddings and both sources, respectively). Notably, results indicate that in this scenario, gene expression features, as opposed to UCDBase extracted embeddings, provide superior deconvolution performance (see Supplementary Fig. 11c). We therefore suggest to users that UCD be utilized primarily in cases where whole transcriptome data is available so as to maximize accuracy and performance.
## Training data composition and sensitivity across selected technology platforms
As UCDBase was trained using a comprehensive collection of studies collected using a range of technology platforms, from a range of sources including different species, we sought to better understand the composition of our training dataset, and assess the impact, if any, on UCDBase deconvolution accuracy. Utilizing keyword extraction on metadata available for each collected project, we assessed the most likely technology platform used to generate the collected dataset. For instances where multiple technologies were identified, the most common and/or first occurring keyword was assigned as a label for that study. A similar keyword approach was used to detect species for each dataset, with semicolons denoting multiple potential species identified for a given study. We report on the results of this technology and organism assessment in Supplementary Fig. 12. We estimate that ~$66.4\%$ of our collected cells were generated using a version of the 10X Genomics Chromium platform (see Supplementary Fig. 12a). $95.1\%$ of cells in our database are estimated to derive from short-read sequencing data, with just $4.9\%$ coming from technologies such as Smart-Seq. UCDBase and UCDSelect performance was then benchmarked for deconvolution of synthetic mixtures generated from PBMCs derived using a number of scRNA-Seq technologies from a study conducted by Ding et al. 201928. We highlight the performance of this comparison in Supplementary Fig. 13, where we demonstrate that both UCDBase and UCDSelect show comparable performance in deconvolution accuracy across multiple platform technologies, including Smart-Seq long-read data.
Looking at species origins, human derived data made up the majority of cells in our database at $43.2\%$, with $22.1\%$ coming from mice, and $33.1\%$ of cells coming from datasets where both Human and Mouse keywords were found. By and large the vast majority of single cell data in our training dataset ($98.4\%$) is derived from either Human or Mouse sources, which represent the most common species subject to single cell analysis (see Supplementary Fig. 12b). Across all matched cell types, the average correlation between gene expression across mouse and human data was found to be moderately positive (pearsonr $r = 0.46$) (see Supplementary Table 4). Given the potential discrepancies in gene expression between species, we therefore suggest that users bear in mind the species of origin when utilizing UCDBase given the species composition underpinning its training dataset.
## Characterization of pathophysiologic cell type aberrations in ischemic kidney injury
Kidney ischemia reperfusion injury (IRI) describes the oxidative stress and inflammatory damage induced by revascularization following a loss of blood flow and oxygen to cells of the renal system29. IRI is a common perioperative complication occurring during major trauma, shock, sepsis, or transplant, and understanding the pathophysiologic changes it induces is critical in developing strategies to mitigate its long term impacts30. Using temporal spatial transcriptomics data of coronal kidney tissue sections collected from a mouse bilateral renal IRI model, developed by Dixon et al. 202231, we leveraged UCDBase to explore changes in kidney cell fractions associated with progressive IRI damage (see Fig. 2a).Fig. 2UniCell deconvolves mouse kidney undergoing ischemic reperfusion injury.a Five publically available spatial transcriptomics samples were acquired representing kidney cross sections taken from mice at different stages of ischemic renal reperfusion injury (IRI), and analyzed using UCDBase to determine predicted cell type compositions. A visual summary of the experimental conditions and sample processing is provided. b Overview of critical kidney anatomy and general spatial localization of key kidney cell types is shown as a reference. c Spatial deconvolution and distribution of select cell types of the murine kidney across different time points ($$n = 1$$ spatial sample at each time point) following IRI. d Bar plots of average predicted fractions (y-axis) for select cell types deconvolved from spatial transcriptomics samples taken at different time points (x-axis) following IRI. Sample sizes are shown beneath each compared condition, representing individual spatial capture spots. Spots with <$0.5\%$ reported fraction of a given cell type were excluded from analysis. Bar height denotes the average predicted cell type fraction for each cell type across conditions. Error bars denote $95\%$ confidence interval (CI). P-values indicate the significance of difference between groups evaluated using an unpaired two-sided Wilcoxon rank sum test, with Benjamini-Hochberg correction for multiple comparisons (Source Data File—(d)). e Spatial predictions of fibrotic and immune infiltrate before and after IRI ($$n = 1$$ spatial sample at each time point). f Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of select cell types predicted to be present at the control ($$n = 1$$) timepoint. Sample sizes represent individual spatial capture spots with at least $10\%$ predicted fraction for that given cell type (Source Data File—Fig. 2f). g Changes in feature attribution weights for select genes (x-axis) indicating proximal convoluted tubule (PCT) epithelial cell fractions shown across different time points (y-axis) following IRI (Source Data File—Fig. 2g). h Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of select cell types predicted to be present at the 6-week post-IRI ($$n = 1$$) timepoint. Sample sizes represent individual spatial capture spots with at least $10\%$ predicted fraction for that given cell type (Source Data File—Fig. 2h). For scale bars in c and e, these represent the fraction (range 0–1) a given spatial coordinate is predicted to be composed of a given cell type. For boxplots in f and h, the center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. Individual data points are superimposed over each boxplot.
We began by examining deconvolution results in the context of normal control tissue in Fig. 2c, comparing it with expected cellular organization as summarized in Fig. 2b. UCDBase identified spatial distributions of proximal (PCT) and distal convoluted tubule epithelial cells localizing correctly to the outer cortex zone of the kidney. The thick-ascending limb of the loop of henle (TAL/LOH) was localized to the inner-renal medulla, while cells of the collecting duct (CD) were identified to be distributed across the renal cortex with increased abundance in the medulla, as they coalesce into the renal calyx. Intercalated cells (IC) were identified mainly along the boundary zone of the outer medulla, consistent with IC preferential localization in the earlier sections of the CD32. UCDBase also predicted “brush cells” in the outer medullary zone, which we suspect correspond to the S3 straight segment of the PCT based on identified gene attributes (see Supplementary Table 2). This is unsurprising, as the morphology of PCT cells is brush-border like, and the S3 segment displays the least degree of functional differentiation33. *Specific* genes UCDBase associated with all renal cell types were contrasted with established literature and are detailed in Supplementary Table 2.
We next examined changes in absolute cell type fractions predicted to occur following IRI. The overall composition and spatial organization of major kidney cell types remained unchanged (see Fig. 2c-center & right). Increases in t cell, suppressor macrophage, and fibroblast content became apparent as early as 2 days post-IRI compared with control, peaking at the 6 week timepoint (see Fig. 2d, e).
A notable gene attributed to t cells was Ccr7. It has been shown that Ccr7 + t cells mediate kidney injury during transplant allograft rejection, suggesting a similar role in IRI34. Suppressor M2-like macrophages promote kidney repair after acute IRI by modulating innate immunity30.
Fibroblast infiltrate at the 6 week timepoint (see Fig. 2h-left) was, to our surprise, associated with complement factor-H (cfh) expression. The authors of the original study explicitly noted the inability to establish a link between cfh and fibroblasts from *Visium data* alone, and verified its selective expression among kidney fibroblasts using an independent single-nucleus RNA-Seq dataset31.
While the canonical PCT marker Slc34a1 remains a consistent attribute of PCT cells across time points (see Fig. 2g), we see evidence of secondary markers overexpressed following injury, suggesting temporal physiologic changes to PCT cell function. The metabolic waste efflux pump Abbc2 has been shown to be overexpressed after acute renal IRI in mice35, and exhibits increased attribution for PCT cells at 12 h post-injury, suggesting overexpression and increased PCT stress35.
Together, UCDBase enables us to rapidly paint a comprehensive picture of physiological changes underpinning the kidneys’ response to IRI. Through cell type deconvolution in addition to feature attribute analysis, UCDBase identifies physiologically relevant marker genes underpinning the transition from homeostatic renal function to chronic inflammation and fibrosis, while simultaneously capturing the complex interplay between fibroblasts, t cells, and immunosuppressive macrophages.
## Robust malignant subtype identification and cancer feature attribute analysis
Dysregulation of gene expression programs is a hallmark of cancer36, as such we expected that deconvolution of nonmalignant cells from cancerous cells using transcriptional profiles was possible. We sought to determine UCDBase’s cancer detection and subtype classification performance.
Testing UCDBase’s sensitivity to malignant vs. normal tissues, we deconvolved bulk RNA samples from GTEx ($$n = 7$$,851) and TCGA ($$n = 10$$,459), predicting samples to be $97.3\%$ vs $74\%$ non-malignant ($$p \leq 0$$) when comparing median values of GTEx and TCGA samples, respectively (see Supplementary Fig. 14a right). A notable outlier prediction is seen among GTEx liver samples (see Supplementary Fig. 14a left), which can be attributed to sample-specific pathological, preprocessing, or quality control factors (or UCDBase training data label misannotation between non-malignant hepatocytes and liver hepatocellular carcinoma (LIHC)). Using deconvolved TCGA data spanning 18 cancer subtypes matched between UCDBase and TCGA, we re-normalized malignant cell results independently of non-malignant cell types to predict cancer subtypes (see Supplementary Fig. 14b). UCDBase achieved a micro-average AUC of 0.889 across all cancers (see Supplementary Fig. 14c), indicating strong classification capability.
To gain insight into the gene feature profiles learned by UCDBase, we examined the top-5 gene integrated gradient weights for all 1,143,791 primary cancer cells in our training database averaged by subtype (see Supplementary Fig. 15). Examining the results, we see that UCDBase successfully learns gene expression profiles representing unique transcriptome signatures of subtype-specific malignancies. Demonstratively, prostate cancer adenocarcinoma (PRAD) is identified via NKX3-1, a distinct marker of prostatic cancers37, as well as other genes such as PCA3, and FOLH1. For melanoma (SKCM), UCDBase associates it with the expression of MLANA, the melanoma diagnostic antigen melanin-A38, as well as genes such as TRYP1 and MTRNR2L2. Further inspection of the abovementioned gene features and others (see Supplementary Table 2) demonstrates UCDBase learned subtype-specific gene representations that appear to corroborate their relevance as suggested in prior studies.
We next asked how exactly the feature weights learned by UCDBase distinguish, at a pan-cancer level, malignant vs. non-malignant epithelial cells. We performed differential “relevance” analysis to identify top gene feature weights that tended to be overrepresented and/or underrepresented as predictors among malignant vs. non-malignant epithelial cells across all cancer subtypes. In total, 1,365 genes were identified to be typically positively correlated with malignant cells, while 821 genes were identified to be positively correlated with normal epithelial cells. *Each* gene set was then subject to GO_BIOLOGICAL_PROCESS_2021 gene set enrichment analysis using Enrichr (see Supplementary Fig. 16). Of significance among malignancy-associated genes, we found “inflammatory responses” to be among the highest upregulated geneset (adj. $$p \leq 5.4$$E-5). *Numerous* genesets pertaining to signaling pathways including PI3K (adj. $$p \leq 0.022$$), ERK$\frac{1}{2}$ cascade (adj. $$p \leq 0.024$$), and the MAPK (adj. $$p \leq 0.016$$) cascades were also identified to be significantly upregulated, in addition to angiogenesis (adj. $$p \leq 0.008$$). In contrast, normal epithelial cell gene features appear to overwhelmingly favor cell cycle and regulatory machinery, such as “regulation of G2/M transition of mitotic cell cycle” ($$p \leq 2.57$$e-12). Overall, these results appear to suggest that UCDBase may interpret an epithelial cell as cancerous if it exhibits the simultaneous expression of inflammatory and pro-proliferative signaling pathways. *Further* gene set analysis of UCDBase learned representations may yield additional insights into the fundamental biology of cancer and other disease processes.
## Spatial transcriptomic deconvolution of tumor microenvironment
We next elected to deconvolve a diverse set of publically available solid tumor spatial transcriptomic tissues, including Breast Adenocarcinoma (BRCA), Prostate Adenocarcinoma (PRAD), and Colorectal Adenocarcinoma (COAD). Where available, we compared UCDBase deconvolution results to histological annotations performed by certified human pathologists to determine relative accuracy of underlying cell type predictions. Feature attribute analysis was performed for all predicted cell types, with pathophysiologic significance elaborated for each gene in Supplementary Table 2 where appropriate.
## Breast adenocarcinoma spatial deconvolution
UCDBase correctly identified the most likely tumor subtype, BRCA, localized across ductal glands consistent with pathologists annotations (see Fig. 3a). There was strong concordance with pathologist-designated fibrous tissue deposits and fibroblast predictions, attributed to numerous well-established extracellular-matrix (ECM) genes including COL12A1, a gene previously implicated in pro-inflammatory stromal desmoplasia and tumor progression in several cancers39. Endothelial cells were detected throughout the tumor stroma, and particularly showed strong attribution to apelin receptor (APLN), a gene involved in maintaining pro-angiogenic states among endothelial cells, possibly indicating active tumor neovascularization [73].Fig. 3UniCell allows for deconvolution of tumor microenvironments across varying cancer subtypes with unique histologic features.a (left) Hematoxylin & Eosin (H&E) stained section of a breast invasive adenocarcinoma (BRCA) sample with human-derived pathological annotations (provided with source data) overlaid. ( right) UniCell Deconvolve Base (UCDBase) predicted distribution of key cell types in the tumor microenvironment for a sequential section derived from the same sample ($$n = 1$$). b Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of select cell types predicted to be present in the BRCA spatial sample. Sample sizes represent the top $2\%$ ($$n = 51$$) of individual total spatial capture spots by predicted fraction for that given cell type (Source Data File—Fig. 3b). c (left) Hematoxylin & Eosin (H&E) stained section of a prostate adenocarcinoma (PRAD) sample with human-derived pathological annotations (provided with source data) overlaid. ( right) UCDBase predicted distribution of key cell types in the tumor microenvironment for a sequential section derived from the same sample ($$n = 1$$). d Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of select cell types predicted to be present in the PRAD spatial sample. Sample sizes represent the top $2\%$ ($$n = 88$$) of individual total spatial capture spots by predicted fraction for that given cell type (Source Data File—Fig. 3d). e (left) Hematoxylin & Eosin (H&E) stained section of a colorectal adenocarcinoma (COAD) sample. ( right) UCDBase predicted distribution of key cell types in the tumor microenvironment for a sequential section derived from the same sample ($$n = 1$$). f Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of select cell types predicted to be present in the COAD spatial sample. Sample sizes represent the top $2\%$ ($$n = 63$$) of individual total spatial capture spots by predicted fraction for that given cell type (Source Data File—Fig. 3f). For scale bars on the right-side of a, c, and e, these represent the fraction (range 0–1) a given spatial coordinate is predicted to be composed of a given cell type. For boxplots in b, d, and f, the center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. Individual data points are superimposed over each boxplot.
UCDBase identified multiple immune subtypes, including plasma cells, macrophages, and t cells, localizing to regions of pathologist-annotated immune infiltrate. Tumor-associated macrophages (TAMs) were found at or around areas of comedo-like tumor necrosis40. T cells were found to be localizing selectively around a distinct malignant duct located center-left of the tissue section, with attributed genes such as immune checkpoint costimulatory receptor CD28, as well as IFIT3, CCL5, and PLAAT4 implicating an active anti-tumor immune response. CD28 is required for an interferon-mediated immune response, coinciding with expression of interferon induced response protein IFIT341. The potent lymphocyte attractor ligand CCL5 is reported to be prospectively upregulated in tumor-infiltrating CD4 + t cells following an initial immune stimulation to maintain t cell infiltration42. Furthermore, phospholipase A / acetyltransferase 4 (PLAAT4) has been identified as loosely expressed in t cells to support the adaptive immune response43.
Interestingly, our model strongly implicates CXCL9 in the prediction of t cells, which is traditionally believed to be secreted by tumor cells themselves or TAMs to drive t cell recruitment44. When overlaying gene expression of CXCL9, CD3D (t cells) and CD68 (macrophages) (see Supplementary Fig. 17), we see moderate spatial correlation with CXCL9 and CD3D ($r = 0.4$, $$p \leq 3$$E-29) along the tumor-stromal interface, and weaker correlation with CD68 ($r = 0.17$, $$p \leq 1$$E-10). We hypothesize that cell-free RNA originating from apoptotic tumor cells in proximity to tumor infiltrating t cells may be captured during single cell encapsulation for sequencing. As the t cell category of UniCell’s training data is a generalized category encompassing 191,425 cells of varying possible subtypes and originations, some of which may be tumor-associated, this may be reflected in our results when analyzing cancer datasets. Nevertheless, we see an active image of the breast tumor microenvironment rapidly painted by UCDBase, whereby stromal and immune cellular components react to an ever-changing environment driven by active malignancy.
## Prostate adenocarcinoma spatial deconvolution
Turning our attention to prostate cancer (see Fig. 3c), UCDBase robustly distinguishes the tumor subtype, PRAD (Prostate Adenocarcinoma), and localizes malignant cell signatures within the Invasive Carcinoma region denoted in Fig. 3c-left, with nonmalignant luminal epithelial / basal cells in the lower-left region designated as “Normal Gland”. Fibromuscular zones outlined in green show distributions of myofibroblasts and smooth muscle cells. This sample contained a nerve fiber cross section, which UCD detected as schwann cells, the myelinating cells of the peripheral nervous system45. PRAD is widely considered to be an immunologically “cold” tumor, compared to immunologically “hot” cancers such as melanoma46,47. Supporting this, UCDBase did not detect meaningful presence of immune cells in the tested spatial section, and likewise we see PRAD ranking at the lowest end of absolute immune cell fractions among TCGA data deconvolved with UCDBase (see Supplementary Fig. 18).
Changes seen in prostate stromal tissue induced by carcinogenesis are mediated by cancer-activated fibroblasts (CAFs) adopting a myofibroblast-like phenotype48. Differentiating between myofibroblasts and conventional smooth muscle cells (SMCs) can be difficult as this phenotype is thought to reflect a continuum spanning conventional fibroblasts to mature prostatic SMCs49. Consequently, UCDBase showed overlapping gene attributions used to differentiate these two highly-related cell types (see Fig. 3d).
Feature attributes reveal how UCDBase learned to distinguish normal from cancerous prostate cells. Normal prostatic luminal epithelium was associated with KLK3 expression (see Fig. 3d). KLK3 encodes Prostate Serum Antigen (PSA), the most commonly used serum biomarker for prostate cancer despite suffering from low sensitivity due to its universal expression by both normal and malignant prostate cells. UCDBase instead delineates prostate malignancy to KLK4, an intracellular kallikrein localizing to the nucleus providing markedly different functions from other KLK family genes50. Studies comparing KLK gene expression between prostate cancer and healthy controls have shown stronger statistical correlations between malignancy status and KLK4 compared with KLK351.
## Colorectal adenocarcinoma spatial deconvolution
Lastly, we examine UCDBase’s deconvolution of colorectal adenocarcinoma (COAD, see Fig. 3e-right), and we can see clear localization of COAD malignant cells across presumptive tumor nodules shown in the unannotated H&E section in Fig. 3e-left. The stroma surrounding colorectal tumors has been shown to contain uniquely high proportions of infiltrating plasmablasts, a rapidly-dividing intermediate cell state representing activated B cells transitioned into mature, non-dividing plasma cells that function in an immunosuppressive role, which UCDBase readily detects in this sample52. Additional immune infiltrates identified by UCDBase include macrophages and t cells sitting among fibroblast cells, highlighting the significant stromal immune responses commonly associated with pro-inflammatory tumor microenvironments.
## Detecting cell type compositional changes in pathological bulk RNA-seq data
Given that scRNA-Seq and spatial transcriptomics remain cost-prohibitive for large-scale translational studies, bulk RNA-*Seq data* continues to dominate most clinical analyses. We tested UCDBase’s bulk-RNA-Seq ability to deconvolve bulk RNA-seq data to reveal pathologic changes in cellular fractions. Feature attributes for each predicted cell type are shown in Supplementary Fig. 19, with detailed analysis of each feature’s cellular relevance in Supplementary Table 2.
## Increased fibromuscular tissue deposition in idiopathic pulmonary fibrosis
Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by the progressive inflammation, damage, and subsequent deposition of fibromuscular tissue into the lung interstitial space, and a corresponding destruction of the alveolar epithelium leading to a reduction in gas-exchange efficacy (see Fig. 4a)53. Acute lung injury (ALI), also known as acute respiratory distress syndrome (ARDS), is characterized by transient damage to the gas-exchange apparatus often induced by viral infection, and features significant fibrous tissue deposition as part of the tissue healing process54.Fig. 4UniCell resolves expected pathophysiological changes in cellular fractions from Bulk RNA-sequencing data.a Visualization summarization basic pathophysiology of interstitial pulmonary fibrosis and potential shifts in cell type fractions. b Box plots of cell type fractions predicted by UniCell Deconvolve Base (UCDBase) for key lung cell types (y-axis) stratified by disease state (x-axis) (Source Data File—Fig. 4b). c Visualization summarization basic pathophysiology of type ii diabetes and potential shifts in cell type fractions. d Box plots of cell type fractions predicted by UCDBase for key pancreatic cell types (y-axis) stratified by disease state (x-axis) (Source Data File—Fig. 4d). e Visualization summarization basic pathophysiology of multiple sclerosis and potential shifts in cell type fractions. f Box plots of cell type fractions predicted by UCDBase for key brain white matter cell types (y-axis) stratified by disease state (x-axis) (Source Data File—Fig. 4f). For all boxplots shown in b, d, and f, the center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. Sample sizes for each stratification across all dot plots are shown below x-axis labels, with individual data points being patient samples and superimposed over each boxplot. For all boxplots shown in b, d, and f, P-values indicate the significance of difference between groups evaluated using an unpaired two-sided Wilcoxon rank sum test, with Benjamini-Hochberg correction for multiple comparisons.
Comparing Normal, ALI, and IPF tissues (see Fig. 4b), we saw significant reductions in fraction of Type II and Type I pneumocytes (ATII & ATI cells) in chronic IPF patient lungs ($$p \leq 3.33$$E-09 ATII, $$p \leq 5.16$$E-08 ATI), with no difference seen between Normal and ALI ($$p \leq 0.705$$ ATII, $$p \leq 0.058$$ ATI). This is consistent with the pathophysiologic destruction of alveolar epithelial cells in IPF. Fibroblast fractions were considerably higher for both ALI ($$p \leq 2.77$$E-03) and IPF ($$p \leq 2.41$$E-07) patients compared to normal controls, consistent with the role that excessive fibroblast proliferation plays in IPF pathogenesis55. We note a significant increase in smooth muscle cell fractions ($$p \leq 1.55$$E-07), defined by markers such as myosin heavy chain 11 (MYH11), occurring only in IPF patients. Pulmonary hypertension (PH) is a common secondary sequelae to IPF, whereby excessive vascular smooth muscle deposition leads to elevated arterial pressure and potentially fatal cardiopulmonary consequences56. Interestingly, we also saw a distinct increase in monocyte fractions for IPF patients ($$p \leq 7.65$$E-08), a finding not seen in ALI. It has been previously reported that elevated monocyte count is associated with IPF progression and may play a role as a useful prognostic biomarker57.
## Reduction of pancreatic Beta cells in Type II diabetes
Type II diabetes mellitus (T2DM) is a disease characterized by the progressive increase in cellular insulin resistance, leading to a state of persistent hyperglycemia causing a chronic increase of insulin production58. The production stresses placed on pancreatic beta cells, responsible for insulin production in the body, eventually lead to apoptosis and selective reduction in beta cell fractions among pancreatic islets (see Fig. 4c)59.
As T2DM progression exclusively impacts beta cells, we expected to see differences in cell type fractions with respect to disease status only among this cell type. Indeed, we noted a clear, statistically significant decline in pancreatic beta cell fractions ($$p \leq 1.57$$E-03) between normal and diabetes status (see Fig. 4d), with a downward trend ($$p \leq 0.0346$$; non-significant after correcting for multiple comparisons) among pre-diabetes patients correlating with disease progression. Beta cell fraction was not correlated to age in this cohort ($$p \leq 0.67$$, see Supplementary Fig. 20), although the rate of beta cell proliferation is known to decrease as age increases in the general population60. Examining other subpopulations of cell types present in pancreatic tissue (see Fig. 4c), we saw no significant differences in Alpha, Delta, and PP (gamma) cells, and similarly no differences in acinar and ductal cells forming the pancreatic glands.
## Reduced oligodendrocyte fractions in chronic multiple sclerosis
Multiple sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system characterized by chronic inflammation induced by neural lymphocytic infiltration, which leads to progressive destruction of oligodendrocytes, the cells responsible for production of the myelin sheath (see Fig. 4e)61.
We saw significantly reduced oligodendrocyte fractions ($$p \leq 1.25$$E-04) comparing control and active multiple sclerosis (MS) lesions (see Fig. 4f). No significant changes to cortical neuron or neural progenitor cell fractions were noted; however, a weak trend ($$p \leq 0.041$$; non-significant after correcting for multiple comparisons) showing increase in immature astrocytes between control and active MS was found. The proliferation of immature macroglial cells such as astrocytes has been associated with the neurotoxic effects of chronic inflammation induced by multiple sclerosis62.
Overall, we demonstrated that UCDBase is capable of faithfully recapitulating pathological changes in cell type fractions across a wide range of disease states. This robustness coupled with the validation offered by feature analysis makes UCDBase a promising tool for the analysis of other pathologic bulk RNA-Seq datasets.
## Rapid cell type annotation and disease subtyping in non-small cell lung cancer scRNA-seq data
Given strong performance across spatial and bulk RNA-seq tissues, we leveraged UCDBase to assist in basic cell type annotation of a non-small cell lung cancer scRNA-Seq dataset (see Fig. 5a and Supplementary Fig. 21a), validating assigned cell types using feature attribution analysis (see Supplementary Fig. 23) followed by a literature analysis of identified markers (see Supplementary Table 2).Fig. 5UniCell assists in rapid annotation of an integrated scRNA-seq Non-Small Cell Lung Cancer (NSCLC) dataset.a Visualization demonstrating the basic steps underlying NSCLC sample collection, processing, and analysis using UniCell Deconvolve Base (UCDBase). b UMAP visualization of human lung cancer biopsy single cells, annotated by unsupervised leiden cluster (left) and sample of origin (right). c UMAP visualization of cell type labels applied for each leiden cluster using UCDBase deconvolution results to guide annotation. d UCDBase predictions are used to separate normal from malignant epithelium. UMAP visualization showing probability of malignant lung adenocarcinoma (LUAD) cells initially co-clustering with cells labeled as normal epithelium (left). Re-clustering select subpopulation reveals two major clusters separating by sample of origin, Adjacent Normal or Tumor (right upper). Visualizing UCDBase LUAD probabilities on re-clustered cells demonstrates Tumor-specific cluster contains the majority of predicted LUAD malignant cells. e UMAP visualization showing probabilities of four major lung normal epithelial cell types distributed across re-clustered cells. f Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of LUAD malignant cells learned by UCDBase. Sample size ($$n = 1576$$) reflects the total number of single cells annotated as malignant LUAD. For boxplots, center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. Individual data points representing single cells are superimposed over each boxplot (Source Data File—Fig. 5f).
Examining the annotated clusters (see Fig. 5b, c) further, we sought to identify malignant LUAD cell subpopulations among predicted epithelial cells, which were found by UCDBase to likely be located within leiden clusters 18, 7, 22, 24, and 10 (see Fig. 5d-left). Because these clusters appeared intermixed with normal epithelial cells, we reclustered this subset of cells at higher resolution to reveal separations between malignant and nonmalignant cells (see Fig. 5d-right). We saw clear separation of cell clusters by biopsy status, indicating most likely that tumor tissue contained a predominance of malignant cells. Indeed, UCDBase predicted a higher probability of lung adenocarcinoma (LUAD) cells across the tumor biopsy derived cell clusters, with little to no malignant signal across cells derived from adjacent normal. To orthogonally validate malignancy predictions, we performed copy-number variation (CNV) inference, using a combination of smooth muscle, fibroblast, lung ciliated, and endothelial cells as reference controls, finding that UCDBase malignancy predictions overlapped estimated increased copy number variation (see Supplementary Fig. 21b, c). We quantified this relationship, finding considerably positive and significant correlation (spearman $r = 0.39$, $$p \leq 1.7$$E-88) between malignancy probability and average CNV score per cell (see Supplementary Fig. 21d).
Some LUAD feature attributes (see Fig. 5f) were found to mirror surfactant genes related to type II pneumocytes, unsurprising as ATII cells are believed to be the cell of origin of LUAD63. A major malignancy-specific feature identified was carcinoembryonic antigen 6 (CEACAM6), known oncogenic gene overexpressed in numerous cancers including non-small cell lung (NSCLC), colon, and breast cancers64. Additional NSCLC-related genes identified include NKX2-1, a key transcription factor involved in early lung development and diagnostic marker for LUAD65. Non-malignant epithelial cells (see Fig. 5e) were clearly assigned to lung-related cell types with straightforward feature attributes (see Supplementary Figu. 22) corresponding to established cell type markers (see Supplementary Table 2 for details). Overall, UCDBase enabled the rapid and accurate annotation of a complex NSCLC patient case, with feature attribute analysis allowing for prospective validation of cell type assignment, in addition to delivering contextual information pertaining to the biological processes underpinning the data itself.
## Discussion
In this work, we presented UniCell: Deconvolve Base, a universal, context-free cell type deconvolution tool for transcriptomic data that integrates the entirety of publicly-available scRNA-*Seq data* into a single unified training dataset for deep learning applications. Our corpus of 28 M fully-annotated single cells enables UCDBase to generate accurate cell type fraction predictions without the need for tissue or disease-context, enhancing its ability to explore and discover biological phenomena across all major subtypes of transcriptomic data. UCDSelect on the other hand, allows for context-specific deconvolution using user-defined cell signatures by leveraging transfer learning of UCDBase features.
We demonstrate that UCDBase and UCDSelect are capable of producing highly accurate deconvolution predictions using both synthetic scRNA-Seq mixtures and real-world spatial transcriptomics data, that are comparable and/or superior to state-of-the-art methods.
We highlight UCDBase’s deconvolution of the dynamics underpinning ischemic renal injury, in addition to the tumor microenvironment from differing cancer subtypes. We show how UCDBase can be leveraged together with feature attribute analysis to uncover pathophysiologic responses in bulk RNA-Seq datasets for: idiopathic pulmonary fibrosis, type II diabetes mellitus, and multiple sclerosis. Lastly, we leverage UCDBase to assist in cell type annotation of a scRNA-Seq NSCLC dataset.
We acknowledge that cell type labels provided by authors either directly via metadata or indirectly in studies may not be entirely accurate, and/or lack specificity with respect to labeling of distinct cell subtypes (i.e. labeling an immune cell as a CD4 + t cells vs. CD4 + effector memory t cell). Prediction specificity can be improved markedly by increasing the granularity of cell type label assignments via enhanced data integration to refine our primary training data corpus.
We demonstrate in Supplementary Fig. 10 how high levels of gene dropout ($80\%$+), and by extension absolute sequencing depth, can negatively affect model performance. This limits the applicability of UCDBase and UDCSelect towards targeted / in-situ spatial transcriptomics platforms. Improvements in training data augmentation by direct modeling of count downsampling in real time during training will enable future iterations of UCDBase to be more robust to dropout, and capable of better handling in-situ data.
UCDBase was designed using empirical evaluation of a range of hyperparameters concerning layer sizes, depth, and regularization parameters. Future iterations of UCDBase will leverage neural architecture search to iteratively test model layouts across a range of architecture choices and corresponding hyperparameters66.
The analysis of transcriptomic data is a challenging process necessitating significant time investment by end users to generate biologically plausible conclusions. Cell type annotation and deconvolution in the cases of scRNA and ST, respectively, are often laborious processes. With UniCell: Deconvolve Base (UCDBase), we provide a one step solution to this problem, generating accurate predictions across three transcriptomic data modalities, scRNA-Seq, bulk RNA, and ST without the need for additional user input. With UniCell: Deconvolve Select (UCDSelect), we enable deeper exploration of data, leveraging the benefits of transfer learning from a global pre-trained model, together with the contextual specificity of user-defined cell signatures.
We believe that the UCD tools suite, as a consequence of its comprehensive nature, ease of use, and speed, will accelerate the ability for the broader research community to conduct complex science, understand the cellular context underpinning diseases, and drive the development of therapeutics to address them.
## Ethics and oversight statement
The analysis in this manuscript was conducted using predominantly publicly available datasets. Prospective tissue samples acquired for single cell analysis were collected through the Mount Sinai Hospital (MSH) via the Mount Sinai Pathology Core Facility. Approval for this study was granted by the Mount Sinai Lung Tissue Utilization Committee. The tissue studied was acquired under the Institutional Biorepository protocol [12-00145] which allows for collection of excess surgical tissue that is not needed for diagnostic purposes, to be used for research. For this protocol, informed consent is based on specific language included in the general surgical consent, which all patients sign prior to surgery. All tissue is distributed in a de-identified fashion.
## UCDBase model overview
UCDBase is a Deep Neural Network (DNN) with 281,397,066 trainable parameters that accepts normalized RNA expression input and outputs predicted cell type fractions (see Fig. 6b). Below we describe the UCDBase architecture in detail and provide a rationale for key design choices. Fig. 6Summary of UniCell data collection, training mixture generation, foundation model architecture, and transfer learning strategy.a Depicted on the left is a flow chart summarizing the training data collection strategy. Candidate studies are first indexed from several primary and secondary data sources. Raw data is downloaded from respective source locations, and processed through an ETL engine where the output represents a standardized single cell count matrix. GPU accelerated post processing is performed, resulting in a normalized single cell expression profile. The number of studies indexed and total number of cells profiled (y-axis) is shown as a histogram on the right, within 3 month interval buckets (x-axis). b Each normalized single cell expression profile is utilized to form training data in the form of single cell mixtures, whereby random subsets of cells from across studies are selected (see flow chart on left) and averaged together to create mixed expression vectors of known cell type fractions. Expression vectors are fed into a deep learning model trained to predict the known cell type fraction. The basic elements and structure of the UniCell Deconvolve Base model are shown in the flow chart. On the right, an overview of the training process is shown. The y-axis represents either model loss or coefficient of determination (R2) while the x-axis represents training epoch, where one epoch represents a single full cycle through training dataset. Each colored line corresponds to a different size of training dataset (250 K, 1 M, 3 M, or 10 M synthetic mixtures). Solid lines represent model performance on the training dataset, while dashed lines represent model performance on test dataset. c Users have the option of supplying a contextualized reference profile, which is used in conjunction with embeddings obtained from UCD Base acting as a universal cell state feature extractor. A regression model is then trained using processed embeddings, yielding a fine-tuned transfer learning model applicable to user-specific use cases. Details of the transfer learning model architecture are shown in the corresponding flow chart.
## Primary data input and preprocessing
UCDBase accepts, by design, nearly all coding and non-coding human genes, for a total input size of 28,867 genes. Our approach takes advantage of the fact that DNNs, by nature of their overparameterization, do not suffer reduced performance from multicollinearity67; a phenomenon exhibited when one or more model input values (e.g. gene expression) are highly correlated that can negatively impact machine learning model performance. We hypothesized that an overparameterized input space would buffer performance against sparsity due to tissue heterogeneity and/or technical resolution exhibited by current transcriptomics platforms, allowing UCDBase to rely on alternate, non-canonical genes for cell type prediction in cases where canonical markers are not captured insufficiently sequenced.
Inputs, consisting of a 1-dimensional vector of gene expression values representing a single-cell or mixture of cells (hereby referred to as input), are normalized on a per-input basis. We surmised that UCDBase would be able to infer cell type signatures using relative differences in expression signals, and that per-input normalization would make the model more robust to differences in feature scales between training and test data. Gene expression counts are first normalized to 10,000, followed by log2 scaling so as to reduce the effect of heteroscedasticity on expression distribution. Each sample is z-scored to standardize variance across features. Lastly, we apply min-max scaling to rescale each feature value from 0 to 1, which is then used as input into UCD.
To further reduce reliance on canonical markers and limit the impact of sparsity, we introduced a two-step corruption process to our normalized sample inputs during model training. We first inject $5\%$ gaussian noise to the normalized expression profile of each gene (e.g. A normalized expression value of 0.8 from a given gene i will range anywhere from 0.75–0.85 following $5\%$ gaussian noise injection). This is followed by a dropout layer, where $20\%$ of input values are randomly set to zero. We reasoned that a combination of noise and dropout would further encourage the DNN to learn more complex representations of cell types that are robust to noise and missing genes.
## Intermediate layers
The core of UCDBase consists of four fully connected dense layers of 8192, 4096, 2048, and 1024 neurons using an exponential linear unit (ELU) activation function. Baseline characteristics of the model architecture, layer sizes, and overall depth of the network were determined through sequential, empirical evaluation of preliminary models of varying size on subsets of the final training dataset. In brief, we randomly subset the core training dataset to between 250 K to 1 M mixtures, and repeatedly trained models out to 10 epochs to determine the effect of sequential changes to individual parameters on deconvolution performance. We noted that model performance in the first few epochs (see Fig. 6b-right) was indicative of eventual convergence accuracy, making this a suitable proxy for rapid iterative optimization.
## Output and post processing
The final layer of the model is a dense layer of 840 neurons, corresponding to all cell types available in our training database to-date, with a softmax activation function yielding cell type fraction estimates summing to 1. No additional regularization is applied to the output layer,for it was found to reduce overall performance.
The cell types in the resulting deconvolution sit at varying levels of cellular specificity hierarchies (i.e. ‘t cell’ vs. ‘cd4-positive, alpha-beta t cell’), a consequence of leveraging author-derived annotations and/or low-confidence in more specific labels. In order to account for prediction biases induced by this uncertainty (i.e. some t cells may in fact be cd4 + t cells, while all cd4 + t cells are themselves t cells), we employ a belief propagation (BP) step during output post processing. BP involves projecting initial cell type fraction estimates onto a cell type hierarchy subset from the Cell Ontology (see Supplementary Fig. 4)68, and summing probabilities upwards along the directed tree structure. In such a way, fractional probabilities assigned to certain cell type subclasses are captured to yield higher confidence estimates of deconvolution fractions for more generic cell types.
## Generation of training data
UCDBase is trained using mixtures of simulated RNA-seq data (pseudobulk mixtures) generated from scRNA-Seq data. The process of generating a mixture is described in the following steps: [1] The total number of cells (T) comprising a mixture is selected. Given our desire to develop a model robust to both low-input (i.e. single cell / ST) and high-input (i.e. bulk RNA) samples for deconvolution, we randomly selected a value from 1 to 10,000 with uniform probability. [ 2] The number of unique cell types (N) in a mixture is chosen. We selected anywhere from 1 to 32 cell types to appear in a given mixture with uniform probability. The maximum value of 32 cell types (although parameterizable for future training) was assigned after analyzing the cellular diversity of all curated scRNA-Seq datasets, and taking the nearest log2 value of the $95\%$ percentile for the number of unique cell types per dataset. Selecting cell types with uniform probability has the effect of oversampling cells with low representation in the dataset, which improves model performance on rare classes. [ 3] The mixture fraction ratios F for N cell types are assigned. We assigned a random fraction ratio Fi for each cell Ni in a given mixture, such that all fraction ratios summed to 1. [ 4] *Expression data* for cell types are accumulated and averaged together. For each cell type Ni in a sample, we randomly selected Ni*T cells of that type from our uniformly preprocessed, integrated scRNA-Seq database. In cases where the required number of cells exceeds the total number of cells of a given type available in the dataset, the maximum number possible were added to the mixture, without duplication. Once all required cells were randomly selected, expression profiles were averaged together with a simple mean, resulting in a pseudobulk RNA expression profile with a known cell type fraction.
## Mixture formation via rapid data integration
The process of pseudobulk sample generation was implemented in python and optimized for high-performance execution using the python numba package (Numba: A high performance python compiler. https://numba.pydata.org/). All hyperparameters T, N, and F were precomputed as described above prior to generating mixtures, and cell type array row locations were pre-indexed to avoid repeat searches and improve performance. A total of 10 million pseudobulk mixtures were generated over the course of 18 h at a rate of 150 mixtures per second using a total corpus of 28 million annotated single cells into a 28,000,000 × 28867 compressed-sparse-row (CSR) matrix, running on a Google Cloud Engine (GCE) n2d-standard-224 virtual machine (VM) instance with 224 vCPU cores and 896GB system RAM. The choice of 10 million pseudobulk mixtures was made by training multiple iterations of UCD with stepwise increases in training dataset size, noting the impact the amount of mixture examples had on model performance (see Fig. 6b-right). We observed an increasing logarithmic relationship between training data size and performance, and determined 10 M mixtures to be the optimal size for initial model evaluation as a tradeoff between model accuracy and training time. Increases in size offered diminishing projected returns with respect to theoretical peak performance (see Supplementary Fig. 1). Ultimately, these training parameters can all be customized as future training sets become more expansive beyond 840 cell types and/or if necessary for extended accuracy in use cases where runtime beyond 18 h is not limiting given the rapid nature of the overall end-to-end training time.
## Single cell dataset curation
The collection and integration of a large annotated scRNA-Seq database is essential to the performance of UCDBase. In this section, we describe the major stages of our data curation process (summarized in Fig. 6a) and highlight technical approaches used to overcome challenges inherent to operating with integrated high-dimensional data at scale.
## Study indexing
*We* generated an index of all publicly available scRNA-Seq datasets, leveraging both primary sources such as NCBI Gene Expression Omnibus (GEO) (geo. Home—GEO—NCBI). https://www.ncbi.nlm.nih.gov/geo/ and EMBL ArrayExpress (AE) (EMBL-EBI. ArrayExpress. https://www.ebi.ac.uk/arrayexpress/), as well as numerous secondary source including the UCSC Cell Browser (UCSC Cell Browser. https://cells.ucsc.edu/?), EMBL-EBI Single Cell Expression Atlas (EBI Gene Expression Team—https://www.ebi.ac.uk/about/people/irene-papatheodorou, Single Cell Expression Atlas. https://www.ebi.ac.uk/gxa/sc/home), TISCH69, and the CZI Human Cell Atlas (Home, https://www.humancellatlas.org/). For primary data repositories GEO/AE, we performed an API-based programmatic keyword search for “scRNA-Seq OR single cell OR single-cell sequencing OR scRNA” to collect an exhaustive list of studies potentially containing scRNA-Seq data. Primary and secondary sources were then manually cross-referenced to eliminate duplicate entries.
At present, our base index contains 2695 studies published between January 2015 and June 2021, as such any studies published before or after this period are not currently included in UCDBase, but will be ingested, indexed and included in training sets for future builds. Examining global trends in publications (see Fig. 6a-top right), we note a steady increase in the number of scRNA-Seq biomonthly binned publications between 2014 and 2021 where data is available. Importantly, we see the number of single cells profiled in experiments increasing from a general average of 100 cells beginning around 2015 to over 10,000 cells per study in 2021 (see Fig. 6a-bottom right). As these trends are only expected to continue increasing, we anticipate a plethora of additional transcriptomic information will become available, the integration of which into global, accessible datasets will further aid in the development of not only machine learning algorithms, but fruitful data reanalysis revealing unique biological insights. We anticipate performing additional study indexing on at least a monthly basis at minimum to allow for integration of recently published studies into model training cycles, but it should be noted that ad hoc re-training can be conducted anytime using public or non-public datasets in <24 h using existing computing infrastructures.
## Data extraction
Each indexed study is passed through an automated data loader customized to each unique input source (i.e. GEO vs. AE) in an attempt to automatically extract scRNA-Seq count matrices. We first categorize all supplementary files associated with a particular study, looking for delimited file type extensions used for either transcriptional data or metadata (e.g.csv,.tsv,.h5,.h5ad,.mtx, etc…). In cases where expression data is stored as multiple files (i.e. 10X Genomics matrix.mtx/barcodes.tsv/genes.tsv triplet format), we successfully match pairs of common filenames by stem using text-similarity unsupervised clustering. Metadata when present, including cell type annotations, is typically found in separated delimited files and is identified by matching filename substrings “meta OR metadata OR annot OR annotation”. Files identified as potential expression or metadata are then batch downloaded using the aria2 utility for further processing.
## Data transformation
For each datafile in a study, we attempt to load, parse, and standardize gene expression data, and then match it with any associated metadata (see Fig. 6a). In most cases, expression data is stored in a delimited file structure (i.e.txt,.csv,.tsv formats) where each row and column correspond to cells and genes, respectively, or vice-versa. The major steps in file loading and standardization are: [1] File delimiters are first identified based on the most common present in the first line of the file (i.e. tab, space, comma, etc). [ 2] We estimate file dimensions using a heuristic function that calculates the bytesize of the first N-lines of a given file and compares it to the total file size. [ 3] Delimited files exceeding 100,000 projected rows or columns are read using a bespoke lightweight data parser, SRead, which distributes line reads across a unified thread pool for rapid data loading. Smaller files are read using the python pandas read_table function using the identified delimiter. The final output is yielded as a pandas DataFrame object. [ 4] Gene names are standardized to gene symbols using a comprehensive dictionary of gene IDs, synonyms, and symbols, where we further identify whether or not a row or column in the loaded DataFrame contains gene information and set this as the index or header, respectively. Depending on the initial data frame orientation, we correct orientation to follow tidy data conventions such that rows correspond to cells (observations) and columns correspond to genes (variables). Columns containing string-like characters are assumed to correspond to cell index names or associated cell-metadata, while those containing floating point or integer values are assumed to be expression data. [ 5] We attempt to match rows or columns of metadata with standardized row indexes of a given sample file. If a high degree of concordance is found between a data matrix and files flagged as potential metadata, we assume the file corresponds to cell-level metadata and align both dataframes together for final integration. [ 6] Lastly, we convert expression data into compressed-sparse row (CSR) matrices and map them together with align metadata (if-any) using the annotated dataset (i.e. h5ad) library. These H5-like objects are then uploaded to a Google Cloud Storage (GCS) bucket as unprocessed, standardized data sets suitable for downstream processing.
## Data preprocessing
Before a scRNA-Seq dataset can be utilized, it must undergo additional preprocessing. The most commonly used packages for scRNA-Seq processing and analysis, scanpy (python) and seurat (R), were not originally designed for high-throughput batch processing of thousands of scRNA-Seq datasets. Many computational steps, including covariate regression, batch correction, nearest neighbor calculation, and dimensionality reduction, can take significant time for datasets exceeding 100,000 cells. To enable UCD, we developed scanpyRAPIDS, a single cell analysis framework that enables complete end-to-end GPU-accelerated scRNA-Seq preprocessing. Leveraging the CuML, CuGraph, and CuPy python libraries from RAPIDS.AI (API docs. RAPIDS Docs https://docs.rapids.ai/api), we reimplement the entire standard scRNA-Seq preprocessing pipeline from basic QC through batch correction, dimensionality reduction and clustering residing entirely in GPU memory. Relative performance gains compared to traditional CPU-bound analysis is dependent on both the size of the input data and functional requirements of data preprocessing. For example, our scanpyRAPIDS implementation of the popular harmony batch correction algorithm70 successfully integrates 209,264 cells from 107 individual samples representing a time course of iPSC induction in 201.1 s on an NVIDIA Tesla T4 GPU, compared to 1204.6 s on a 16-core vCPU instance with 100+GB RAM. This presents a 6-fold speedup in runtime that continues to scale linearly with dataset size.
Using scanpyRAPIDS, all raw H5AD objects from the previous stage are concurrently preprocessed, parallelized across 4 NVIDIA Tesla T4 16GB GPUs. In brief, cells with <200 counts or genes expressed in <3 cells in a dataset were filtered out. Cells with >$20\%$ mitochondrial read fraction were assumed to be dead or damaged cells, and filtered out. Cells whose total counts exceeded two times the standard deviation of log-normal total counts for all cells in the sample were assumed to be damaged outliers, and filtered out. Total counts were normalized to 10,000 reads per cell and subsequently log-normalized. Depth normalized log counts are subsequently retained as input vectors for training mixture generation.
Dimensionality reduction and sample-level visualization was performed to better facilitate manual quality control (QC) checks of dataset quality and cell type annotations. As such, highly variable genes (HVG) were calculated, keeping genes with a log-normed mean between 0.0125 and 4, and a minimum dispersion of 0.25. HVGs were then z-score scaled to +/− 10. We regressed the effect of cell read depth (total counts) on expression of each HVG using an CUDA-accelerated ElasticNet regressor. Lastly, we performed PCA, with the number of components determined based on the number of post-filtered cells present in the sample. Nearest neighbor calculation was performed with n_neighbors set to 30. We ran both 2D and 3D UMAP dimensionality reductions, with min_dist set to 0.3. Lastly, unsupervised leiden clustering was performed, with resolution determined, like PCA components, on the number of post-filtered cells in the sample. Post-processed H5AD AnnData objects were then uploaded to a GCS bucket. For cases where multiple samples were preprocessed for a given study, we performed batch correction and re-clustering using the described approach with our GPU-accelerated implementation of the Harmony algorithm71.
## Data storage
When determining the optimal active data storage format, we had two concerns to address. Firstly, as our total preprocessed data repository contains nearly 1TB of data, which is expected to grow overtime and will need to be shared between team members, local on-disk storage would not be practical. Second, the need to rapidly load, inspect, and validate preprocessed data prior to final integration made traditional disk-mapped data formats such as HDF5 (and by extension, H5AD) limiting due to I/O throughput and cloud-access flexibility perspectives. As a result, we designed a bespoke data storage model, SingleCellData (SCD), built on top of the TileDB API. TileDB is a cloud-native data storage solution that integrates with cloud storage solutions such as GCS and S3, with explicit support for multidimensional, sparse array storage and parallel, chunked I/O operations72. The conversion of our preprocessed H5AD objects into SCD format allowed us to rapidly access and validate preprocessing quality for our datasets.
## Cell type annotation and label transfer
A total of 1,712 unique studies with 10,000+ associated data files were successfully preprocessed and stored using the above methods. Approximately $20\%$ of preprocessed data had cell type annotations available. Datasets without cell type labels were annotated using a semi-automated procedure involving manual curation of annotations using canonical & publication-derived marker genes, supported by an initial coarse cell type label transfer.
## Annotation of cell types
We sought to first project both annotated and unannotated cells into a common latent space using a deep autoencoder model in order to cluster similar cell types together and transfer labels of nearby known cell types onto unannotated cells. Although manual verification of the annotations was conducted, the use of an autoencoder joint-embedding model significantly accelerated the rate at which annotation could be accomplished.
To that end, we trained a spherical variational autoencoder (sVAE) with 30 latent dimensions on all preprocessed gene expression profiles (see Supplementary Fig. 5). In brief, sVAEs differ from conventional variational autoencoders (VAE) in the use of a non-normal prior distribution for parameter regularization. Early work applying sVAEs to scRNA-*Seq data* has shown benefits compared to traditional VAE in terms of embedding stability, leveraging the von-Mises-Fisher (vMF) spherical distribution73. For our implementation we utilized the PowerSpherical distribution, a related distribution that offers improved numerical stability during model training74. Nearest neighbors were determined using cosine similarity relative to 30 embedded latent dimensions using CuML, followed by unsupervised leiden clustering with the resolution hyperparameter set to 2.
For each of the 4200 initially identified clusters, known cell type classifications were averaged, assigning the most common annotation within a cluster to any cells unknown labels. Preliminary validation of each cluster annotation was performed by decoding the mean embedding vector to obtain a denoised, average gene expression profile for that cluster, and examining the highest expressed genes for correlations between canonical marker genes and predicted class types75,76. The process was then repeated, re-grouping cells into high-level subtypes (i.e. b cells, t cells, neuronal cells, etc) to obtain more refined subtype classifications.
For each dataset, cell type assignments were manually compared to available figures published in corresponding studies. Cases where first-pass, automated coarse annotations were too broad, incorrect, or did not match tissue-specific labels found in the study, were each manually identified and corrected. At the time of writing, $\frac{898}{1}$,712 studies were verified to pass QC, with an initial focus on the largest and most diverse datasets available. Expression profiles of all cells associated with studies that passed the QC criteria were then averaged across common cell types, and the top 50 differentially expressed genes for each cell type cluster were computed and made available in Supplementary Table 3. Visual inspection of top marker genes cross-referenced with known canonical markers provided empirical evidence of accurate cell type label assignments. In total, just over 28,000,000 single cells are contained in our dataset reflecting 840 unique cell types, including 55 cancer subtypes and 156 distinct cell lines (see Supplementary Fig. 3).
## Training strategy
The UCDBase model described previously was implemented and trained using Tensorflow 2.5.0. We utilized the Adam optimizer for supervised backpropagation with a learning rate of 0.0001 and an effective batch size of 256. Loss was computed using a variation of mean-squared error, sparse MSE. Given that for most examples, the vast majority of true cell type proportions are zero, we found during initial testing that conventional MSE would be artificially inflated, introducing a negative bias on model training. As a result, we opted to mask cell types whose mixture proportions are zero from calculation of MSE for that given sample, in effect focusing the loss function to contextualize towards each training sample.
Pseudobulk training data generated as described previously was serialized into TFRecord objects and saved into a separate GCS bucket, subsequently fed into the UCDBase model using the tf.data API. We trained UCD across 50 epochs over the course of 7 h running a preemptible Google Cloud Engine (GCE) a2-megagpu-8g instance, comprising eight NVIDIA A100 40GB GPUs, 96 CPUs, and 680GB system RAM. A train-test-split ratio of $\frac{80}{20}$ was selected for training validation, and test validation was conducted every five epochs and subsequently interpolated for visualization. Early stopping was utilized with a patience interval of 4 epochs and a loss delta threshold of 1.25E-5. Learning rate was dynamically lowered by $50\%$ if training loss did not improve by 1E-4 within 5 epochs. Results of model training, as measured by sparse MSE and pearson correlation, are highlighted in Fig. 6b-right. We observed model convergence by epoch 50, at which point the conditions of early stopping were met. Additional analysis of model convergence (see Supplementary Fig. 6) demonstrated that further training using the current training dataset would not yield tangibly relevant increases in model performance.
## UCDSelect model overview
UCDBase was designed to support the unbiased deconvolution analyses of datasets in instances where a reference signature is unavailable or unclear. However, when available, we recognize that following an initial assessment of the cellular composition within a dataset, it would be beneficial to enable a mechanism for model fine-tuning using a user-specified cell type signature. The contents of the user defined signature could also be determined and supported by the context-free analysis generated by UCDBase. Ideally, this mechanism would work seamlessly with the existing UCBase pipeline, and leverage the pre-trained base model to increase performance with minimal computational overhead. To that end, we propose a transfer learning extension, called UCDSelect (see Fig. 6c), that enables users to leverage the benefits of a pre-trained foundation model together with the specificity of a user-defined cell type signature.
As input, UCDSelect takes the same expression data representing a cell type mixture as UCDBase, with an added reference expression dataset including corresponding cell type labels. Reference data is then averaged across cell types to generate a mean expression signature for user-specified cell states. The input data from both mixture and reference are then fed into our pre-trained UCDBase model, with outputs consisting of the middle two dense layers of the neural network, of dimensions 4096 and 2048, respectively. The two feature vectors corresponding to the reference signature are then independently subject to dimensionality reduction using non-negative matrix factorization, an approach similar to that employed by the SPOTlight deconvolution algorithm, albeit with the input representing model features rather than raw gene expression values18. Each NMF model is fit using the reference, and used in turn to then transform the mixture. We utilize the Combat algorithm77 to perform batch alignment on the resulting NMF components so as to improve distribution concordance between reference and mixture, which has been used successfully in other reference-driven techniques, such as CIBERSORTx12. We repeat the above decomposition process using feature-selected gene expression values, and generate a final merged set of batch-corrected NMF components. We found that in most cases UCDBase features achieve improved relative deconvolution performance, the integration of both extraction techniques leads to slightly higher overall accuracy with negligible performance degradation.
The resulting adjusted and merged components are then subsequently used as feature vector inputs into a bagging ensemble of 48 Nu-Support Vector Regressor (nu-SVR) models using a linear kernel, implemented using the sklearn python library. The user in turn receives cell type deconvolution results specific to the cell type signatures used as a contextualized reference.
## Synthetic mixture generation and spatial downsampling
To assess UCDBase & UCDSelect performance, we generated pseudobulk mixtures using well-characterized baseline scRNA-*Seq data* collected from multiple tissue types profiled with scRNA-Seq including human PBMCs, lung, and retina (see Supplementary Table 1). Additionally, using an approach similar to Li. et al. 2022 in their Spatial Benchmarking paper27, we computationally downsampled a high-resolution (10 um) mouse hippocampal spatial dataset profiled using Slide-SeqV278 and made available in the squidpy python package via the function sq.datasets.slideseqv2 to create synthetic spatial mixture profiles of lower 100 um resolution with known cell type fractions.
For the PBMC dataset, we performed standard scRNA-Seq preprocessing, dimensionality reduction, and clustering, followed by manual cell type annotation using canonical markers, identifying 8 unique cell types (see Supplementary Fig. 7). Preprocessed and annotated lung and retinal tissue datasets were downloaded from the cellxgene database79. For each tissue type we selected one of the two paired datasets and generated 500 pseudobulk mixtures of 100 total cells, representing two to ten randomly selected cell types. We note that datasets utilized for mixture generation were not used in the training of UCDBase. The corresponding paired dataset for each tissue was then utilized as a reference profile. Cell types were matched between both reference and mixture datasets such that references and mixtures both contained the same possible cell types, with no outliers.
To further assess UCDBase & UCDSelect performance on bulk-RNA Seq data, we obtained reference and mixture datasets curated by the recent community DREAM challenge focused on bulk-RNA deconvolution26, and compared deconvolution accuracy to results available from challenge participants.
## Performance evaluation
We deconvolved mixtures using nine competing approaches developed for different deconvolution applications. Comparators primarily developed for spatial deconvolution include Cell2Location, Stereoscope, Tangram, destVI, SPOTlight, and RCTD. Bulk deconvolution approaches utilizing scRNA-Seq references include SCDC, MuSiC, and Scaden.
Each comparator method was run using published default parameters as recommended by vignettes available at the time of writing, unless otherwise stated. Tangram was run in “cluster” mode for improved performance, given we were only evaluating deconvolution. Reference datasets were randomly downsampled to retain 5,000 cells. Because existing deconvolution methods are sensitive to collinearity and most recommend a degree of input gene filtering, input dimensionality for comparators was limited to the top 7,000 most highly variable genes in the source dataset, as determined by the scanpy function sc.tl.highly_variable_genes using the seurat_v3 method.
We measured performance on the basis of how well UCDBase and UCDSelect were able to predict cell type fractions relative to ground truth. We reported results using Lin’s concordance correlation coefficient (CCC), a measure similar to pearson’s R, but one that is sensitive to both slope and intercept in addition to variance, making it a suitable metric for comparing deconvolution performance.
## Sensitivity analysis
We reasoned that deconvolution performance of UCDBase and UCDSelect would be sensitive to several hyperparameters pertaining to model complexity, notwithstanding the total cells in a bulk sample, the number of unique cell types present, and fraction of gene dropout.
Using our three scRNA-Seq mixture datasets, we established baseline mixing hyperparameters consisting of 100 cells, 5 unique cell types per mixture, and $0\%$ gene dropout. We then systematically perturbed each variable and generated 500 distinct mixtures, followed by deconvolution and performance evaluation. Total cells in a sample varied from 1 to 1000. The number of unique cell types in a sample varied from 1 to 8. Then, we tested the effect of gene dropout by randomly removing between 0 and $100\%$ of all expressed genes in each mixture at the input stage.
## Integrated gradients analysis
Deep neural network (DNN) models are often described as being “black-box” in nature, whereby the underlying mechanisms correlating inputs to outputs are largely unknown. The ability to interpret DNN models is highly desirable in biomedical science, as it enables researchers to verify a model is learning to generate predictions using plausible mechanistic correlations. Furthermore, interpretability can potentially deliver unique insights into biological processes as they pertain to input genes correlating with model outputs such as cell types. Several approaches for DNN interpretability have been proposed, including model agnostic approaches such as Shapley (SHAP) values80, Local Interpretable Model-agnostic Explanations (LIME)81, and DNN-specific methods such as Integrated Gradients (IG)82. IG differentiates itself from competing approaches with respect to its scalability to large input dimensions, making it particularly appropriate for interpreting UCD predictions with a 28,867 gene input space. While IG is only applicable to fully differentiable models, making it unsuitable for interpretation of ML methods such as gradient boosted trees or random forest, UCD’s implementation as a pure DNN makes it fully compatible with integrated gradients. The goal behind IG is calculation of the effect a change in a particular input i has on a given output class probability j, expressed as the gradient (i.e. partial derivative of j with respect to i). The integrated component refers to the accumulation (i.e. mathematical integration) of local gradients for input i across an interpolated range of values starting from a zero-baseline to its true value within a particular sample. Integrated gradients for each input gene are then multiplied by a scaling factor representing the absolute difference between the baseline case and normalized sample expression level, such that only genes actually expressed in the sample being analyzed will yield non-zero input attributions. Intuitively, this enables one to attribute the importance of input (gene) i with respect to how much it is adding to (positive attribution) or subtracting from (negative attribution) the models overall output probability for a given class (celltype) j. The intuition behind this approach is visualized in Supplementary Fig. 2.
For IG Analysis (IGA) in UCD, our baseline interpolation function consists of a 50-step linear interpolation of gene expression between zero and true sample values, multiplied by randomized gene dropouts (with a 50-step descending probability of $100\%$ to $0\%$ dropout, as a means of roughly simulating the effect of lower-read depth on absolute gene transcript detection). We approximate the integral of interpolated local gradients using a trapezoidal Riemann summation.
## Secondary spatial and Bulk-RNA-Seq data acquisition and preprocessing
We collected five publicly available, temporal spatial transcriptomics datasets from a mouse bilateral renal IRI model developed by Dixon et al. 202231. Breast Invasive Adenocarcinoma and Prostate Adenocarcinoma Spatial FFPE samples were downloaded from the 10X Genomics Datasets repository (see Supplementary Table 1). Colorectal ST data was downloaded from the 10X Genomics Datasets repository by means of the scanpy function sc.datasets.visium_sge.
Bulk-RNA Seq lung data originating from 5 mg tissue samples of patients with ALI, IPF, and healthy lungs collected by Sivakumar et al. 201983 was downloaded from the Gene Expression Omnibus (GEO) using accession GSE134692. Bulk-RNA *Seq data* of white matter lesions sampled from patients with multiple sclerosis or healthy controls by Elkjaer et al. 201984 was downloaded from GEO using accession GSE138614. Bulk-RNA *Seq data* from Fadista et al. 201485 comprising pancreatic islet samples from individuals with varying states of T2DM was downloaded from GEO using accession GSE50244. Severity of T2D is monitored long-term by the measure of Hemoglobin % A1c (HgA1c). Values <$5.7\%$ are considered “Normal”, values between 5.7 and 6.4 are considered “Prediabetes” while values >$6.4\%$ indicate a patient has T2DM86. Samples were stratified by patient HgA1c clinical thresholds into three groups: normal, prediabetes, and diabetes.
For each bulk-RNA-Seq dataset, TMM-normalized (Lung & Pancreas) or raw (MS) count data, gene annotations, and clinical metadata were integrated into a single annotated dataset object. No filtering was performed on genes or read counts, however read depths for raw counts were normalized to 10,000 per sample. Depth-normalized count data was then passed to UCD for deconvolution. Wilcoxon rank-sums test was used to determine differences in deconvolve cell type fractions between groups, with bonferroni correction for multiple testing.
## Primary non-small cell lung cancer data acquisition and preprocessing
Paired biopsies reflecting tumor and matched adjacent normal tissue were obtained from a patient with non-small cell lung cancer (NSCLC) undergoing surgical resection at the Mount Sinai Hospital (MSH) via the Mount Sinai Pathology Core. Samples were dissociated into single-cell suspensions using the Miltenyi Tumor Dissociation Kit [130-095-929] and the Miltenyi gentleMACS Dissociator [130-093-235]. Single cell suspensions were processed with the 10X Genomics Chromium Next GEM Single Cell 3' v3.1 kit (PN-1000121), targeting 10,000 loaded cells per sample. Whole-transcriptome sample libraries were sequenced on a NovaSeq 6000, targeting 50,000 reads per cell. Sequenced data was processed through CellRanger, yielding filtered count matrices for use as input into downstream single-cell data analysis using the python scanpy package. Both count matrices were concatenated into a single merged dataset. Briefly, cells with <2000 or >100,000 reads were filtered out, as well as cells that contained <200 or >30,000 unique genes. Cells with >$10\%$ mitochondrial gene fractions were assumed to be dead or damaged, and excluded from further analysis. Cell counts were normalized to 10,000 counts per cell, and subsequently, the effects of total counts, percent mitochondrial counts, and cell cycle score were regressed out. Regressed, normalized counts were then log-scaled and z-scored with a min-max of +/−10. Highly variable genes were identified on the basis of a dispersion score of 0.1 or greater for genes with log-normalized expression values between 0.1 and 20. HVGs were used to generate 75 principal components. At this stage, we performed batch correction using harmony, which outputs a corrected principal components array for use in all subsequent analysis steps. Calculation of nearest neighbors using our adjusted PCA vectors was done with n_neighbors set to 30. UMAP was used for final dimensionality reduction with minimum_distance set to 0.3. Leiden clustering was then performed to identify transcriptionally-related clusters, with resolution set to 1. Log-normalized counts were used as input into UCD to generate cell type prediction scores.
## Statistics and reproducibility
Unless otherwise noted, for all box plots depicted in this study the center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. For bar plots, bar heights correspond to the mean value of the population being visualized. Bar heights denote $95\%$ confidence interval (CI). Unless otherwise noted, p-values comparing distributions between groups across box or bar plots were calculated using unpaired two-sided Wilcoxon rank sum test, with Benjamini-Hochberg correction for multiple comparisons where appropriate.
For experimental single cell profiling of lung cancer tissue, the two samples were selected for processing and sequencing on the basis of cellular viability and minimal debris post-dissociation. No statistical methods were used to predetermine sample size for benchmarking, and all available samples were used as described and provided in the literature for each study. Reproducibility of the computational analysis presented in this manuscript is achieved through robust benchmarking, and public availability of both datasets and analysis code included in the supplementary software file. All attempts at replication and validation of the results presented were successful.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Reporting Summary Peer Review File Description of Additional Supplementary Files Supplementary Software The online version contains supplementary material available at 10.1038/s41467-023-36961-8.
## Source data
Source Data File
## Peer review information
Nature Communications thanks Ruishan Liu, Chao Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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title: Cryo-EM structures of human Cx36/GJD2 neuronal gap junction channel
authors:
- Seu-Na Lee
- Hwa-Jin Cho
- Hyeongseop Jeong
- Bumhan Ryu
- Hyuk-Joon Lee
- Minsoo Kim
- Jejoong Yoo
- Jae-Sung Woo
- Hyung Ho Lee
journal: Nature Communications
year: 2023
pmcid: PMC10008584
doi: 10.1038/s41467-023-37040-8
license: CC BY 4.0
---
# Cryo-EM structures of human Cx36/GJD2 neuronal gap junction channel
## Abstract
Connexin 36 (Cx36) is responsible for signal transmission in electrical synapses by forming interneuronal gap junctions. Despite the critical role of Cx36 in normal brain function, the molecular architecture of the Cx36 gap junction channel (GJC) is unknown. Here, we determine cryo-electron microscopy structures of Cx36 GJC at 2.2–3.6 Å resolutions, revealing a dynamic equilibrium between its closed and open states. In the closed state, channel pores are obstructed by lipids, while N-terminal helices (NTHs) are excluded from the pore. In the open state with pore-lining NTHs, the pore is more acidic than those in Cx26 and Cx$\frac{46}{50}$ GJCs, explaining its strong cation selectivity. The conformational change during channel opening also includes the α-to-π-helix transition of the first transmembrane helix, which weakens the protomer-protomer interaction. Our structural analyses provide high resolution information on the conformational flexibility of Cx36 GJC and suggest a potential role of lipids in the channel gating.
Connexin 36 (Cx36) gap junction channel is responsible for signal transmission in electrical synapses. Here, the authors determine cryo-EM structures of Cx36, providing insights into a potential role of lipids in the channel gating.
## Introduction
Intercellular signaling is an essential function of multicellular organisms and involves directly connecting two adjacent cells for cell-to-cell communication. The direct connection between adjacent cells is made by end-to-end docking of two hemichannels (termed connexons) from each cell, each consisting of six protomers, thereby forming a dodecameric gap junction channel (GJC)1. The GJCs are a family of integral membrane proteins, enabling direct exchange of electrical and small molecule signals such as ions, second messengers, hormones, and metabolites2. As a result, GJCs play key roles in numerous cellular processes, including synaptic electrical transmission, cardiac contraction, development, and differentiation. Vertebrate GJCs are formed by connexins, while invertebrate GJCs consist of innexins with no sequence identity to connexins. Vertebrate Pannexins have detectable sequence identity and structural similarity with innexins, but function as hemichannels connecting cytoplasmic and extracellular space3.
Twenty-one human connexin genes, except for the highly diversified connexin 23 (Cx23)/GJE1, share high sequence identity (50–$80\%$) in the region spanning four transmembrane (TM) helices and two extracellular loops (ECLs). However, cytoplasmic regions, including the N-terminal helix (NTH), cytoplasmic loop (CL), and C-terminal tail (CT), are quite diverse, suggesting that each GJC has its own specific functions mediated by these regions. Notably, it has been suggested that conformational changes in NTHs are critical for channel gating4,5. The gating and permeability of GJCs are regulated by voltage, pH, divalent ions, and membrane lipids6,7. Furthermore, GJCs can be dynamically regulated by isoform composition, assembly, disassembly, or post-translational modifications such as phosphorylation8,9. Therefore, it is crucial to understand the structural features of each GJC, to determine how they are differentially regulated in various cell types.
Cx36 is mainly expressed in neurons and forms the major component of GJCs in electrical synapses, playing important roles in cognitive functions, including memory consolidation and epileptogenesis10. It is also expressed in pancreatic β-cells, mediating insulin secretion9,11. The abolition of *Cx36* gene in mice largely disrupted the synchrony of agonist-induced supra- or subthreshold oscillations12,13, suggesting its pivotal role in brain function. The dysfunction of Cx36 is closely related to the acquired central nervous system (CNS) diseases, amyotrophic lateral sclerosis (ALS), and diabetes14–17. Therefore, understanding the molecular structure of Cx36 GJC is of biological and medical interest. Notably, Cx36 contributes to neuronal death following a range of acute brain insults such as ischemia, traumatic brain injury, and epilepsy, suggesting that specific blockers of Cx36 GJC might be useful for treating these pathological situations17. For example, in ALS, secondary neuronal death is extended by neuronal GJCs, and progressive neuronal death can be mitigated by blocking these channels17. However, since Cx36 is widely expressed in the nervous system and plays a role in the regulation of neuronal activity, inhibiting Cx36 GJCs could disrupt normal brain function and potentially worsen the symptoms of neurodegenerative disorders.
Although various structural and physiological studies of connexin GJCs have been performed, it remains unclear how the large pores of GJC are completely closed, and whether lipids are directly involved in the closing process. Analysis of the undocked hemichannel structure of Caenorhabditis elegans innexin-6 in lipid nanodiscs showed that flat double-layer densities obstruct the channel pore, suggesting that lipids can completely close the hemichannel18. A recently solved structure of the human pannexin 1 channel, which shares high structural homology with innexin hemichannels, showed pore-occlusion by phospholipids in the presence of a chemical inhibitor, probenecid19. However, there is no experimental evidence for a lipid-mediated closing model of connexin GJCs.
In this study, we determine eight structures of human Cx36 GJC in its pore-occluded and open states using single-particle cryo-electron microscopy (cryo-EM). In the pore-occluded state, the channel pores are filled with two layers of lipids, and the NTHs of Cx36 are dissociated from the pore. In comparison, the channel pore is completely open without any obstruction in the pore-lining NTH (PLN) state, suggesting that the binding of NTHs to TM1 and TM2 of the channel pore through the hydrophobic interaction is an essential step for channel opening. Extensive single-particle analyses and molecular dynamics (MD) simulations are used to investigate the structural dynamics and functional properties of Cx36 as a neuronal gap junction channel.
## Structure determination of Cx36 GJC at 2.2 Å resolution using the BRIL-fusion method
To understand the function of Cx36 GJC in electrical synapses, we conducted a structural study of Cx36 using cryo-EM. Wild-type Cx36 (Cx36-WT) proteins were solubilized in lauryl maltose neopentyl glycol (LMNG) and cholesterol hemisuccinate (CHS) and purified as dodecameric GJCs (Supplementary Fig. 1b). In the cryo-EM images, Cx36-WT GJC particles showed a highly preferred orientation (top view) (Supplementary Fig. 1c). This problem was difficult to solve by extensive screening of grid types, glow-discharge protocols, grid preparation methods, and sample buffer conditions. We attributed this to 12 long cytoplasmic loops (CLs) at both the top and bottom of Cx36 GJC. Approximately $41\%$ of the total residues in these CLs are hydrophobic (Supplementary Fig. 1e). We therefore reasoned that the CLs might prefer the hydrophobic air-water interface, facilitating the preferred orientation of the particles20.
Based on this hypothesis, we designed three Cx36 constructs by removing the CLs (residues 109–187) or their replacement with cytochrome b562RIL (BRIL; residues 21–128, Supplementary Fig. 1a) or T4 lysozyme (residues 21–128). Since BRIL and T4 lysozyme are highly soluble and their N- and C-termini are close to each other (~8 Å between the two ends), they could replace the CLs without disturbing the structural integrity of the TM helices, but significantly decrease the hydrophobicity of the cytosolic regions of Cx36 GJC. The three constructs were individually produced in insect cells, but only the BRIL-fused Cx36 (Cx36-BRIL) could be purified with sufficient yield for cryo-EM single-particle analysis. BRIL fusion indeed changed the behavior of Cx36 GJC particles in thin vitrified ice, and we obtained cryo-EM images with various particle orientations (Supplementary Fig. 1c). Using Cx36-BRIL, we determined the high-resolution cryo-EM structure of Cx36 GJC solubilized in LMNG (hereafter referred to as Cx36LMNG-BRIL) with D6 symmetry at 2.2 Å (Fig. 1a and Supplementary Tables 1 and 2). The cryo-EM structure revealed a dodecameric architecture of Cx36 subunits with overall dimensions of 90 Å × 90 Å × 140 Å (Fig. 1a). The overall structure and dodecameric interactions in Cx36 GJC were similar to those of Cx26 homomeric and Cx$\frac{46}{50}$ heteromeric GJCs, as expected from the high sequence identity (52–$54\%$) between these connexins21,22. The two hemichannels docked with each other through intermolecular interaction of ECLs, and water molecules were highly concentrated at the boundary between the TM helices and the ECLs (Fig. 1a, middle, red spheres). At the corresponding boundary of Cx31.3 hemichannel, a solvent tunnel was observed23. However, similar to available Cx26 and Cx$\frac{46}{50}$ GJC structures, the solvent tunnel was closed in Cx36 GJC by the interactions of Glu49, Arg77, Arg246, and Glu249 residues (Supplementary Fig. 2a).Fig. 1Overall structure and inter-hemichannel docking interface of human Cx36.a Cryo-EM reconstruction map and ribbon representation of Cx36LMNG-BRIL. The density and atomic model of Cx36, CHS, acyl chain, and water molecules are colored green, orange, gray, and red, respectively. The ambiguous densities of detergent micelles and BRIL are colored white. The size of each part of the protein (middle), the channel diameter, and the solvent-accessible pore diameter (right) are represented. b Two CHS densities and one acyl chain density covering the surface-exposed NTH-binding site at the channel entrance. c Comparison of the junctional docking regions of Cx36 and Cx26 (PDB code 2ZW3). EC extracellular space, CHS cholesteryl hemisuccinate, ECL extracellular loop, NTH N-terminal helix.
## Unique features of the hemichannel-hemichannel docking interface
Each protomer of Cx36 contains two ECLs that are responsible for hemichannel-hemichannel docking. Both ECLs (ECL1 and ECL2) are interconnected by three disulfide bonds (Cys55-Cys242, Cys62-Cys236, and Cys66-Cys231, Supplementary Fig. 2b). While the residues of ECL1 that participate in docking interactions are conserved in the connexin family, those of ECL2 includes two variable residues. These variable residues are together called the compatibility motif because two connexons with the same motif can dock together not only between identical but also different connexons24,25 (Fig. 1c). As depicted in Fig. 1c, the ECL2 of Cx26 docks through the interactions mediated by Lys168, Asn176, Thr177, and Asp179; however, Cx36 contains Lys238 and Glu239 in ECL2, instead of the K/R-N motif seen in Cx26 and Cx$\frac{46}{50}$ GJCs21,22 and the H-type of recently reported Cx43 GJC26. This motif is therefore Cx36-specific and leads to the formation of alternative intermolecular salt bridges in it. We reasoned that the salt bridges between Lys238 and Glu239 contributed to the tight docking of the Cx36 hemichannels. Indeed, the K238E mutation caused the dissociation of the Cx36 gap junction into the two hemichannels (Supplementary Fig. 1d).
## Cx36LMNG-BRIL has flexible NTHs and pore-bound detergents
We observed no Coulomb potential map for NTHs in Cx36LMNG-BRIL, which was also the case with the X-ray structure of Cx26 GJC in n-Decyl-β-D-Maltopyranoside (DM) detergents21 (Fig. 1a). Although another structure of Cx26 GJC in n-Undecyl-β-D-Maltopyranoside (UDM) detergents contains pore-lining NTHs, their weak electron densities indicate that they are mostly disordered or not strongly bound to transmembrane domains (TMDs)21. It is plausible that, when the amphipathic NTHs do not line the pore vestibule, a large hydrophobic surface is exposed to the solvent and masked by detergents present in the sample solution. Indeed, we observed substantial densities presumed to be CHS molecules (two CHS molecules per protomer) and acyl chains of LMNG or cellular lipids, which sufficiently covered the cylindrical hydrophobic surface of the pores (Fig. 1b). This suggests that detergents or lipids may contribute to the flexible NTH (hereafter referred to as FN) state of Cx36.
## Structures of Cx36 GJC in soybean lipids in two different conformations
To investigate the structure of Cx36 GJC in a lipid bilayer, we reconstituted wild-type Cx36 GJCs in lipid nanodiscs (hereafter referred to as Cx36Nano-WT) using the membrane scaffold protein 1 E1 (MSP1E1) and soybean polar lipid extract. The preferred orientation problem was partly solved by adding 50 mM phenylalanine to the GJC-nanodisc sample, and we obtained a cryo-EM consensus map of Cx36Nano-WT with D6 symmetry (Supplementary Fig. 3). In this map, we observed clear map densities of the NTHs lining the channel pores. However, the map densities of the NTH-TM1 linkers were not observed, and those of the cytoplasmic halves of the TM helices were very poor.
Because locally poor densities are usually caused by varying conformations of the corresponding region, we performed further 3D classification using the initial consensus map and solved two GJC structures refined with D6 symmetry in PLN and FN conformations at 3.05 and 3.16 Å resolutions, respectively (Supplementary Fig. 3). In the structure of the PLN state, the map densities of NTHs and TM helices were much clearer than those in the initial consensus map, and those of NTH-TM1 loops were also clearly observed, allowing us to build a reliable atomic model for all regions except those of CL and CT (Fig. 2b). In the FN state, the protein model including TM helices and ECLs was almost identical to those in the LMNG/CHS environment (Figs. 1a and 2a). However, unlike the structure in detergents, it showed no clear map densities of CHS molecules in the interior of the pore (Supplementary Fig. 6a), probably because the resolution was much lower and/or CHSs were mostly removed during nanodisc reconstitution. Fig. 2Structural comparison of Cx36 FN and PLN states in lipid nanodiscs.a, b Cryo-EM reconstruction map and ribbon representation of Cx36Nano-WT in the FN and PLN states, respectively. The acyl chains, nanodiscs, and NTHs, are colored in deem gray, white, and magenta, respectively. c Structure alignment of protomers of the FN and PLN states, colored in green and yellow, respectively (left). TM1 of the PLN state is bent towards the channel pore at ~6.7°, and NTH binds to the hydrophobic surface of TM1 (middle), mediated by the α-to-π structural transition (right). The π-helix is colored black. d Detailed interactions around TM1 of Cx36 in FN (green) and PLN states (yellow), and Cx50 in the PLN state (sky blue). The TM2 of the neighboring protomer is represented as a white ribbon. Phe33 of Cx36 and its corresponding residue in Cx50 (Phe32) are represented as cyan sticks.
## Unique structural features and hydrophobic pockets of Cx36 GJC in the PLN state
Compared with other human connexins, Cx36 has a longer NTH-TM1 loop (residues Ala13-Ser19) owing to the insertion of Ala14 (Supplementary Fig. 4). Although the amino acid sequence of the NTH-TM1 loop is not highly conserved in the connexin family, its length is strictly conserved with the exception of Cx36. In the available Cx43 and Cx$\frac{46}{50}$ structures, the corresponding loop is not flexible, suggesting that it may play an important role in maintaining the PLN conformation. Therefore, we investigated the structure of the NTH-TM1 loop of Cx36 to determine whether it affects the overall NTH conformation and, thus, the funnel-shaped vestibule structure formed by the six NTHs.
In the structure of the Cx36 PLN state (Cx36Nano-WT), the NTH-TM1 loop was likely stabilized by its close intramolecular interactions with TM2 and TM3. In particular, all carbon atoms of Gln17 interact with the phenyl ring of Phe93 from TM2 at a distance of 3.7–4.4 Å, while the amide group of Gln17 interacts with Gln202 from TM3 at a distance of ~3.8 Å in the hydrophobic environment formed by Phe93, Phe198, and Tyr199 (Fig. 3a, bottom box). Because Gln17 is a residue unique to Cx36, these interactions and the consequent loop structure are likely Cx36-specific (Supplementary Fig. 4).Fig. 3Unique structural features of the Cx36 PLN state.a–e Detailed structures of NTHs and the NTH-TM1 loops in Cx36 (a), Cx43 (b), Cx46 (c), and Cx50 (d), and their structural alignment (e). f–i Ribbon representation of two facing protomers in Cx36 (f), Cx43 (g), Cx46 (h), and Cx50 (i) hemichannel regions. NTHs and acyl chains in the lipid-binding pockets are represented as magenta ribbons and dark gray ball-and-chain models, respectively. The detailed interactions in box 1 (blue dashed line) and box 2 (red dashed line) are represented in the bottom panels. Compared with Cx43, Cx46, and Cx50, Cx36 has two unique structural features: hydrophobic pockets between two NTHs (f, box 1, green dotted circles) and an intermolecular salt bridge between two neighboring NTHs (f, box 2, black dotted line). The Cx36 Glu8, which forms an intermolecular salt bridge by interacting with Arg9, is colored green. Other neighboring protomers and the π-helix are shown as white and black ribbons, respectively.
In the structural comparison of the region from NTH to the following loop in Cx36, Cx43, and Cx$\frac{46}{50}$, we found that the Ala14 insertion in Cx36 not only slightly pushed the C-terminal region of NTH toward the pore center (Fig. 3e), but also caused ~90° rotation of the highly conserved Val15 (Fig. 3a–d, bottom box). This caused the C-terminal regions of NTHs to be loosely packed on TMDs and opened a large space surrounded by hydrophobic residues from TM1 and two adjacent NTHs (Fig. 3a, f, box 1). Notably, we identified two acyl chain densities that filled this space (Fig. 3f, box 1, 2), suggesting that a lipid molecule may contribute to the PLN conformation of Cx36. Val15 directly interacts with both acyl chains, whereas the corresponding residues of Cx43 (Val14), Cx46 (Ala14), and Cx50 (Val14) are involved in the tight intramolecular interaction with TM1 and TM2, and Gln15 (Cx43 and Cx46) and Asn15 (Cx50) are placed at the position corresponding to Val15 in Cx36 (Fig. 3f–i, box 1). Therefore, Cx43, Cx46, and Cx50 do not form a hydrophobic pocket at this location, suggesting that the lipid binding between NTHs in the PLN conformation may be a feature unique to Cx36.
Trp4, which is conserved in 18 human connexins, plays a major role in NTH-TMD interaction. In the Cx43, Cx46, and Cx50 GJC structures, the indole ring of Trp4 is stuck in a deep groove between two adjacent TM1s. Although Cx36 GJC has a similar hydrophobic groove for Trp4 binding, the exact binding site in the groove differs between these GJCs. Since Cx36 NTH is located ~2 Å further from the channel entrance than Cx43, Cx46, and Cx50 NTHs, Trp4 binds more closely to the residues (Ala39 and Ile40) at the 6th helical turn of TM1 than the 5th helical turn (Fig. 3e). Since Cx36 NTH is located slightly further from TM1 and closer to the pore center than Cx43, Cx46, and Cx50 NTHs in the PLN state, the overall volume of the pore lumen in Cx36 GJC is much smaller than those of Cx43 and Cx$\frac{46}{50}$ GJCs22,26 (Supplementary Fig. 5a, b). We also compared the NTH structure of Cx36 with that of Cx26 where the conserved tryptophan (Trp3) is exposed to the pore lumen21 (Supplementary Fig. 9g). Since the high-resolution structures of Cx36, Cx43, Cx46, and Cx50 commonly showed the binding of the conserved tryptophan to the groove between two adjacent TM1s, Cx26 would have a similar binding mode of Trp3 in its PLN conformation. Because the density map of Cx26 NTH was very weak, the structure of the N-terminus including Trp3 in Cx26 would have been difficult to be correctly modeled.
On the pore surface formed by six NTHs in a Cx36 connexon, each Glu8 residue forms a salt bridge with Arg9 on a neighboring NTH (Fig. 3f, box 2), thereby contributing to the stability of the PLN conformation. Although these residues are not conserved in most human connexins, Cx25 and Cx37 have Arg8/Asp9 and Glu8/Lys9 pairs, respectively, suggesting that their homomeric GJCs may have similar salt-bridge networks in the PLN state (Supplementary Fig. 4). Taken together, our structural analysis suggests that the longer NTH-TM1 loop affects the overall NTH conformation, reflecting Cx36-specific structural features.
## The structurally hetero-junctional Cx36Nano-WT GJC
The 3D classification of Cx36Nano-WT GJCs indicated only two different GJC conformations, in which both hemichannels were in the same state (PLN or FN) (Fig. 2). However, the conformations of the two opposing hemichannel regions are not strongly dependent on each other because the structures of the extracellular loops are nearly identical between the PLN and FN states. Therefore, we reasoned that the particles in a single Cx36Nano-WT sample might include conformationally hetero-junctional GJCs that contain two opposing hemichannel regions in different conformations.
To determine the structure of hetero-junctional GJC, we first processed the particle images with a focus on the hemichannel region. In the results of the 3D classification (Supplementary Fig. 3), three classes (classes 2, 6, and 7; referred to as PLN group) showed clear map densities for PLNs and an empty hole through the hemichannel region, whereas the other five classes (referred to as the FN group) exhibited neither PLN densities nor a central hole. We collected hemichannel particles (~$13\%$ of the total particles) in the FN group, traced them back to the original GJCs, and removed duplicates. This process led to the selection of 14,155 GJC particles with two hemichannel regions each belonging to the PLN and FN groups, 3D reconstruction of which with C6 symmetry produced a conformationally hetero-junctional GJC structure at 3.34 Å resolution. Compared with the GJC structures in the full PLN and FN states, the map density of the hemichannel region in the PLN state was weaker because of the smaller number of particles used for reconstruction, but that in the FN state was significantly improved in the cytoplasmic half of the TMD, as we expected. These analyses suggest that two opposing hemichannel regions are not structurally interdependent and may have different conformations. More importantly, the hetero-junctional GJC structure provides an avenue for analyzing the distinct conformations of NTHs in the same GJC.
It should be noted that the hetero-junctional GJC structure is not the main conformation in the Cx36Nano-WT GJC sample, but is the average of only $13\%$ selected particles, which are still conformationally heterogeneous. In addition, it is currently unclear which structure of Cx36 GJC represents the fully open state. Although the purified GJC sample was in the condition without transjunctional voltage where this channel showed the maximum conductance27, other conditions such as physiological membrane lipids may be needed to induce the fully open channel.
## The pore in the FN state is completely obstructed by lipids
In the hetero-junctional structure of Cx36Nano-WT GJC, the hemichannel in the FN state had two flat layers of density blobs blocking the pores around the cytoplasmic and extracellular ends of TM1 or TM2 (Fig. 4a and Supplementary Fig. 6a). These densities inside the pore were as strong as those of the lipids filling the nanodisc and were not observed in the other hemichannels in the PLN state. To avoid artifacts from C6 symmetry imposed during 3D reconstruction, we confirmed that the pore-obstructing densities were also present in the density map reconstructed without imposed symmetry. The features of the pore-obstructing densities, including double layers, flatness, and a thickness of ~4 nm, are highly consistent with those observed in the pore-occluded structures of the Innexin-6 hemichannel and pannexin 1 channel18,19, suggesting that Cx36 GJC in the FN state may also be obstructed by lipids. We also investigated the Cx36LMNG-BRIL structure and confirmed that the double layers of pore-occluding densities are consistent in the detergent environment (Supplementary Fig. 2f).Fig. 4Structural characterization of the pore-occluded state in lipid nanodiscs.a–c Top, cross-sectioned side, and bottom views of the cryo-EM reconstruction map with C1 symmetry imposition. Ribbon representations of the hetero-junctional Cx36Nano-WT (a), Cx36Nano-ΔN8 (b), and Cx36Nano-BRIL-ΔN16 (c) GJCs are also shown. C.L. and E.L. denote cytoplasmic layer and extracellular layer, respectively. The lipid nanodiscs and pore-occluding lipids are displayed as white densities. Cx36 proteins in the FN and PLN states are colored green and yellow, respectively. The NTHs and the π-helix spanning residues 30–33 are colored magenta and black, respectively. Red dotted circles indicate possible locations of flexible NTHs.
To investigate the possibility that the pore-obstructing densities correspond to flexible NTHs and CLs, we performed a structural study on two Cx36 mutants: Cx36Nano-ΔN8, in which 7 N-terminal residues (residues 2–8) are deleted, and Cx36Nano-BRIL-ΔN16, in which 15 N-terminal residues (residues 2–16) are deleted and the CL is replaced with BRIL. The cryo-EM maps of mutant Cx36 GJCs reconstructed with and without symmetry imposition showed similar pore-occluding densities (Fig. 4b, c and Supplementary Fig. 6b, c), indicating that the channel pore is obstructed in the absence of NTHs and/or CLs, likely by lipids. However, in Cx36-WT GJC, flexible protein domains such as NTH, CL, or CT may partly contribute to the cytoplasmic layer of the pore-occluding densities. Especially, amphipathic NTHs are highly possible to interact with the surface of the cytoplasmic lipid layer.
## The acidic pore surface of Cx36 GJC confers strong cation selectivity
Previous electrophysiological studies have shown that Cx36 GJC preferentially transfers small cationic molecules with a diameter of <10 Å, such as ethidium bromide (net charge +1), EAM-1 (net charge +1), and EAM-2 (net charge +1) fluorescence dyes28,29. We showed that Cx36 GJC in the PLN state has a pore with a solvent-accessible diameter of ~8.5 Å, which is sufficient for the passage of these dye molecules as well as hydrated cations such as K+ (~6.6 Å), Na+ (~7.2 Å), and Ca2+ (~8.2 Å)30. Therefore, the PLN conformation likely represents the open state of the channel.
To understand the mechanism underlying cation selectivity, we first analyzed the surface charge distribution of Cx36 GJC. We found two acidic surface bands formed by six NTHs (the cytoplasmic acidic band) and the C-terminal regions of six TM1s (the extracellular acidic band), respectively. The cytoplasmic acidic band is composed of Glu3, Glu8, and Glu12, but the acidity of Glu8 is compromised by Arg9 (Fig. 3f and Supplementary Fig. 5c). While Glu3 and Glu12 are conserved in the majority of human connexins including Cx46 and Cx50, Glu8 is not conserved at all (Supplementary Fig. 4). The extracellular acidic band is composed of Asp47, Asp48, and Glu49, and its acidity is stronger than that of the cytoplasmic band because of the absence of surface-exposed basic residues in this region (Supplementary Fig. 5c). Although Asp48 and Glu49 are strictly conserved in the human connexin family, Asp47 is conserved only in Cx31.9, Cx62, and Cx30 (Supplementary Fig. 4).
Next, to determine how the pore surface properties affect ion selectivity, we performed MD simulations of this channel in two lipid bilayers containing 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) and 150 mM NaCl. Since 12 CLs are considerably long and located close to the channel entrance and exit, they may prevent ions from accessing the entrance and escaping at the exit, resulting in a substantially reduced ion-transfer rate. Therefore, we tested two models of Cx36 GJC in the PLN state with and without the CL, respectively. We used the CL model predicted by Alphafold, which mostly consists of unstructured loop regions. Six CLs in each hemichannel region were highly flexible during the simulation and formed large pores/gaps for ions to freely diffuse through.
In the 1.2 μs simulation without transmembrane potential for the channel with the CL, Na+ ions gathered at the two extracellular acidic bands, increasing the local concentration to >2 M (Supplementary Fig. 7a), while few Cl− ions (~0.03 M) were found inside the pore (Supplementary Fig. 7b). This suggests that the diffusion of anions through these pores is very limited because of the acidic bands. When we applied a transjunctional potential of 200 mV, the Na+ current was 0.007 nA with maximum flux at the extracellular acidic band on the cathode side (Supplementary Fig. 7c), whereas the Cl− current was undetectable (Supplementary Fig. 7d). Therefore, the single-channel conductance in this simulation was 35 pS, which was higher than the experimental conductance of 5–15 pS31,32. This discrepancy may be because the viscosity of the standard water model used in this simulation is approximately $30\%$ of the experimental viscosity33–35, resulting in diffusion coefficients and currents overestimated by a factor of three. For example, the current values computed using similar MD simulation methods showed a deviation from the experimental value by a factor of two to three34,36,37. If this also applies to the above simulation of Cx36 GJC, the calculated conductance changes to ~12 pS, which is within the range of the experimental one.
In contrast, the 0.6 μs simulation for Cx36 GJC without the CL showed the Na+- and Cl– currents of 0.023 nA and 0.001 nA, respectively, resulting in the conductance of 120 pS (Supplementary Fig. 7i, j). This data confirms that the strong cation selectivity is caused by the pore surface property, not by the CL, and suggests that the CL might be able to regulate ion permeability independently of the gating regulation by NTHs and lipids. However, these data need to be carefully interpreted because our MD simulations were not based on the exact information of the structural dynamics and intermolecular/intramolecular interactions of six CLs in each hemichannel region.
Taken together, these data confirm the strong cation selectivity of Cx36 GJC shown in previous electrophysiological studies and provide mechanistic insights into the process of ion transfer through the channel. However, the experimentally determined conductance (5–15 pS) could not be well explained by our structures and MD simulations in the current state where the CL structure and dynamics are unknown.
## Dynamic conformational changes in individual protomers in a single Cx36 GJC
Recently solved structures of Cx43 GJC showed that 12 protomers in a single channel underwent independent conformational changes, resulting in structurally diverse GJC particles in the protein sample26. To determine whether a single Cx36 GJC also contains conformationally heterogeneous protomers, we performed single-subunit-focused 3D classification (see “Methods” for details) using the initial consensus map from the Cx36Nano-WT dataset (Supplementary Fig. 8a)38,39. The results showed that ~$43\%$ of the protomers were in the FN conformation (FN protomers) (Supplementary Fig. 8a; classes 5, 6, and 8) and ~$57\%$ were in the PLN conformation (PLN protomers) (Supplementary Fig. 8a; class 1–4 and 7). Next, we traced back the protomers in the PLN conformation (referred to as PLN protomers) to the GJC particles to which they originally belonged, and investigated the distribution of PLN protomers in each GJC particle. We found that Cx36 GJCs (85,080 particles) had various FN:PLN compositions ranging from 0:12 (the full PLN state, ~$0.4\%$) to 12:0 (the full FN state, ~$0.05\%$) (Supplementary Fig. 8b). Consistent with the previous Cx43 GJC structures, PLN protomers in the Cx36 GJC structure showed a normal distribution, suggesting that they are randomly distributed throughout GJC particles (Supplementary Fig. 8b). We also analyzed the positional distribution of the PLN protomers in a hemichannel region and compared the percentages of all possible PLN/FN compositions with the predicted values when they were randomly distributed (Supplementary Fig. 8c). The result showed that the experimental and predicted values were not significantly different. Thus, we concluded that the conformational changes in individual protomers are not strongly affected by the conformations of neighboring protomers in a hemichannel region. However, we cannot exclude the possibility of the structural interdependency between neighboring PLNs caused by the intermolecular salt bridges between neighboring Glu8 and Arg9 residues, which may be too weak to be identified by our analyses with the current dataset.
It should be noted that one FN class showed a putative NTH density of which position and orientation are similar to those of gate-covering NTHs (GCNs) shown in Cx31.3 and Cx43 structures (Supplementary Fig. 8d). This was unexpected because the hydrophobic residues of TM2 to maintain the GCN conformation in Cx43 (Tyr92, Leu93, Val96, Phe97, and Met100) and Cx31.3 (Thr95, Leu96, Val99, Ile100, and Trp103) were not conserved in Cx36 (Supplementary Fig. 4). We performed hemichannel-focused 3D classification with C6 symmetry imposition and identified one hemichannel class with the GCN-like densities. However, the refined 3D map did not show the map densities of NTHs that are sufficiently clear to build the structural model (Supplementary Fig. 8d). The unclear NTH densities would be primarily because substantial noises from PLN and FN protomers were included in the final 3D map. In addition, NTHs in this conformation might be still flexible due to weak interaction with TM2 and lie on the intracellular layer of the pore-occluding lipids.
While the protomers in Cx36Nano-WT GJCs mainly showed the PLN conformation ($57\%$), those in Cx36LMNG-BRIL GJCs were completely in the FN conformation (Supplementary Fig. 10). We were curious whether the shift of the structural equilibrium is caused by the change in lipid/detergent environments or the replacement of CL with BRIL. Thus, we performed cryo-EM experiments of Cx36-WT in LMNG/CHS (Cx36LMNG-WT) and Cx36-BRIL in soybean lipid nanodiscs (Cx36Nano-BRIL) and obtained 3.2 Å and 3.4 Å consensus maps, respectively. Next, we conducted protomer-focused 3D classification to understand the structural equilibrium in each GJC sample and found that Cx36LMNG-WT and Cx36Nano-BRIL GJCs showed the FN/PLN ratios of 71:29 and 12:88, respectively.
While Cx36-WT and Cx36-BRIL in soybean lipids were mainly in the PLN conformation (Supplementary Fig. 10), those in detergents were mainly or completely in the FN conformation, suggesting that detergents contributed to the PLN-to-FN transition more than lipids. Since the GJC pore in the full FN conformation is funnel-shaped, detergents with large head groups may be more tightly packed at the intracellular layer in the pore, resulting in increased FN protomer populations. However, although BRIL fusion had a considerable effect on the PLN-FN equilibrium in GJC, the role of CL or BRIL is still unclear because the replacement of CL with BRIL had different effects on the structural equilibrium of GJC in detergents and lipids: increasing and decreasing the FN/PLN ratio, respectively.
## Structural comparison of Cx36 FN and PLN protomers reveals α-to-π helix transition in TM1
In the structure of Cx36Nano-WT GJC in the full PLN state, we identified two π-helices in the TM1 of each protomer, consisting of highly conserved residues (Supplementary Fig. 9b). Although they have been previously observed in the Cx43 and Cx$\frac{46}{50}$ GJC (Fig. 2d and Supplementary Fig. 9d, f), their functions are not yet clearly understood. While the typical α-helix is characterized by main chain hydrogen bonds between residues that are set four residues apart in the sequence (i + 4), the π-helix has an additional amino acid per helical turn (i + 5), resulting in a “bulge” structure40. When we compared the structures of TM1s in Cx36 PLN and FN protomers, one π-helix at the C-terminus of TM1 39–42 was observed in both protomer states (Supplementary Fig. 9a, b). This π-helix is also found in currently available connexin structures such as Cx26, Cx31.3, Cx43, and Cx$\frac{46}{50}$ (Supplementary Fig. 9a–f, cyan)4,22,23,26, suggesting that it may play an important structural role in maintaining a 30.8° kink in TM1. However, the other π-helix in the middle of TM1 (residues 30–33) was only observed in the PLN protomer, and the corresponding region was α-helical in the FN protomer, indicating that this region undergoes an α-to-π-helix transition during the conformational change of NTH from FN to PLN state (Fig. 2c and Supplementary Fig. 9a–d).
The α-to-π helix transition in the middle of TM1 causes a slight bending (~6.7°) of TM1 toward the channel pore, and a large helical rotation (~65°) in the cytoplasmic half of TM1, including residues Thr20 to Phe33 (Fig. 2c and Supplementary Fig. 9a, b). This results in significant changes in the interactions between TM1 and other TM helices. First, the tight intermolecular interaction of Phe33 in TM1 with Phe81 and Ile84 in TM2 is loosened, while new intramolecular interactions with Val29, Ile32, and Val260 are established. Second, Arg34 moves slightly towards Glu210 in TM3 (Supplementary Fig. 9a, b, cross-section 1). The ionic interaction between these two residues in the hydrophobic core of TMD is the only strong interaction between TM1 and TM3, and is thus thought to be crucial for the structural integrity of TMD. Therefore, the dislocation of Arg34 during the α-to-π-helix transition may increase structural stability via a closer interaction with Glu210 of TM3. Third, Ile32, which is completely exposed to the channel pore in the FN state, moves to interact with Phe33 of TM1 and Leu91 of TM2 in the adjacent protomer (Supplementary Fig. 9a, b, cross-section 1). Fourth, the intermolecular salt bridge between Arg24 of TM1 and Glu271 of TM4 is broken (Supplementary Fig. 9a, cross-section 2) and an intermolecular hydrogen bond between Thr28 of TM1 and Thr95 of TM2 is formed (Supplementary Fig. 9b, cross-section 1). Residues 30–33 of Cx36 are highly conserved in the connexin family, and the corresponding residues of Cx43 have also been found to undergo an α-to-π-helix transition (Supplementary Fig. 9c, d, cross-section 1)26. Therefore, we concluded that the transition and consequent structural changes in TMD might be a common feature of all connexin homologs.
## Discussion
Cx36 GJC mediates ionic transmission through vertebrate electrical synapses, which collaborate with chemical synapses to dynamically shape brain function41. Like chemical synapses, electrical synapses are plastic, and their modifications reconfigure the neural circuits42. The strength of the electrical connection varies between different neurons and on distinct timescales from milliseconds to days43. In a single synapse, the strength can be regulated not only by changing the total number of GJCs with a quick turnover rate of ~3 h, but also by modulating the ratio of open and closed GJCs or the probability of each GJC being open44. Known regulatory mechanisms include inhibition by Mg2+ and phosphorylation, and the combined role of calcium, calmodulin, and Ca2+/calmodulin-dependent protein kinase II8,14,25. In this study, the structural analysis of Cx36Nano-WT GJC indicated a distinct gating mechanism that involved the possible role of membrane lipids.
Membrane lipids are currently not considered direct regulators of GJC gating, probably because previous structural studies on GJCs have not clearly identified lipids inside the channel pore. In addition, the direct role of a specific lipid is difficult to study at the cell or tissue level, since it requires the delivery of insoluble molecules into the cell membrane or the channel without using detergents. Based on previous studies22,45, it can be concluded that NTHs strongly bind to TMDs, forming a hydrophilic pore; thus, membrane lipids cannot enter the pore. However, the structures of Cx36 GJC in lipid nanodiscs in this study revealed two different conformations (PLN and FN) of this channel (Fig. 2), corresponding to the open and closed states, respectively. Because these structures were produced from a single grid sample, we believe that we captured the equilibrium between the two states. This was further supported by the identification of intermediate states with various combinations of the two different protomer conformations in a single channel (Supplementary Fig. 8). Importantly, in the FN hemichannel of the hetero-junctional GJC structure (Fig. 4a), the channel pore was completely obstructed by the cytoplasmic and extracellular layers of lipids, whereas the channel pore was open in the PLN hemichannel region. These structural data suggest that, at least in soybean lipids, Cx36 is conformationally flexible, and lipids can dynamically move into and out of the channel. However, we cannot completely exclude the possibility that the pore-occluding lipids in the FN conformation were artificially introduced during the cryo-EM sample preparation, While we consistently observed the conformational equilibrium of NTHs in the structures of Cx36LMNG-WT, Cx36Nano-WT, and Cx36Nano-BRIL GJCs, only the FN conformation was observed in Cx36LMNG-BRIL GJC, indicating that the equilibrium completely shifted to FN. When we examined the inner surface of the channel pore of Cx36LMNG-BRIL GJC, strong map densities were observed for the acyl chains of LMNG, but not the head group, and for the sterol ring of CHS, but not the succinyl group. These data suggest that a specific lipid environment with high cholesterol content might induce the FN conformation of the channel, leading to the complete obstruction of the pore. A previous electrophysiological study using HeLa cells showed that most Cx36 GJCs (>$99\%$) were closed46. In this context, judging by our structural data, the closed channels might be mostly in the FN conformation, obstructed by membrane lipids. However, this does not exclude the possibility that the channels are in the PLN state and mostly plugged by CL or other regulatory proteins. For example, the channel closing by Ca2+-loaded calmodulin has been extensively studied, and the calmodulin-cork model has been proposed47, although this model needs to be confirmed by structural studies in the future. In addition, direct pore-plugging by NTHs was recently observed in Cx26 GJC at acidic pH5. Since Cx26 GJC was reconstituted in amphipol A8-35, the role of detergents or lipids was likely excluded.
This study raises several interesting questions. First, it is still unclear whether the Cx36 gap junctions in physiological cell membranes (e.g., cryo-frozen electrical synapses) contain GJCs in both PLN and FN conformations, and whether the ratio of the two conformations is related to the strength of the electrical synapse. Investigating this would require the advancements in in situ electron cryotomography (cryo-ET) and sub-tomogram averaging technologies.
Second, it is unclear how lipids diffuse into and out of channel pores. The Innexin-6 hemichannel structure shows a large gap between the protomers through which the lipids in the inner leaflet can pass18. Consistent with this, the recently solved structures of Cx43 GJC showed that a small but sufficiently large gap for lateral lipid transfer is created during conformational change26. Although the corresponding gap is much smaller in Cx36 GJC, we observed much weaker intermolecular interaction at the gap in the PLN state than in the FN state (Fig. 2d). Since the independent conformational transition of each protomer in Cx36 GJC is possible, there may be an intermediate conformation with a larger gap, which we have not been able to classify from our data yet. Interestingly, we have observed top views of either tetradecameric GJCs or heptameric hemichannels in all the datasets collected in this study (Supplementary Fig. 1f). Although we cannot exclude the possibility that these channels are artificially created during protein purification or nanodisc reconstitution, the fact that they were commonly observed in both detergent and lipid environments suggests that they might be formed in various detergent/lipid environments including cell membranes. This observation also suggests that Cx36 GJC or hemichannel might be interchangeable between two different assemblies with six- and sevenfold symmetries. The interchange would require a temporary disruption of the channel assemblies, which may allow the diffusion of lipids into and out of channel pores (Supplementary Fig. 11). This idea needs to be validated by cryo-ET or single-molecule experiments.
Third, we identified a unique hydrophobic pocket between the NTHs in Cx36 GJC in the PLN state (Fig. 3f, box 1). Although two acyl chains bound to the pocket likely contribute to the structural stability of the PLN conformation, it is unclear whether this interaction is crucial for channel opening. There is also a strong possibility that other hydrophobic signaling molecules may strongly bind to this pocket to increase the probability of Cx36 GJC opening (Fig. 5).Fig. 5Overall model and unique structural features of Cx36.Schematic representation of Cx36 in the FN and PLN states. TM1 in the FN and PLN states, π-helix spanning residues 30–33, and NTH, are colored green, yellow, black, and magenta, respectively. The phospholipids, cations, and surface-exposed Asp47 at the ECL1 of the acidic band, are represented by the tailed gray, blue, and red circles, respectively. The flexible NTH of the FN state is outlined by a light-gray dashed line. The channel pore of Cx36 in the FN state is occluded by membrane components, whereas that of Cx36 in the PLN state is open. Some lipid molecules are bound to the hydrophobic pockets (dashed line) around the NTHs in the PLN state. Cx36 undergoes a conformational change from the FN to PLN state via the α-to-π transition in TM1.
Fourth, it is unclear whether the conformational change of Cx36 GJC discovered in this study is involved in its voltage-gating mechanism. Since the charged residues in NTHs function as voltage sensors44,48–50, the transjunctional voltage likely induces the conformational transition of NTHs. Because two hemichannel regions in a GJC face each other, their responses to the applied transjunctional voltage would be opposite to each other. In the case of Cx36 GJC, the full PLN conformation might be formed in one hemichannel region and the full FN in the other, and vice versa, resulting in the channel closing in both cases. Amphipathic NTHs in the FN state might not be totally out of the pore pathway but lie on the intracellular layer of the pore-occluding lipids so that they can sense the voltage field. GJCs generally show the maximum conductance when the transjunctional voltage difference tends to zero, and our experimental conditions for the structural study have no transjunctional voltage. Therefore, although Cx36 GJC in the full PLN conformation showed substantial ionic current and cation selectivity in our MD simulation, the conformationally dynamic state of Cx36 GJC might form a larger pore and show maximum conductance. For example, a hemichannel region with four PLN protomers and two consecutive FN protomers without lipids or with highly mobile lipids has a pore substantially bigger than that with only PLN protomers. Therefore, the frequent conformational change of one or two NTHs in the full PLN conformation could result in an ion-transfer rate higher than no conformational change. Alternatively, the full PLN state might be the maximum conductance state, and its maintenance might require other factors that are abundant in cells but not included in our experimental system. A specific membrane lipid or amphipathic molecule might strongly bind to the pocket between neighboring NTHs in the PLN conformation and stabilize the conformation.
The dynamic conformational change observed in Cx36 GJC, as previously shown in Cx43 GJC, may help large molecules pass through the channels. Since a pore with a diameter of ~12 Å can be formed during the dynamic conformation change (Supplementary Fig. 12), large cellular metabolites such as ATP or dinucleotides might pass through the channel more efficiently in the structural equilibrium state than in the full PLN state. The binding affinity of NTH for TMD to form the PLN conformation may have been optimized to maintain the structural equilibrium in the cell membrane for ease of the gating regulation. Therefore, the binding of specific signal molecules could shift the equilibrium to the full PLN or FN state for the channel opening or closing. However, the hypothesis of thermodynamic equilibrium interconnecting different states of Cx36 GJC, particularly considering the role of lipids as structural mediators of channel blocking, remains to be validated by functional experiments. Unfortunately, there is no method to measure the conformational state of GJCs during a patch clamp recording. In addition, it is difficult to design mutational studies for the measurement of ionic current in a specific channel conformation. Any mutation to fix NTH or TM1 in a specific conformation would inhibit the conformational dynamics of the channel, which may be crucial for lipid exclusion at its initial assembly. To prove this hypothesis, further structural and electrophysiological studies are needed to find molecules or conditions that greatly shift the conformational equilibrium of the channel to the FN and PLN states, respectively, and investigate their effects on the ionic current through the channel.
## Expression and purification of Cx36-WT and Cx36-ΔN8 in the human cell expression system
A synthetic gene fragment encoding the full-length human Cx36 (GJD2) was purchased from Integrated DNA Technologies and inserted into pX plasmid vector as previously reported51. The Cx36-WT gene was expressed in transiently transfected HEK293E cells as a fusion protein C-terminally connected with a human rhinovirus (HRV) 3 C cleavage site, an enhanced yellow fluorescence protein (eYFP) tag, 10×His-tag, and Rho-1D4 epitope tag (8 amino acids of TETSQVAPA). The nucleotide sequences of the primers used for molecular cloning are listed in Supplementary Table 3. The pXY-Cx36-WT plasmid was transfected using 25-kDa linear polyethylenimine (Polysciences) into HEK293E cells, which were grown at 37 °C in suspension in Dulbecco’s modified Eagle’s medium (DMEM) with glucose (4500 mg/l) without calcium (WELGENE) supplemented with $5\%$ fetal bovine serum (FBS). Dimethyl sulfoxide (Amresco) was added immediately after the transfection to a final concentration of $1\%$, and the temperature was lowered to 33 °C. At 48 h after transfection, tryptone (Amresco) was added to a final concentration of $0.5\%$. At 96 hours after transfection, the cells were centrifugated at 500×g for 20 min.
The harvested cells were resuspended in a buffer A [20 mM CAPS pH 10.5, 500 mM KCl, and 2 mM β-mercaptoethanol] supplemented with $10\%$ glycerol and 1 mM phenylmethylsulfonyl fluoride (PMSF) and lysed using a Dounce homogenizer (Bellco) with a tight (B) pestle (25–30 strokes). The membrane fraction was isolated by high-speed centrifugation at 42,600×g for 1 h. The membrane pellet was resuspended using a WiseTis homogenizer (Daihan Scientific Co., Ltd.) in 50 ml buffer A supplemented with 1 mM PMSF and 0.5/$0.05\%$ (w/v) LMNG/CHS (Anatrace). After incubation for 2 h with slow rotation, the sample was mixed with 2.5 ml neutralization buffer containing 1 M Tris (pH 7.0) to lower the sample pH to ~7.5 and centrifugated at 42,600×g for 1 h. The supernatant was mixed with adipic acid dihydrazide-agarose resin (Sigma) conjugated with Rho-1D4 antibody (University of British Columbia) in an open column (Bio-Rad) and incubated with slow rotation at 4 °C for 1 h. The resins were settled down in the column and washed twice with 10 column volumes (CVs) of buffer B [20 mM HEPES pH 7.5, 500 mM KCl, 2 mM β-mercaptoethanol] supplemented with 0.005/$0.0005\%$ (w/v) LMNG/CHS. The bound proteins were incubated at 4 °C overnight with the addition of excess HRV 3 C protease (~0.25 mg) to remove the C-terminal eYFP-Rho-1D4 tag from Cx36 and eluted from the resin. The eluted Cx36 proteins were concentrated and further purified using Superose 6 Increase $\frac{10}{300}$ column (Cytiva) equilibrated with a buffer B. Peak fractions were pooled, concentrated to ~2 mg/ml, flash-frozen in liquid nitrogen, and stored at −80 °C for nanodisc reconstitution and EM grid preparation. Protein purity and quality were assessed by SDS-polyacrylamide gel electrophoresis (SDS-PAGE).
The Cx36-ΔN8 construct was created by polymerase chain reaction (PCR) using pXY-Cx36-WT as a template. The resulting plasmid pXY-Cx36-ΔN8 was transfected into HEK293E cells, and the mutant Cx36 was expressed and purified using the same protocol as for Cx36-WT.
## Expression and purification of Cx36-BRIL and Cx36-BRIL-ΔN16 in the baculovirus expression system
The full-length *Cx36* gene was subcloned into pEG BacMam expression vector to produce pEG-Cx3652, which was designed to express Cx36 as a fusion protein with the C-terminal eYFP and FLAG tags (8 amino acids of DYKDDDDK) or only with the FLAG tag. This plasmid was further engineered to create pEG-Cx36-BRIL through the deletion of the CL region (residues 109–187) by PCR and the insertion of the cytochrome b562RIL gene fragment (BRIL; residues 21–128) by the conventional enzymatic DNA assembly method53. E. coli DH10Bac strain (Gibco, cat #10361012) was transformed with pEG-Cx36-BRIL to produce Cx36-BRIL Bacmid, which was transfected into *Spodoptera frugiperda* (Sf9) to produce baculovirus containing the Cx36-BRIL expression cassette, according to manufacturer’s instructions. Human embryonic kidney (HEK) 293E cells and *Spodoptera frugiperda* (Sf9) cells were obtained from ATCC (CRL10852 and CRL-1711).
Sf9 cells were grown at 27 °C in suspension in ESF293 medium (Expression Systems) supplemented with 0.06 mg/ml penicillin G and 0.1 mg/ml streptomycin (Sigma). At 72 h after infection, the cells were centrifugated at 500×g for 10 min. The membrane was solubilized with buffer C [20 mM HEPES pH 7.5 and 200 mM NaCl] supplemented with 5 mM ethylenediaminetetraacetic acid (EDTA), protease inhibitors (1 mM PMSF, 2 μg/ml leupeptin, 2 μM pepstatin A, and 2 μM aprotinin), and $1\%$ (w/v) LMNG for 2 h at 4 °C. The insoluble fraction was removed by high-speed centrifugation at 100,000×g for 1 h. The soluble fraction was twofold diluted with buffer C supplemented with $0.01\%$ LMNG and mixed with monoclonal anti-FLAG antibody agarose beads (Wako chemicals, cat #016-22784). The mixture was incubated with slow rotation at 4 °C for 5 h. The resins were settled down in the column and washed three times with 10 CVs of buffer D [20 mM HEPES pH 7.5, 200 mM NaCl, $0.01\%$ LMNG, and $0.001\%$ CHS]. The bound proteins were eluted with buffer D supplemented with 450 μg/ml FLAG peptide (sigma) at 4 °C overnight. The eluates were concentrated and further purified using Superose 6 Increase $\frac{10}{300}$ column equilibrated with buffer D. Peak fractions were pooled, concentrated to ~2 mg/ml, flash-frozen in liquid nitrogen, and stored at −80 °C for EM grid preparation.
The Cx36-BRIL-ΔN16 construct was created by PCR using pEG-Cx36-BRIL as a template. The resulting plasmid pEG-Cx36-BRIL-ΔN16 was used for Bacmid and baculovirus production. The mutant Cx36 was expressed and purified, using the same protocol as for Cx36-BRIL.
## Reconstitution of Cx36-WT, Cx36-ΔN8, and Cx36-BRIL-ΔN16 in lipid nanodiscs
Purified Cx36-WT, Cx36-ΔN8, and Cx36-BRIL-ΔN16 proteins were reconstituted into membrane scaffold protein (MSP1E1) nanodiscs containing soybean polar lipids extract. Soybean lipid extract powder (Avanti), mainly composed of phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, and phosphatidic acid was solubilized in 5/$0.5\%$ (w/v) LMNG/CHS and incubated at room temperature for overnight to make a clear lipid stock solution in ~10 mg/ml. The pET28a plasmid containing MSP1E1 gene was obtained from Addgene (plasmid #20062). The membrane scaffold protein (MSP1E1) was expressed and purified as previously described54. The purified Cx36 sample was mixed with the soybean polar lipid extract stock (~10 mg/ml) at the molar ratio of Cx36 to lipids of 1:100 and incubated at 4 °C for 1 h. Then, the purified MSP1E1 protein was added to the final molar ratio of Cx36:MSP1E1:lipids of 1:0.5:100. The mixture was incubated with slow rotation at 4 ˚C for 30 min. For the removal of detergents and the protein-nanodisc reconstruction, Bio-Beads SM2 resin (Bio-Rad, resin 100 mg) was pre-washed with buffer E [20 mM HEPES pH 7.5, 250 mM NaCl, and 2 mM β-mercaptoethanol] and added to the Cx36-lipid-MSP mixture. After 4 h with gentle rotation, the supernatant was collected, and another round of detergent removal was done with the pre-washed 100 mg Bio-Beads SM2 resin. The mixture was incubated overnight at 4 °C with gentle rotation. To remove insoluble particles, the supernatant was filtered through a membrane with a pore diameter of 0.22 μm (Millipore). The filtered sample was further purified by size-exclusion chromatography using Superose 6 Increase $\frac{10}{300}$ column equilibrated with buffer E for Cx36-WT and Cx36-ΔN8 or buffer C for Cx36-BRIL-ΔN16. Fractions containing both Cx36 and MSP1E1 were pooled, concentrated to ~2 mg/ml, flash-frozen in liquid nitrogen, and stored at −80 °C for EM grid preparation. Protein purity and quality were assessed by SDS-PAGE.
## Fluorescence-detection size-exclusion chromatography
The oligomerization state of Cx36 was evaluated by fluorescence-detection size-exclusion chromatography (FSEC)55. C-terminally eYFP-tagged Cx36-WT and Cx36-K238E were expressed as described above and solubilized with buffer C supplemented with 5 mM EDTA, protease inhibitors, and $1\%$ (w/v) LMNG for 2 h at 4 ˚C. The insoluble fraction was removed by high-speed centrifugation at 100,000×g for 1 h. The soluble fraction was loaded onto Superose 6 increase $\frac{5}{150}$ GL size-exclusion column, and the fluorescence signal was monitored by a fluorescence detector (Agilent Technologies, cat #DEAEJ00102).
## Cryo-EM specimen preparation and data collection
Three microliters of purified Cx36 proteins (1–2 mg/ml) in detergents or nanodiscs were applied onto a negatively glow-discharged (15 mA current, 60 s) holey carbon grid (Quantifoil R$\frac{1.2}{1.3}$ Cu 200 mesh). In case of Cx36LMNG-WT and Cx36Nano-WT, to improve orientation diversity, phenylalanine was added at the final concentration of 50 mM. The grid was blotted and plunge-frozen in liquid ethane using Vitrobot Mark IV (ThermoFisher Scientific, USA) at 4 °C and $100\%$ humidity. Cryo-EM images were collected at Institute for Basic Science (IBS) and Korea Basic Science Institute (KBSI), using Krios G4 (ThermoFisher Scientific;TFS, USA) equipped with BioQuantum K3 detector (Gatan Inc, USA) and Titan Krios G2 (FEI, USA) with Falcon 3EC detector, respectively. Automated data acquisition was performed in electron counting mode using EPU software (TFS, USA). More details are described in Supplementary Table 2.
## Image processing and reconstruction
The cryo-EM image processing was performed with cryoSPARC version 3.156 or Relion 3.1 softwares57 (Supplementary Fig. 13). The Cx36LMNG-WT dataset was processed using cryoSPARC (v.3.1.0.). Patch-based pre-processing (Patch motion correction & Patch CTF estimation) was performed for the dataset containing 3780 movies. Next, 609,292 particles picked by reference-based auto-picking were extracted into 360-pixel boxes. After three rounds of 2D classification, good particles were re-extracted into 512-pixel boxes and subjected to two rounds of 2D classification. Finally, 51,480 particles were used for 3D refinement with D6 symmetry, which yielded an EM density map at a resolution of 3.2 Å.
The Cx36LMNG-BRIL dataset was processed using cryoSPARC (v.3.1.0.). Patch-based pre-processing (Patch motion correction & Patch CTF estimation) was performed on the dataset containing 2228 movies. Next, 435,817 particles picked by reference-based auto-picking were extracted into 340-pixel boxes. After three rounds of 2D classification, good particles were re-extracted into 540-pixel boxes and subjected to three rounds of 2D classification. Finally, 70,095 particles were used for 3D refinement with D6 symmetry, which yielded an EM density map at a resolution of 2.2 Å.
The Cx36Nano-BRIL dataset was processed using cryoSPARC (v.3.1.0.). Patch-based pre-processing (Patch motion correction & Patch CTF estimation) was performed on the dataset containing 499 movies. Next, 71,238 particles picked by reference-based auto-picking were extracted into 400-pixel boxes. After three rounds of 2D classification, good particles were re-extracted into 512-pixel boxes and subjected to three rounds of 2D classification. Finally, 10,611 particles were used for 3D refinement with D6 symmetry, which yielded an EM density map at a resolution of 3.4 Å.
The Cx36 Nano-WT-ΔN8 dataset was processed using cryoSPARC (v.3.1.0.). Patch-based pre-processing (Patch motion correction & Patch CTF estimation) was performed on the dataset containing 3463 movies. Next, 766,823 particles picked by reference-based auto-picking were extracted into 360-pixel boxes. After six rounds of 2D classification, good particles were re-extracted into 540-pixel boxes and subjected to three rounds of 2D classification. Finally, 460,806 particles were used for 3D refinement with D6 symmetry, which yielded an EM density map at a resolution of 3.2 Å.
The Cx36Nano-BRIL-ΔN16 dataset was processed using cryoSPARC (v.3.2.0.). Patch-based pre-processing (Patch motion correction & Patch CTF estimation) was performed on the dataset containing 2402 movies. Next, 426,090 particles picked by reference-based auto-picking were extracted into 512-pixel boxes. After five rounds of 2D classification, good particles were used for the generation of 3D initial model. Finally, 39,444 particles were used for 3D refinement with D6 symmetry, which yielded an EM density map at a resolution of 3.4 Å.
## Focused 3D classification for the Cx36Nano-WT dataset and structure determination of full PLN, full FN, and structurally hetero-junctional Cx36 GJCs
The Cx36Nano-WT dataset was processed using Relion (v.3.1). Beam-induced motion correction and CTF estimation of 7250 movies was performed using MotionCor2 version 1.2.6 and Gctf version 1.18, respectively. Next, 689,081 particles picked by reference-based auto-picking were extracted into 360-pixel boxes. After six rounds of 2D classification, good particles were re-extracted into 540-pixel boxes and subjected to additional three rounds of 2D classification. After five rounds of 3D classification, final 85,080 particles were used for 3D refinement with D6 symmetry imposition, which yielded a 3.19 Å consensus map. The consensus map was used for further processing focused on GJC or hemichannel. The overall workflow is presented in Supplementary Fig. 3.
In the approach focused on GJC, 85,080 particles were subjected to 3D skip-alignment classification with D6 symmetry imposition. To increase the accuracy of classification, we applied a mask covering GJC for a focused 3D classification. The first 25 iterations with Regularization parameter (T) of 20 and the second 10 iterations with increasing T value ($T = 40$) and GJC mask were performed. In the resulting three classes, Class 1 and 2, respectively, including ~$50\%$ and ~$15\%$ GJC particles showed clear map densities of pore-lining NTHs (PLNs), whereas the NTH densities were unclear in Class 3 with ~$35\%$ particles. To improve the map quality, particles in two GJC classes were used for 3D refinement with D6 symmetry imposition and local angular searches, respectively. After sharpening, the result showed a 3.05 Å GJC map with full PLN state and a 3.16 Å GJC map with full FN state.
In the approach focused on hemichannel, 85,080 particles were used for 3D skip-alignment classification, as previously described in ref. 38. Cx36 hemichannel in the PLN state and Cx43 hemichannel in the GCN state (PDB 7F92) were combined and used to create a hemichannel mask. Two masks covering each hemichannel of the consensus GJC map were used to generate the subtracted particles in two opposite orientations. After re-centering and re-extraction into 300-pixel boxes, the subtracted particles were subjected to 3D classification ($K = 6$; K is the number of classes to classify) with C1 symmetry imposition to align into one orientation. Three hemichannel classes (Class 2, 4, and 6) with good quality side views were subjected to focused 3D refinement (C1 symmetry) with a soft mask covering hemichannel to obtain a 3.65 Å map reconstructed from 140,255 particles. With this new consensus map, we performed 25 iterations of 3D classification ($T = 10$). Then, the additional 10 iterations were performed with increasing T value ($T = 20$) and a soft mask covering only the cytoplasmic half of hemichannel to classify more accurately for NTH conformations. To obtain a structurally hetero-junctional GJC map, hemichannel particles in five classes (Class 1, 3, 4, 5, and 8) in the FN state were chosen and traced back to their original GJC particles, and the redundant particles were removed. Finally, 14,155 GJC particles were subjected to 3D refinement with 1.8° angular sampling, producing a 4.9 Å unsharpened map refined with C1 symmetry imposition and a 3.3 Å sharpened map refined with C6 symmetry imposition. The GJC structures clearly showed PLN hemichannel on one side and FN hemichannel on the other side.
## Protomer-focused classification for the Cx36Nano-WT dataset
D6 symmetry expansion was performed with the 3.19 Å consensus map from the Cx36Nano-WT dataset. All protomer particles were subtracted using a mask covering a single protomer. The subtracted protomer particles were subjected to focused 3D classification ($K = 8$, $T = 20$) with the protomer mask and without orientation search. In the resulting eight classes, three classes showed the flexible NTH conformation (FN protomer), and five classes showed the pore-lining NTH conformation (PLN protomer) (Supplementary Fig. 8a).
## Analysis of the distribution of PLN protomers in GJC particles
To investigate how many PLN protomers were included in each GJC particle, we analyzed the metadata file generated by protomer-focused 3D classification as previously reported39. The metadata includes the class number of each protomer particle and the identification number (ID) of the original GJC particle. Each GJC ID should be found 12 times in the metadata due to D6 symmetry expansion. The metadata was sorted by protomer class number, and GJC IDs were collected only for the two classes with the PLN conformation. The number of repetitions of each GJC ID in the collection indicates the number of PLN protomers in the GJC particle. We counted the number of GJCs at each of 13 different ratios of FN to PLN protomers (0:12 to 12:0) to produce the final distribution graph in Supplementary Fig. 8b. To analyze relative protomer positions in each hemichannel represented in Supplementary Fig. 8c, we used the rotation angle information for the rotation of each GJC particle during the symmetry expansion, which is recorded in the metadata file for each PLN protomer. More detailed information is described in the previous report in ref. 26.
## Model building and refinement
All structural models with acyl chains and/or CHS molecules were built in Coot program58,59. All models do not include CL (Ala102-Glu193), and CT (Trp277-Val321), and all models except the PLN state of Cx36LMNG-WT, Cx36Nano-WT and Cx36Nano-BRIL do not include NTH (Met1-His18), due to weak map density. The structure of Cx36LMNG-BRIL GJC was initially solved and used as a reference for modeling other structures in the FN state. The head groups of lipids and detergents could not be modeled due to weak map density. All structures were refined using phenix.real_space_refine60 in PHENIX software and visualized using UCSF Chimera61.
## MD simulation protocol
We performed all MD simulations using the GROMACS package62. We used the CHARMM36 force field in the Gromacs format downloaded from the website of the Mackerell group: the CHARMM36m for proteins63, the CHARMM36 for lipid molecules64, the CHARMM-modified TIP3P model for water molecules, and the CHARMM36 standard ion parameters. In combination with the CHARMM force fields, we employed the CUFIX corrections to improve the charge-charge interactions among ions and charged side chains65. Van der Waals forces were computed using a 10- to 12-Å switching scheme. Long-range electrostatic forces were calculated using the particle-mesh Ewald summation scheme66 of a 1.2-Å grid spacing and 12-Å real-space cutoff. The time step was two femtoseconds by constraining covalent bonds to hydrogen in non-water using the LINCS67 and water molecules using the SETTLE algorithm68.
## MD preparation of Cx36 embedded in a lipid bilayer
We used the structural model of Cx36Nano-WT in the PLN state for MD simulation, in which the CL and CT regions are missing. To prepare the GJC model composed of Cx36 with the CL, we manually reconstructed the unstructured CL of each Cx36 chain (residues 102 to 196) using the corresponding region in the predicted Cx36 model (Q9UKL4) from the Alphafold Protein Structure Database69. Near the transmembrane domain of each hemichannel of Cx36, we placed a lipid bilayer of a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC), followed by the removal of lipid molecules overlapping with the channel. We immersed the resulting complex of Cx36 and the double bilayer systems in an explicit solution of 150 mM NaCl. The final system contained a channel, 734 POPC lipids, 135,625 water molecules, 450 Na ions, and 462 Cl ions in a periodic hexagonal box (a = b ≈ 14 nm, c ≈ 34 nm, α = β = 90˚, γ = 60˚). We energy-minimized each system for 5000 steps and equilibrated it for 70 ns under a constant surface tension–constant temperature (NPγT) ensemble at zero surface tension (γ = 0)70 and 300 K temperature71. We simulated each of the assembled systems for 400 ns in total (200 ns at a voltage bias of 0 mV with position restraints on the experimentally determined heavy atoms followed by 200 ns at a voltage bias of 200 mV). For the measurements of ionic currents through the two channels composed of Cx36 with and without the CL, we performed the simulation under 200 mV for 1.2 and 0.6 µs, respectively, starting from the final structure of the equilibration. During MD simulations, we saved atomic coordinates every 20 ps. Using the saved trajectory, we computed the three-dimensional density-flux map and visualized the results as described in a previous report by Yoo and Aksimentiev72.
The Cα root mean square deviations (RMSDs) of Cx36 GJC with and without the CL converged to about ~4 Å and ~3.5 Å, respectively, suggesting that these channels remained structurally stable under a thermal fluctuation (Supplementary Fig. 7f, k). The root mean square fluctuation (RMSF) using the trajectory more than 200-ns (Supplementary Fig. 7f, i) was ~2 Å for all residues except those in six CLs and several terminal residues, suggesting that this channel does not have particularly dynamic structural motifs and are stable in lipid bilayers.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37040-8.
## Source data
Source Data
## Peer review information
Nature Communications thanks Tomás Perez-Acle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: 'Association of diabetes mellitus and glycemic control with left ventricular
function and deformation in patients after acute myocardial infarction: a 3 T cardiac
magnetic resonance study'
authors:
- Yue Gao
- Rui Shi
- Yuan Li
- Ying-kun Guo
- Hua-Yan Xu
- Ke Shi
- Zhi-gang Yang
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10008587
doi: 10.1186/s12933-023-01785-9
license: CC BY 4.0
---
# Association of diabetes mellitus and glycemic control with left ventricular function and deformation in patients after acute myocardial infarction: a 3 T cardiac magnetic resonance study
## Abstract
### Background
Diabetes mellitus (DM) is considered a major risk factor for myocardial infarction (MI), and MI patients with DM have a poor prognosis. Accordingly, we aimed to investigate the additive effects of DM on LV deformation in patients after acute MI.
### Materials and methods
One hundred thirteen MI patients without DM [MI (DM−)], 95 with DM [MI (DM+)] and 71 control subjects who underwent CMRscanning were included. LV function, infarct size and LV global peak strains in the radial, circumferential and longitudinal directions were measured. MI (DM+) patients were divided into two subgroups based on the HbA1c level (< $7.0\%$ and ≥ $7.0\%$). The determinants of reduced LV global myocardial strain for all MI patients and MI (DM+) patients were assessed using multivariable linear regression analyses.
### Results
Compared with control subjects, both MI (DM−) and MI (DM+) patients presented higher LV end-diastolic and end-systolic volume index and lower LV ejection fraction. LV global peak strains progressively declined from the control group to the MI(DM−) group to the MI(DM+) group (all $p \leq 0.05$). Subgroup analysis showed that LV global radial PS and longitudinal PS were worse in MI(MD+) patients with poor glycemic control than in those with good glycemic control (all $p \leq 0.05$). DM was an independent determinant of impaired LV global peak strain in radial, circumferential and longitudinal directions in patients after acute MI (β = − 0.166, 0.164 and 0.262, both $p \leq 0.05$). The HbA1c level was independently associated with a decreased LV global radial PS (β = − 0.209, $$p \leq 0.025$$) and longitudinal PS (β = 0.221, $$p \leq 0.010$$) in MI (DM+) patients.
### Conclusions
DM has an additive deleterious effect on LV function and deformation in patients after acute MI, and HbA1c was independently associated with impaired LV myocardial strain.
## Introduction
Coronary artery disease and myocardial infarction (MI) are major causes of global morbidity and mortality [1]. Assessment and management of risk factors are the core of the treatment strategy for MI patients. Diabetes mellitus (DM) is considered a major risk factor for coronary artery disease, and patients with DM are at a high risk of MI and have a poor prognosis [2–4]. Left ventricular (LV) hypertrophy, myocardial fibrosis, and diastolic and systolic dysfunction are the main causes of diabetic cardiomyopathy [5–7]. Diastolic dysfunction is one of the important indicators of early left ventricular (LV) dysfunction before reduced LV ejection fraction in DM patients, which can earlier indicate the possible occurrence of ischemic events [8, 9].
Previous studies have noted that both MI and DM can lead to LV dysfunction and impaired deformation, culminating in the progressive deterioration of HF and poor outcomes [10, 11]. Meanwhile, hyperglycemia status can aggravate cardiac structural and functional abnormalities, such as replacement myocardial fibrosis and LV wall stiffness [12, 13]. Therefore, among patients after acute MI, investigating the effects of DM and glycemic control on LV myocardial deformation is important to achieve the goal of health management.
Cardiac magnetic resonance (CMR) imaging provides comprehensive information on cardiac function, deformation, and myocardial tissue. Deformation, especially impaired global longitudinal strain, has been proven to be associated with cardiovascular events and has better prognostic value than LVEF [14, 15]. Therefore, the current study sought to investigate the additive effects of DM on LV function and global deformation in patients after acute MI.
## Study population
The study protocol was approved by the Biomedical Research Ethics Committee of our hospital. Informed consent was waived due to the retrospective nature of the research.
Initially, we consecutively retrospectively enrolled 648 patients with MI who had completed CMR examinations in our hospital between January 2010 and March 2022. MI was diagnosed in our hospital and meet the diagnostic criteria for the universal definition of MI (2007, 2012 and 2018, which based on clinical symptoms, electrocardiogram changes, and creatine kinase and/or troponin T levels greater than standards), and have a history of MI by clinically diagnosed. The exclusion criteria were as follows: [1] cardiomyopathy, congenital heart disease, pericardial disease, severe arrhythmia, or valvular heart disease (confirmed by echocardiography, electrocardiogram, coronary computed tomographic angiography or CMR); [2] acute or subacute MI patients [16]; [3] an incomplete clinical record; and [4] inadequate images because of arrhythmia or poor image quality. Following these criteria, a total of 208 patients after MI were included in this study. According to whether there was coexisting DM, patients were further divided into MI (DM+) and MI (DM−) groups. The diagnosis of DM was based on current European Society of Cardiology [2019] guidelines [17]. The treatment, culprit vessel and diseased coronary artery of all MI patients were recorded. To evaluate the influence of glycemic control on LV, MI(DM+) patients were categorized as having good glycemic control (HbA1c < $7.0\%$) or poor glycemic control (HbA1c ≥ $7.0\%$). A detailed flow chart of the present study is presented in Fig. 1. In addition, age-, sex-, and body mass index-matched subjects without a history of MI were enrolled as controls. Exclusion criteria for the control group were as follows: [1] DM or impaired glucose tolerance; [2] presence of dyspnea, chest pain, palpitation, or other cardiovascular disease-related symptoms; [3] electrocardiogram abnormalities; and [4] CMR detected abnormalities (perfusion defect, local or diffuse myocardial late gadolinium enhancement, abnormal ventricular motion, valvular stenosis, etc.). Finally, a total of 71 controls were included in this study. Fig. 1Flow chart of the study
## CMR scanning protocol
All CMR examinations were performed in the supine position using a 3.0 T whole body magnetic resonance scanner Trio Tim or MAGNETOM Skyra (Siemens Medical Solutions, Erlangen, Germany) equipped with 32-channel body phased array coils and standard ECG trigger equipment. Balanced steady-state free precession (b-SSFP) cine images were acquired using a retrospective vector ECG gating technique at the end of inspiratory breath holding, and twenty-five frames were reconstructed per breath-hold acquisition. Standard short-axis, long-axis two- and four-chamber cine images were obtained. that covered the entire left ventricles. The following scanning parameters were used: repetition time (TR) 2.8 ms or 3.4 ms, echo time (TE) 1.22 ms, flip angle 40° or 50°, slice thickness 8 mm, field of view (FOV) 250 × 300 mm2 or 340 × 285mm2, and matrix 208 × 139 or 256 × 166. Gadolinium-based contrast agent was intravenously injected at a dose of 0.2 mmol/kg body weight at an injection rate of 2.5–3.0 mL/s, followed by a 20 mL saline flush at a rate of 3.0 mL/s. LGE images were acquired in the corresponding slice position as the cine imaging 10–15 min after contrast injection. The images were obtained using a phase-sensitive inversion recovery sequence with the following parameters: TR $\frac{750}{512}$ ms, TE $\frac{1.18}{1.24}$ ms, flip angle 40°, slice thickness 8 mm, FOV 240 × 300 mm2 or 288 × 360 mm2, and matrix 256 × 184 mm2 or 256 × 125 mm2.
## CMR data analysis
All CMR data were uploaded to an offline workstation using semiautomated software (Cvi42; Circle Cardiovascular Imaging, Inc., Calgary, Canada). The LV endocardial and epicardial traces were manually or semiautomatically delineated in serial short-axis slices at the end-diastolic and end-systolic phases. Papillary muscles were considered part of the ventricular cavity and LV mass, and epicardial fat was excluded. LV functional parameters, including LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV stroke volume (LVSV), LVEF and LV mass (LVM), were computed automatically. LVEDV, LVESV, LVSV and LVM were indexed to body surface area (BSA). The LV global function index (LVGFI) was calculated using the following formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{LVGFI}} = \left\{ {{\text{LVSV/ }}\left[{\left({{\text{LVEDV}} + {\text{LVESV}}} \right)/2 + \left({{\text{LVM}}/1.05} \right)} \right]} \right\} \times 100$$\end{document}LVGFI=LVSV/LVEDV+LVESV/2+LVM/1.05×100 For LV myocardial deformation analysis, LV long-axis cine images (2-chamber and 4-chamber) and short-axis cine images were loaded into the feature tracking module. LV endocardial and epicardial borders were delineated at the end-diastolic phases of all cine images. The LV global LV global radial peak strain (GRPS), global circumferential peak strain (GCPS), and global longitudinal peak strain (GLPS) were acquired automatically (Fig. 2). For LGE analysis, the hyper-enhanced myocardium area was defined as the MI area on the LGE short-axis images when the signal intensity was five standard deviations above the mean intensity of the normal myocardium [18]. We assessed the extent of the LGE regions involving the LV wall by dividing it into the interventricular septum, anterior wall, inferior wall, and lateral wall using the 16-segment model. Two radiologists evaluated the images separately, and if the results were inconsistent, they discussed and agreed on the result. Fig. 2Representative CMR imaging LV pseudo color images of long-axis four-chamber cine images at the end-systole and CMR imaging derived global longitudinal peak strain curves. A1–2: a control subject, B1–2: a patient after acute MI without DM, C1–2: a patient after acute MI with DM
## Reproducibility analysis
To determine intra-observer variability, LV global myocardial strain and LGE parameters in 90 random subjects (including 65 MI patients and 25 control subjects) were measured twice within 1 month by one observer (Y, G). A second observer (R, S), who was blinded to the results of the first observer and clinical data, reperformed the measurements to assess the interobserver variability.
## Statistical analysis
Statistical analyses were performed with SPSS (version 23.0; IBM SPSS, Inc., Chicago, IL, USA) and GraphPad Prism (version 8.0, GraphPad Software Inc., San Diego, CA, USA). Data were expressed as the mean ± standard deviation (SD) or median interquartile range (IQR) for continuous variables and frequencies for categorical variables. Categorical variables are presented as numbers (percentages) and were compared using Fisher’s exact test or the chi-square test, as appropriate. Parameters among MI(DM−), MI(DM+) and control groups were compared by one-way analysis of variance (one-way ANOVA) followed by Bonferroni’s post hoc test (normally distributed variables) or the Kruskal–Wallis rank test (nonparametric variables), as appropriate. Spearman’s and Pearson’s correlation analyses were conducted to identify the relationship between LV myocardial strain and clinical indices. Pearson’s correlation was used between continuous variables, and Spearman’s correlation was used to analyses the rank correlation. Moreover, variables with a p value of less than 0.1 in the univariable correlation analyses and an absence of collinearity were then included in a stepwise multivariable analysis to identify the independent determinants of LV global peak strain parameters. A p value of < 0.05 was considered statistically significant.
## Patient characteristics
Overall, 208 MI patients and 71 controls were included in this study. Of the 208 patients after acute MI, 95 patients were identified as having DM, and 113 patients were classified as non-DM patients. The main clinical baseline characteristics of the study cohort are summarized in Table 1. Age, sex, BMI, systolic and diastolic blood pressure, serum indexes and cardiovascular risk factors were not significantly different between the observed groups (all $p \leq 0.05$). The NYHA functional class in the MI(DM+) group was decreased compared with that in the MI(DM−) group ($p \leq 0.05$). The left anterior descending artery was the most common culprit vessel in both the MI(DM−) group and MI(DM+) group (46 [$40.71\%$] vs. 41 [$43.16\%$], $p \leq 0.05$). There was a higher number of diseased vessels in the MI(DM+) group than in the MI(DM−) group ($p \leq 0.05$). Additionally, the NT-proBNP value was significantly higher in the MI (DM+) group than in the MI (DM−) group ($p \leq 0.05$), and there was no difference in troponin value between the MI groups. Table 1Baseline characteristics of the study cohortControl ($$n = 71$$)MI (DM−) ($$n = 113$$)MI (DM+) ($$n = 95$$)Baseline characteristics Age, years58 ± 258.9558.08 ± 1.8860.76 ± 77 Male, n (%)55 ($77.46\%$)97 ($85.84\%$)85 ($89.47\%$) BMI, kg/m223.31 (21.78, 25.27)24.37 (22.31, 26.51)25.16 (22.60, 27.22) Systolic blood pressure, mmHg127.83 ± 16.19122.99 ± 19.31126.90 ± 22.44 Diastolic blood pressure, mmHg76.58 ± 10.1675.17 ± 13.1877.45 ± 11.83 Heart rate, bpm70.69 (63.59, 78.97)70.44 (61.79, 80.22)73.19 (65.81, 80.97)Cardiovascular risk factors Previous/current smoker, n (%)–77 ($68.14\%$)60 ($63.16\%$) Hyperlipidemia–65 ($57.52\%$)55 ($57.89\%$) Hypertension–50 ($44.25\%$)57 ($60.00\%$) Pervious PCI, n (%)–47 ($41.59\%$)43 ($45.26\%$) Pervious GABG n (%)02 ($2.11\%$)NYHA functional class, n I–33 ($29.20\%$)12 ($12.63\%$)b II–41 ($36.28\%$)45 ($47.37\%$) III–33 ($29.20\%$)31 ($32.63\%$) IV–6 ($5.31\%$)7 ($7.37\%$)Culprit vessel n (%) Left main–3 ($2.65\%$)2 ($2.11\%$) Left anterior descending–46 ($40.71\%$)41 ($43.16\%$) Left circumflex–23 ($20.35\%$)18 ($18.95\%$) Right coronary artery–41 ($36.28\%$)34 ($35.79\%$)Number of diseased vessels n (%) 158 ($51.33\%$)36 ($37.89\%$) 234 ($30.09\%$)34 ($35.79\%$) 317 ($15.04\%$)23 ($24.21\%$)HbA1c, %5.75 ± 0.467.90 (7.00, 8.90)bTriglycerides, mmol/L1.43 (0.95, 1.83)1.44 (1.04, 2.27)1.43 (0.93, 2.01)Total cholesterol, mmol/L4.33 ± 0.543.55 (3.19, 3.91)3.24 (2.89, 3.98)HDL, mmol/L1.32 ± 0.341.04 (0.84, 1.24)1.00 (0.82, 1.16)LDL, mmol/L2.56 ± 0.581.26 (1.16, 1.96)1.86 (1.51, 2.39)eGFR, mL/min/1.73m2100.32 (87.00, 110.85)84.97 (72.37, 96.21)80.31 (65.69, 94.55)Troponin, ng/L14.20 (9.40, 28.60)20.90 (11.20, 39.10)NT-proBNP–311.00 (167.50, 766.00)687.00 (155.50, 1916.50)bConcomitant medication, n (%) Aspirin, n (%)–106 ($93.81\%$)90 ($94.74\%$) ß-blockers, n (%)–92 ($81.42\%$)78 ($82.11\%$) ACEI/ARB, n (%)–68 ($60.18\%$)65 ($68.42\%$) Diuretics–46 ($40.71\%$)36 ($37.89\%$) Calcium-channel blocker–84 ($74.34\%$)74 ($77.89\%$) Insulin–12 ($10.62\%$)56 ($58.95\%$) Statin, n (%)–89 ($78.76\%$)89 ($93.68\%$)DM: diabetes mellitus; MI: myocardial infarction; BMI: body mass index; PCI: percutaneous transluminal coronary intervention; CABG: coronary artery bypass grafting; NYHA: New York Heart Association; HbA1c: glycated hemoglobin; HDL: high density lipoprotein; LDL: low density lipoprotein; eGFR estimated glomerular filtration rate; ACEI: angiotensin converting enzyme inhibitor; ARB: angiotensin receptor blockersa: $p \leq 0.05$ versus control group (Bonferroni’s)b: $p \leq 0.05$ versus MI patients without DM (Bonferroni’s)
## Comparison of LV function and global strain among MI patients with and without DM and controls
The CMR results for LV function and global peak strain are summarized in Table 2. In contrast to the control group, MI patients with and without DM exhibited an increased LVEDVi, LVESVi, LVMI, and decreased LVEF and LVGFI (all $p \leq 0.05$). The MI (DM+) group exhibited a higher LVMI and lower LVGFI than the MI (DM−) group (all $p \leq 0.05$), whereas the LVEF showed no difference between these two groups ($p \leq 0.05$). Regarding LV deformation parameters, all LV GRPS, GCPS and GLPS were decreased from in the controls to the MI (DM−) group to the MI (DM+) group (all $p \leq 0.001$, Fig. 3). In addition, the MI size of the LV was increased in the MI(DM+) group compared with the MI(DM−) group (24.38 (16.14, 33.46) % vs. 17.63 (10.94, 29.40) %, $p \leq 0.05$). There was no significant difference in MI territory in MI patients with or without DM ($p \leq 0.05$).Table 2CMR findings between controls, MI (DM−) group and MI (DM+) groupControl ($$n = 71$$)MI (DM−) ($$n = 113$$)MI (DM+) ($$n = 95$$)LVEDVi, mL/m272.89 (65.45, 83.05)99.82 (78.57, 131.83)a109.19 (86.83, 144.28)aLVESVi, mL/m224.01 (19.78, 29.14)54.78 (34.58, 84.41)a64.52 (39.49, 98.89)aLVSVi, mL/m249.08 (42.23, 53.77)46.46 (38.77, 53.17)a43.92 (35.41, 52.84)aLVEF, %65.40 (62.75, 70.01)46.91 (34.34, 55.74)a40.64 (28.88, 55.84)aLVMI, g/m268.04 (60.20, 81.68)101.06 (84.43, 126.98)a114.23 (95.62, 134.13)abLVGFI51.51 ± 6.8934.33 ± 11.06a30.71 ± 12.14abLV GPS, % Radial36.95 (33.40, 41.89)18.95 (13.69, 27.15)a15.09 (10.52, 23.46)ab Circumferential − 20.55 (− 22.55, − 19.14) − 13.05 (− 15.67, − 10.51)a − 10.76 (− 16.15, − 7.78)ab Longitudinal − 15.40 (− 17.02, − 12.80) − 9.04 (− 11.60, − 6.58)a − 7.06 (− 9.21, − 4.76)abInfarct size, g % of LV–17.63 (10.94, 29.40)24.38 (16.14, 33.46)bInfarct territory, n (%)– Interventricular septum–65 ($57.52\%$)53 ($55.79\%$) Inferior–43 ($38.05\%$)37 ($38.95\%$) Lateral–25 ($22.12\%$)29 ($30.53\%$) Anterior–32 ($28.32\%$)24 ($6.12\%$)Data are presented as median (25th, 75th percentile)LVEDVi, left ventricular end diastolic volume index; LVESVi, left ventricular end systolic volume index; LVSVi, left ventricular stroke volume index; LVEF, left ventricular ejection fraction; LVGFI: left ventricular global function index; LVMI: left ventricle mass index; GPS: global peak straina: $p \leq 0.05$ versus control group (Bonferroni’s)b: $p \leq 0.05$ versus MI(DM−) group (Bonferroni’s)Fig 3Comparison of LV global strains in three directions among MI (DM−) patients, MI (DM+) patients and control subjects
## Association of LV function and global strain with clinical variables in MI patients
Univariable and multivariable linear regression analyses were performed to evaluate the independent effect of DM on LV function and deformation in MI patients. After multivariable adjustment for covariates among all MI patients, DM was found to be an independent determinant of impaired LVGFI (β = 0.190, $$p \leq 0.004$$) and increased LVMI (β = 0.158, $$p \leq 0.021$$) (Table 3). Furthermore, systolic blood pressure, NT-proBNP level and infarct size were independently associated with LVGFI (β = 0.181, − 0.193, and 0.401, all $p \leq 0.05$). Age, hyperlipidemia and hypertension were independently associated with LVMI (β = − 0.230, 0.174 and 0.287, all $p \leq 0.05$) (Table 3).Table 3Determinants of LV dysfunction in MI patientsLVGFILVMIUnivariableMultivariableUnivariableMultivariableRpβprpβpAge, years − 0.0290.6740.1970.004 − 0.2300.001Male, n (%)0.1050.1310.1470.034BMI, kg/m2 − 0.0460.5060.2050.003Systolic blood pressure, mmHg0.2270.0010.1810.0050.0320.649Hyperlipidemia0.0730.2940.1780.0120.1740.012Hypertension0.1240.1430.2100.0020.287 < 0.001DM0.1470.0330.1900.0040.1900.0060.1580.021eGFR, mL/min/1.73m20.0930.1810.0210.757NT-proBNP − 0.2300.001 − 0.1930.0030.1520.030Infarct size, g % of LV − 0.375 < 0.0010.401 < 0.0010.0710.320Abbreviations as listed in Tables 1 and 2NT-proBNP was log-transformed before being included in the regression analysis After adjusting for confounding factors, the multivariable linear regression analysis showed that DM was independently associated with LV GRPS (β = − 0.166, $$p \leq 0.007$$), GCPS (β = 0.164, $$p \leq 0.005$$) and GLPS (β = 0.262, $p \leq 0.001$) (Table 4). Moreover, NT-proBNP level, infarct size and LVMI were independently associated with LV GRPS (β = − 0.140, − 0.375 and − 0.292, all $p \leq 0.05$), GCPS (β = 0.164, 0.431 and 0.316, all $p \leq 0.05$), and GLPS (β = 0.124, 0.300 and 0.331, all $p \leq 0.05$) (Table 4).Table 4Univariable and multivariable linear regression analysis of LV global peak strain in MI patientsGRPSGCPSGLPSUnivariableMultivariableUnivariableMultivariableUnivariableMultivariableRp valueβp valuerp valueβp valuerp valueβp valueAge#, years − 0.0370.5790.0060.9350.0500.471Male, n (%) − 0.1380.0470.1530.0270.0910.190BMI, kg/m2 − 0.0280.6880.0270.7030.0030.963Systolic blood pressure, mmHg0.1870.0070.1900.04 − 0.1810.009 − 0.1750.023 − 0.1370.048DM − 0.1840.008 − 0.1660.0070.1600.0200.1640.0050.252 < 0.0010.262 < 0.001eGFR, mL/min/1.73m20.1360.049 − 0.0450.519 − 0.1590.021NT-proBNP − 0.1980.004 − 0.1400.0210.1850.0080.1640.0040.1940.0050.1240.041Infarct size, g % of LV − 0.387 < 0.001 − 0.375 < 0.0010.459 < 0.0010.431 < 0.0010.308 < 0.0030.300 < 0.001LVMI, g/m2 − 0.262 < 0.001 − 0.292 < 0.0010.273 < 0.0010.316 < 0.0010.260 < 0.0010.331 < 0.001Abbreviations as listed in Tables 1 and 2, NT-proBNP was log-transformed before being included in the regression analysis
## Comparison of LV global peak strain among MI (DM+) patients with good and poor glycemic control
According to the status of glycemic control, MI (DM+) patients were divided into two subgroups: good glycemic control ($$n = 23$$, HbA1c < $7.0\%$) and poor glycemic control ($$n = 72$$, HbA1c ≥ $7.0\%$). The LV global peak strain among MI (DM−) patients and MI (DM+) patients with good or poor glycemic control were shown in Fig. 4. MI (DM+) patients with poor glycemic control had a lower LV global peak strain in three directions than the MI (DM−) patients (all $p \leq 0.001$). There was no significant difference between MI(DM−) patients and patients with good glycemic control (HbA1c < $7.0\%$). LV GCPS was significantly decreased in poor glycemic control patients compared with good glycemic control patients [− 10.17 (− 14.56, − 7.60) % vs. − 15.13 (− 18.32, − 9.40) %, $$p \leq 0.014$$], whereas LV GRPS and GLPS showed a decreasing tendency. Moreover, there were increased LVESVi values and decreased LVSVi and LVGFI values in MI(DM+) patients with poor glycemic control compared with good glycemic control patients [LVESVi: 76.12 (43.05, 109.36) mL/m2 vs. 41.21 (30.91, 63.23); LVSVi: 43.48 (34.24, 49.32) mL/m2 vs. 49.13 (42.66, 57.98); LVGFI: 26.41 (18.83, 38.28) vs. 38.44 (30.63, 46.95); all $p \leq 0.05$].Fig. 4Comparison of LV global strains among MI (DM−) patients and MI (DM+) patients with good or poor glycemic control
## Independent effect of HbA1c on LV global peak strains in MI (DM+) patients
The univariable analysis of MI (DM+) patients showed that HbA1c was negatively associated with GRPS (r = − 0.228, $$p \leq 0.026$$) and positively associated with GCPS ($r = 0.270$, $$p \leq 0.008$$) and GLPS ($r = 0.345$, $$p \leq 0.001$$) (Fig. 5). After adjusting for confounding factors, HbA1c remained an independent determinant of impaired GRPS (β = − 0.209, $$p \leq 0.025$$) and GLPS (β = 0.221, $$p \leq 0.010$$). Moreover, the log-transformed NT-proBNP level and infarct size were found to be independent determinants of global peak strain in all three directions (GRPS: β = − 0.198 and − 387, GCPS: β = 0.290 and 0.552, GLPS: β = 0.227 and 0.308, all $p \leq 0.01$) (Table 5).Fig. 5The associations between LV global peak strains and HbA1c level in MI (DM+) patientsTable 5Univariable and multivariable linear regression analysis of LV global peak strain in MI(DM+) patientsGRPSGCPSGLPSUnivariableMultivariableUnivariableMultivariableUnivariableMultivariableRp valueβp valuerp valueβp valuerp valueβp valueAge, years − 0.0370.5790.0060.9350.0500.471Male, n (%) − 0.1380.0470.1530.0270.0910.190BMI, kg/m2 − 0.0280.6880.0270.7030.0030.963Systolic blood pressure, mmHg0.1870.007 − 0.1810.009 − 0.1370.048HbA1c − 0.2280.026 − 0.2090.0250.2700.0080.3450.0010.2210.010eGFR, mL/min/1.73m20.1360.049 − 0.0450.519 − 0.1590.021NT-proBNP − 0.1980.004 − 0.2520.0060.1850.0080.2940.0010.1940.0050.334 < 0.001Infarct size, g % of LV − 0.387 < 0.001 − 0.341 < 0.0010.459 < 0.0010.552 < 0.0010.308 < 0.0030.445 < 0.001Abbreviations as listed in Tables 1 and 2NT-proBNP was log-transformed before being included in the regression analysis
## Inter- and intra-observer variability
There was excellent intra- and interobserver agreement in terms of LV global strain and LV infarct size. The intra- and interobserver agreement was excellent for LV strain parameters (ICC = 0.923–0.978 and 0.912–0.961, respectively) and infract size of LV (ICC = 0.826–0.897 and 0.876–0.901, respectively).
## Discussion
This study investigated the combined effects of DM on LV function and deformation in patients after acute MI. The main findings of this study are as follows: [1] MI patients presented impaired LV function and deformation, whereas DM further deteriorated LV function and global peak strain in all three directions (radial, circumferential, and longitudinal); [2] For MI patients, DM was found to be an independent determinant of impaired LVGFI and LV global peak strain in all three directions; and [3] LV global peak strains declined progressively with the increase in HbA1c in MI patients with DM, and HbA1c was an independent determinant of decreased LV GRPS and GLPS. Our study indicated the deleterious effect of DM on LV deformation in patients with MI, and poor glycemic control may further aggravate the impairment.
DM, as the most common chronic metabolic disease, is the major risk factor for cardiovascular complications and adverse cardiovascular events. There is a high diagnosis rate of DM among patients with MI, and previous studies have reported a similar twofold increase in the risk for major adverse cardiovascular events in patients with DM after AMI [19, 20]. For patients after AMI, the absorption of myocardial edema and inflammation or fibrosis in the infarction core results in abnormal movement or adverse remodeling of the LV [10]. However, diastolic dysfunction and myocardial fibrosis have also been proven to be important damage stages in patients with DM. Although the conventional LV function parameters (i.e., LVEDVi, LVESVi, LVEF) were similar between the MI(DM−) and MI(DM+) groups, our study demonstrated that DM further impaired LVGFI and increased LVMI in MI patients. LVGFI is a CMR-validated measure of LV cardiac performance that integrates LV structure into LV functional assessment, which can provide incremental prognostic value for mortality after myocardial infarction. This finding reveals that the effects of DM on structural damage to the LV in patients after MI precede the decrease in LVEF [21, 22].
In this study, we conducted multivariable linear regression analysis and found that comorbid DM augmented the impairment of LV global peak strain in all three directions by CMR-FT in MI patients, and DM was an independent determinant of LV global peak strain in patients after MI. The underlying cause for cardiac alterations in patients after MI with DM is complex. Myocardial metabolism disorder is characteristic of patients with DM, and microvascular endothelial damage is aggravated in this microenvironment, leading to an increased incidence and severity of coronary atherosclerosis [23, 24]. In our study, patients with DM after MI had more coronary artery lesions, suggesting that myocardial ischemia may be more severe. Moreover, in patients with DM, the impairment of subendocardial fibers is aggravated, and these direct and indirect effects may partly explain the additive effect of DM on LV deformation in MI patients.
Blood glucose control is an important indicator to prevent adverse cardiovascular events in diabetic patients [25–27]. HbA1c, as an important biomarker of long-term blood glucose control in DM patients, is effectively and widely used to reflect the status of glycemic control. Previous studies have reported that the process of endothelial dysfunction and even myocardial fibrosis might be associated with hyperglycemia by accumulation of glycosylation end-products [28]. Admission glycemic variability has been identified as a predictor of mortality in patients with myocardial infarction, especially ST-segment elevation myocardial infarction [29]. Nystrom et al. reported that patients with type I MI with poor glycemic control (HbA1c > $7\%$) had a twofold higher risk of MACE than those with good glycemic control [29]. In the current study, our data showed that the decreased LV global peak strains were more prominent in patients after MI with DM. Multivariable linear regression analysis showed that HbA1c was an independent determinant of LV global radial and longitudinal peak strain in MI patients with DM. The infarction size and NT-proBNP level were strongly independently associated with LV GCPS, rather than HbA1c. We speculated that the influence of diabetes on myocardial compliance was mainly the longitudinal distribution of myocardial fibers in the subendocardial region. Since HbA1c is an independent determinant of LV global function and deformation, the status of glycemic control should be given more considerable attention in MI patients with DM.
Additionally, our study showed that NT-proBNP levels and infarct size were significantly higher in MI patients with DM than in those without DM, and these indices were independent determinants of LV global strains in MI patients with DM. Several clinical trials have demonstrated that NT-proBNP and infarct size are associated with reversed cardiac remodeling and dysfunction, which means that MI patients with DM have a more pronounced cardiac load and LV stiffness [30–32].
## Limitations
The study had several limitations. First, this was a retrospective single-center study, so there may be some selection bias in the results. Second, some MI patients underwent PCI and other operations, so the long-term effects of treatment on MI cannot be completely ruled out. However, there was no difference in the proportion of MI patients who received treatment between the two groups, and the possible deviation was reduced as much as possible. Third, we did not assess the type of MI in each patient, and future studies could be investigated with a larger cohort to evaluate the effects of different MI types on LV.
## Conclusions
In patients after MI, DM had an additive deleterious effect on LV myocardial strain. In addition, LV function and myocardial strain deteriorated with increasing HbA1c in these patients, which emphasizes the importance of glycemic control in MI patients.
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|
---
title: 'Predictive value of gut microbiota in long-term blood pressure control: a
cross-sectional study'
authors:
- Guobin Kang
- Hongtao He
- Huawei Miao
- Tiejun Zhang
- Zongde Meng
- Xia Li
journal: European Journal of Medical Research
year: 2023
pmcid: PMC10008596
doi: 10.1186/s40001-022-00944-0
license: CC BY 4.0
---
# Predictive value of gut microbiota in long-term blood pressure control: a cross-sectional study
## Abstract
### Objectives
To investigate the prediction of long-term blood pressure control using the intestinal flora of patients with hypertension.
### Methods
A total of 125 patients with primary grade-2 hypertension who attended the cardiovascular department of Hebei Province Hospital of Chinese Medicine between April 2021 and April 2022 were enrolled; these included 65 patients with substandard long-term blood pressure control (the uncontrolled group) and 60 patients with standard long-term blood pressure control (the controlled group). General clinical data and data on morning stools and diet were recorded for all the enrolled patients. The 16 s rDNA sequencing of faecal intestinal flora was also performed to analyse the differences in intestinal flora between the two groups of patients and to investigate the relationship between blood pressure compliance and the presence of flora.
### Results
The intestinal flora of the two groups of patients differed in terms of the Firmicutes–Bacteroidetes ratio (F/B), α-diversity analysis (Chao1, ACE and Shannon) results and β-diversity analysis results. At the genus level, the number of *Streptococcus and* Paraprevotella in patients in the uncontrolled group was greater than that of the controlled group, and the level of Akkermansia and Bifidobacterium was lower than that in the controlled group. A logistic regression analysis of the difference factors found differences in ACE, F/B, Streptococcus, Paraprevotella and Akkermansia in the two groups; these differences remained after correcting for age, gender and body mass index. The receiver operating characteristic curves revealed the following: ACE (area under the curve [AUC] = 85.282), Streptococcus (AUC = 82.705), Akkermansia (AUC = 77.333), Paraprevotella (AUC = 66.154) and F/B (AUC = 60.436).
### Conclusions
There were significant differences in the intestinal flora of the patients in the controlled blood group compared with that of the uncontrolled group. Therefore, the ACE, genus levels of *Streptococcus and* Akkermansia could provide some prediction of late blood pressure compliance or non-compliance in patients with hypertension.
## Introduction
Hypertension is one of the most important and controllable risk factors for all-cause morbidity and mortality worldwide and is strongly associated with an increased risk of cardiovascular disease [1]. Although reductions in blood pressure can significantly reduce the occurrence of a wide range of acute events, long-term blood pressure control is required to reduce the global burden of disease and mortality [2]. Long-term substandard blood pressure control [3] or unstable control [4] can damage vital organs, such as the heart, brain, and kidneys, and it can lead to serious adverse events. The number of people with hypertension in China has reached 244.5 million [5]. However, the treatment rate of hypertension is less than $30\%$, and the average rate of achieving the standard is only $5.7\%$ [6]. Therefore, the incidence of cardiovascular diseases caused by long-term substandard blood pressure control will remain high in China for many years.
It is well-known that hypertension is associated with genetics [7]. However, the human genome includes not only the deoxyribonucleic acid (DNA) inherited from parents but also the various flora (formed by interactions with the external physical environment after birth) that stably and harmoniously live within the body, accounting for up to $90\%$ or more genome. Together, inherited parental DNA and intestinal flora form the human genome [8].
The abundance and number of intestinal flora vary according to human diseases, such as obesity, type-2 diabetes, non-alcoholic liver disease, malnutrition and hypertension [9]. Studies have shown that dietary modification can reduce the prevalence of hypertension in the population [10], and the absorption and metabolism of food are inevitably affected by intestinal flora and its metabolites. Current research confirms that the composition of intestinal flora and its metabolites, such as short-chain fatty acids, lipopolysaccharides and oxidized trimethylamine, influence the progression of cardiovascular disease [11]. Compared with healthy subjects, patients with hypertension have lower intestinal flora diversity, fewer short-chain fatty acid-producing microflora and more Gram-negative bacteria (which are sources of lipopolysaccharides) [12]. Furthermore, some animal studies have indicated that short-chain fatty acids directly regulate blood pressure, and lipopolysaccharides have significant pro-inflammatory effects [13]. This suggests that intestinal flora plays an important role in blood pressure regulation.
Most clinical studies have focused on investigating the relationship between intestinal flora and its related metabolites on the occurrence [14], development [15], treatment [16] and complications [17] of hypertension, and animal studies have focused on elucidating the mechanisms by which intestinal flora intervene in blood pressure [18, 19]; however, research on whether long-term blood pressure control in patients with hypertension is related to intestinal flora has not been reported.
Given the current situation of the long-term survival of patients being seriously affected by whether blood pressure standards are met or not, this study aimed to analyse the intestinal flora of patients with hypertension with and without standard blood pressure control; the aims were to explore the method of predicting late blood pressure control by the intestinal flora of patients with hypertension and to provide a basis for the achievement of blood pressure standards in patients with diagnosed hypertension.
## Subjects
The study included 125 patients with primary grade-2 hypertension who attended the cardiovascular ward or outpatient clinic of Hebei Province Hospital of Chinese Medicine between April 2021 and April 2022. The subjects were enrolled into an uncontrolled group (65 patients with substandard long-term blood pressure control) or a controlled group (60 patients with standard long-term pressure control) according to whether blood pressure control had been achieved in the last month. The diagnosis of primary hypertension and blood pressure attainment were made with reference to the criteria in the 2018 ESC/ESH Guidelines for the Management of Arterial Hypertension [20]. All enrolled patients provided signed informed consent, and the study protocol was approved by the ethics committee of Hebei Province Hospital of Chinese Medicine (acceptance number: 2020-KY-010–01).
## Inclusion criteria
① Patients diagnosed with grade-2 simple hypertension (160–179 mmHg for systolic blood pressure and/or 100–109 mmHg for diastolic blood pressure) who were aged 18–80 years (including those aged 18 and 80 years). The highest blood pressure from a consultation or previous diagnosis was used to determine whether the enrolled patient met the diagnostic criteria for grade-2 hypertension. ② No adjustment of antihypertensive drugs in the past month and taking 1 × amlodipine benazepril tablet (2.5–10 mg) (Baianxin, Yangzijiang Pharmaceutical Group) regularly each day. ③ No consumption of drugs that may affect intestinal flora (e.g., probiotics, antimicrobials, diet pills, and laxatives) during the previous 3 months. ④ Regular diet and lifestyle and regular bowel movements in the past month. ⑤ Weight change < 5 kg in the past 3 months. ⑥ Voluntary participation in the clinical trial and willingness to provide signed informed consent.
## Exclusion criteria
① Secondary hypertension caused by renal disease, renal artery stenosis, primary aldosteronism, pheochromocytoma, sleep apnoea, etc. ② Patients with combined non-hypertensive diseases. ③ A history of gastrointestinal diseases and gastrointestinal surgical diseases. ④ Other special conditions that may affect intestinal flora. ⑤ Patients on diets, those with weight loss and those with irregular lifestyles and eating habits.
## General data collection
General data, such as age, gender, smoking status, body mass index (BMI), duration of hypertension, blood pressure level and duration of medication, were recorded for both groups of subjects. It has been shown that exercise [21], diet [22] and yoghurt intake [23] can affect intestinal flora, so we divided the enrolled patients into three categories according to their weekly aerobic exercise level (< 3 times, 3–5 times and > 5 times), three categories according to their diet (meat-based, vegetable-based, and meat- and vegetable-based) and two categories according to their weekly yoghurt intake (≤ 300 ml and > 300 ml).
## Stool specimen collection
All subjects fasted for 8–10 h, and their stools were collected in the early morning of the following day. ① Specimen retention: morning faeces were collected with a sterile collection spoon from the middle section of the stool (> 5 ml) and stored in a sterilized stool collector. ② Specimen storage: the stools were placed in a low-temperature refrigerator at − 80 °C within 1 h after collection for long-term storage before testing.
## Intestinal flora assay
Deoxyribonucleic acid extraction was performed using a TIANGEN (Beijing, China) TIANamp stool DNA faecal genomic DNA extraction kit. This was followed by polymerase chain reaction amplification and purification, secondary amplification and purification, steps, such as library mixing and library processing, and finally, sequencing on the machine. The main steps are shown in Fig. 1.Fig. 1Flow chart of 16 s deoxyribonucleic acid assay for intestinal flora. PCR polymerase chain reaction, DNA deoxyribonucleic acid. In the raw data obtained from sequencing, there is a certain amount of interference data. To obtain high-quality sequencing data to improve the accuracy of the subsequent bioinformatics analysis, first, the original data needed to be spliced; then, they were quality controlled and filtered to obtain valid data
## Statistical methods
All statistical analyses were performed using SPSS 17.0 statistical software. The measurement data that obeyed normal or approximately normal distributions were expressed as mean ± standard deviation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }$$\end{document}x¯ ± s), and categorical data were expressed as numbers and percentages. An independent-samples t test was used for comparisons between the two groups when the variance was uniform, and a nonparametric test was used to compare the two groups when the variance was non-uniform. The aspects that initially screened out the differences between the two groups of patients were subjected to a binary logistic regression analysis to establish the factors affecting blood pressure compliance, and finally, the receiver operating characteristic (ROC) curve was plotted to test its predictive value. During the comparison, $P \leq 0.05$ was considered statistically different, and $P \leq 0.01$ was considered significantly different.
## General data of the patients in the two groups
There were no statistically significant differences between the two groups in terms of gender, age, smoking, BMI, duration of hypertension, duration of regular medication, exercise status, and diet (all $P \leq 0.05$). The systolic blood pressure (158.7 ± 7.95 vs. 126.6 ± 6.52 mmHg) and diastolic blood pressure (86.6 ± 6.53 vs. 74.6 ± 5.27 mmHg) were significantly higher in the uncontrolled group than in the controlled group, with a statistically significant difference ($P \leq 0.01$). The yoghurt intake of patients in the uncontrolled group was lower than that in the controlled group, and the difference was statistically significant ($P \leq 0.05$) (Table 1).Table 1Comparison of general information of patients in group A and group BFactorsUncontrolled groupControlled groupP valuen6560Male (%)30(46.2)28(46.7) > 0.05Age (years)68.5 ± 8.5569.7 ± 8.07 > 0.05Smoking (%)28(43.1)22(36.7) > 0.05BMI(kg/m2)24.8 ± 2.4524.8 ± 2.48 > 0.05Duration of hypertension (months)9.7 ± 4.7210.0 ± 4.79 > 0.05Duration of regular medication use (months)2.42 ± 0.952.56 ± 1.00 > 0.05Systolic blood pressure (mmHg)158.7 ± 7.95126.6 ± 6.52 < 0.01Diastolic blood pressure (mmHg)86.6 ± 6.5374.6 ± 5.27 < 0.01Exercise status (aerobic exercise) (%) < 3 times per week9(13.8)7(11.7) > 0.053–5 times per week22(33.8)21(35.0) > 0.05Weekly > 5 times34(52.3)32(53.3) > 0.05Diet (%)Meat-based4(6.2)3(5.0) > 0.05Vegetarian-based3(4.6)3(5.0) > 0.05Meat and vegetables58(89.2)54(90.0) > 0.05Average intake of yogurt (%) ≤ 300 ml per week42(64.6)25(42.7) < 0.05 > 300 ml per week23(35.4)35(58.3)BMI body mass index
## Multilevel species diagram of the intestinal flora in the two groups of patients
In the two groups, the highest to lowest proportions of intestinal flora of different phyla were Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Verrucomicrobia, Fusobacteria and Bacteria (see Fig. 2).Fig. 2Multilevel species composition of faecal flora in the two groups of patients. This figure Shows the loop of the faecal flora phylum-level composition of the two groups of patients; the overall upper half is the classification of the phylum level of intestinal flora, and the lower half is the patient's intestinal flora specimen. The first circle (from the outside to the inside) represents the uncontrolled group, controlled group and the names of the phylum-level flora classification: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Verrucomicrobia, Fusobacteria and Bacteria (unclassified). The second circle represents the number of each specimen in the two groups and the number of the phylum level of the flora. The third circle represents the percentage of phylum level in the two groups of specimens and the total percentage of the two groups. The linked line in the innermost circle shows the specific distribution of the phylum within the group
## Comparison of the intestinal flora of the two groups of patients at the phylum level
There was no difference in intestinal flora between the uncontrolled and controlled groups of patients in terms of Firmicutes (8607.09 ± 3282.96 vs. 7669.87 ± 3408.64), Bacteroidetes (3666.12 ± 1989.33 vs. 3732.60 ± 1308.29), Proteobacteria (2101.82 ± 2646.66 vs. 1391.37 ± 1402.62) and Fusobacteria (58.09 ± 114.92 vs. 91.22 ± 304.10). There were differences in terms of Actinobacteria (901.42 ± 1368.94 vs. 1404.02 ± 1759.22), Verrucomicrobia (51.98 ± 58.65 vs. 140.82 ± 130.75), Bacteria (unclassified) (0.05 ± 0.28 vs. 9.02 ± 19.65) and the Firmicutes–Bacteroidetes ratio (F/B) (2.86 ± 1.64 vs. 2.42 ± 1.60) (see Fig. 3).Fig. 3Comparison of faecal flora composition at the phylum level in the two groups of patients. F/B: Firmicutes–Bacteroidetes ratio, which is an important indicator of intestinal flora balance. The higher the F/B value, the worse the balance of the flora and the more serious the disorder of the flora [37]: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria, Verrucomicrobia, Bacteria (unclassified). * $P \leq 0.05$, ** $P \leq 0.01$
## Alpha diversity analysis
There was a significant difference between the two groups of patients in terms of Chao1, ACE and Shannon α diversity indices ($P \leq 0.01$), and there was no difference in terms of Simpson ($P \leq 0.05$) (see Fig. 4).Fig. 4Comparison of intestinal flora abundance and diversity index in the two groups of patients. The Chao1 and ACE indices are mainly concerned with the species richness information of the samples and estimate the number of species contained in a colony, while the Shannon and Simpson indices mainly comprehensively reflect the diversity and evenness of species, i.e., high ACE and Chao1 indices indicate a high number of species in the samples, and high Shannon and Simpson indices indicate high species abundance and evenness
## Beta diversity analysis
A principal coordinate analysis (PCoA) and a non-metric multidimensional scale (NMDS) analysis were performed using the Bray–Curtis method, and the results showed a significant difference in intestinal flora between the two groups of patients (both $P \leq 0.01$), as shown in Fig. 5.Fig. 5Comparison of the β diversity of the intestinal flora in the two groups of patients. PCoA principal coordinate analysis. During PCoA sorting, other distance/non-similarity matrices can be selected, and thus, the interrelationships between objects can be represented in two-dimensional coordinates. NMDS non-metric multidimensional scale analysis. This differs in that NMDS is no longer a characteristic root sorting technique and no longer aims to sort the bearings to load more variance; therefore, NMDS-sorted graphs can be arbitrarily rotated, centred and inverted. With the same number of axes, NMDS tends to obtain relationships between objects with less distortion than PCoA. The results of an NMDS analysis are measured by stress, which is generally considered to be represented by a two-dimensional point plot of NMDS when stress < 0.2 and its graph has some interpretative significance. When stress < 0.1, it can be considered a good ranking; when stress < 0.05, it is well-represented
## Analysis of the differences in the genus levels of intestinal flora between the two groups of patients
There were differences in the genus levels of the intestinal flora of the patients in the uncontrolled and controlled groups. Considering the actual situation of the flora, there was a significant difference in terms of Streptococcus (891.71 ± 953.61 vs. 193.65 ± 214.90), Paraprevotella (148.11 ± 131.03 vs. 66.38 ± 45.69), Akkermansia (51.98 ± 58.65 vs. 140.82 ± 130.75) and Bifidobacterium (612.37 ± 607.45 vs. 1257.25 ± 1720.87) ($P \leq 0.01$) (see Fig. 6).Fig. 6Differential analysis of the genus levels of intestinal flora in the two groups of patients. Each colour represents a group of samples. The bar on the left indicates the flora with significantly different abundance in the two groups of samples and the average relative abundance in the two groups, respectively. The graph on the right indicates the difference in relative abundance, separated by a dashed line in the middle; the left side of the dashed line indicates flora with higher relative abundance in one group, while the right side of the dashed line indicates flora with higher relative abundance in the other group; hence, each side of the dashed line is in a different colour. Streptococcus, Paraprevotella, Akkermansia and Bifidobacterium
## Binary logistic regression analysis in terms of differences in intestinal flora between the two groups of patients
After the multifactorial regression analysis in the two groups of patients, the intestinal flora F/B [odds ratio (OR): 0.559, $95\%$ confidence interval (CI) 0.336–0.930], Streptococcus (OR: 0.994, $95\%$ CI 0.990–0.998) and Paraprevotella (OR: 0.978, $95\%$ CI 0.964–0.993) were negatively associated with blood pressure attainment, and ACE (OR: 1.273, $95\%$ CI 1.042–1.556) and Akkermansia (OR: 1.022, $95\%$ CI 1.003–1.043) were positively correlated; this correlation persisted after correction for age, sex and BMI: F/B (OR: 0.548, $95\%$ CI 0.327–0.919), Streptococcus (OR: 0.994, $95\%$ CI 0.990–0.998), Paraprevotella (OR: 0.978, $95\%$ CI 0.963–0.992), ACE (OR: 1.305, $95\%$ CI 1.053–1.618) and Akkermansia (OR: 1.025, $95\%$ CI 1.004–1.047) (see Table 2).Table 2Multi-factor binary logistic regression analysis of differential indicators in 2 groups of patientsVariablesORMultivariateVariablesORAdjusted$95\%$ CIP value$95\%$ CIP valueGender0.4810.060–3.8340.490Age0.9510.852–1.0620.376BMI0.9200.626–1.3510.671F/B0.5590.336–0.9300.025*F/B0.5480.327–0.9190.023*chao10.9040.768–1.0640.226chao10.8950.761–1.0520.179ACE1.2731.042–1.5560.018*ACE1.3051.053–1.6180.015*Shannon0.1340.008–2.1370.155Shannon0.0740.003–1.6980.103Akkermansia1.0221.003–1.0430.027*Akkermansia1.0251.004–1.0470.021*Streptococcus0.9940.990–0.9980.004**Streptococcus0.9940.990–0.9980.004**Paraprevotella0.9780.964–0.9930.004**Paraprevotella0.9780.963–0.9920.003**Bifidobacterium1.0001.000–1.0030.094Bifidobacterium1.0011.000–1.0030.111Yogurt intake1.7980.283–11.4140.534Yogurt intake1.8740.257–13.6850.536OR odds ratio*$P \leq 0.05$**$P \leq 0.01$
## Value of intestinal flora for predicting blood pressure attainment
The ROC curves were plotted for the variance factors derived from the multifactorial regression analysis, and the results showed that the florae were ranked from highest to lowest according to the predictive value: ACE (AUC = 85.282), Streptococcus (AUC = 82.705), Akkermansia (AUC = 77.333), Paraprevotella (AUC = 66.154) and F/B (AUC = 60.436) (see Fig. 7 and Table 3).Fig. 7Receiver operating characteristic curves of factors of intestinal flora differences in the two groups of patientsTable 3ROC curve parametersVariablesAreaStd. ErrorAsymptotic Sig$95\%$ CILower boundUpper boundF/B0.3960.0510.0440.2960.495ACE0.8530.0330.0000.7890.917Akkermansia0.7730.0430.0000.6900.857Streptococcus0.1730.0360.0000.1030.243Paraprevotella0.3380.0500.0020.2410.436ROC receiver operating characteristic curve
## Discussion
A comparative analysis of the differential indicators between the two groups of patients and a multivariate regression analysis were performed to statistically identify indicators that may affect blood pressure attainment. The results revealed that intestinal flora F/B (OR: 0.559, $95\%$ CI 0.336–0.930), the genus Streptococcus (OR: 0.994, $95\%$ CI 0.990–0.998) and the genus Paraprevotella (OR: 0.978, $95\%$ CI 0.964–0.993) were negatively associated with blood pressure attainment, and ACE (OR: 1.273, $95\%$ CI 1.042–1.556) and the genus Akkermansia (OR: 1.022, $95\%$ CI 1.003–1.043) were positively associated with blood pressure attainment. The differences persisted after correction for age, sex and BMI. The ROC curves for the genus level of differential bacteria were plotted to assess the predictive value of gut flora on blood pressure attainment, and the results revealed that ACE (AUC = 85.282), Streptococcus (AUC = 82.705) and Akkermansia (AUC = 77.333) had fair predictive specificity and sensitivity.
*The* general clinical data of the two groups of patients did not differ significantly in terms of age, BMI, exercise and diet, which may have interfered with the study outcome, but there were differences in yoghurt intake. Some studies have shown that probiotic supplementation can reduce blood pressure in patients with hypertension [24]. However, after a multifactorial analysis, the present study found no effect of yoghurt intake on blood pressure attainment, which may have been related to the small number of patients and the yoghurt intake classification. Patients differed in the comparison of phyla levels in terms of Actinobacteria, Verrucomicrobia, Bacteria (unclassified) and F/B indices. Only one F/B was finally included in the regression analysis, which is because during the analysis of this bacteriophage assay, under the detected phylum-level classification of Actinobacteria, other detected bacteria (e.g., Senegalimassilia, Collinsella and Adlercreutzia) were excluded from the genus-level comparison because of their low detection rates. Considering the actual clinical situation and to avoid the duplication of statistics, only the genus-level Bifidobacterium was included in the regression analysis without the phylum-level Actinobacteria; only one genus-level bacteria, Akkermansia, was analysed under the phylum level of Verrucomicrobia; this bacterium was different in the subsequent genus-level comparison, so a regression analysis was performed by genus level, and thus, it was not included. The phylum Bacteria (unclassified) was not included in the statistics, because it did not have any clinical application.
The F/B is an important indicator of intestinal flora balance: the larger the F/B value, the worse the flora balance and the more serious the flora disorder. Studies showed that the feeding of minocycline to pregnant and lactating rats resulted in an increased intestinal flora–F/B ratio and increased blood pressure in the offspring, accompanied by decreased levels of plasma acetate and butyric acid [25]. In another study, the exogenous supplementation of butyric acid or acetic acid in spontaneously hypertensive rats prevented an increase in blood pressure and an increase in the F/B ratio [26]. Furthermore, short-chain fatty acids are metabolites of intestinal flora, mainly butyric acid, acetic acid and propionic acid, with hypotensive, immunomodulatory and cardioprotective functions [27, 28]. These studies suggest that F/B is closely related to blood pressure, indicating that the intestinal flora of patients in the blood pressure attainment group in this study may provide more short-chain fatty acids to enhance the antihypertensive effect.
For the comparison of flora diversity, α diversity reflected the diversity, homogeneity and abundance of the distribution of the intestinal flora in the two groups of patients, and as previous studies in humans [29] and rats [30] had confirmed it to be correlated with blood pressure, it was included in the statistics. However, the β-diversity analysis only aimed to identify a significant difference between the two groups of flora and did not clearly propose the index of difference; it was used to describe the general difference in flora and to guide the subsequent analysis of the specific differences in the flora of the two groups of patients. The NMDS analysis was not included in the regression analysis, because the stress value was too high and might not have reflected the true situations of the two groups.
During the initial comparison of genus levels, a large number of differential bacteria were found, but from a practical point of view, the bacteria that were not identified at the genus level and those that were too small in number (the mean value of the genus level OUT in the two groups was < 10) were excluded. The bacteria that accounted for more than one-third of the blanks in both groups were also excluded. Finally, the four genus-level bacteria with differences were counted for the regression analysis. Comparing the differences in genus levels, *Streptococcus and* Paraprevotella were higher in the gut, and Akkermansia and Bifidobacterium were lower in the uncontrolled group compared with the controlled group. An increase in Paraprevotella and a decrease in Akkermansia have been observed in hypertensive rats during the progression from compensated cardiac hypertrophy to heart failure [31]. Chang et al. [ 32] found a lower abundance of Bifidobacterium in the intestinal flora of women with pre-eclampsia. Jin et al. [ 33] discovered that Akkermansia, propionic acid or butyric acid significantly reduced symptoms in rats with pre-eclampsia. Zhang et al. [ 34] found higher pharyngeal *Streptococcus levels* in patients with pulmonary hypertension compared with those of healthy subjects. Liu et al. [ 35] reported fewer genera of short-chain fatty acid-producing bacteria and more genera of *Streptococcus associated* with inflammation in the intestinal flora of patients with primary aldosteronism compared with the flora of a healthy group. It has also been reported that *Paraprevotella is* involved in the pathogenesis of hypertension in salt-sensitive rats [36]. The results of all these studies support those of the present research in one way or another.
The regression statistics of the indicators analysed for differences between the groups showed that intestinal flora F/B (OR: 0.559, $95\%$ CI 0.336–0.930) and the genera levels of Streptococcus (OR: 0.994, $95\%$ CI 0.990–0.998) and Paraprevotella (OR: 0.978, $95\%$ CI 0.964–0.993) were negatively associated with blood pressure attainment, while ACE (OR: 1.273, $95\%$ CI 1.042–1.556) and Akkermansia (OR: 1.022, $95\%$ CI 1.003–1.043) were positively associated with blood pressure; this association persisted after correction for age, sex and BMI. The ROC curves for the predictive value of blood pressure compliance were plotted, with ACE (AUC = 85.282), Streptococcus (AUC = 82.705) and Akkermansia (AUC = 77.333) having the highest predictive values, providing some basis for later blood pressure compliance in patients attending clinics.
## Limitations
This study has some limitations. First, it was a single-centre cross-sectional study in which most of the participating population were patients living in the local neighbourhood. Therefore, a multicenter study is needed to expand the representativeness of the research. Second, although the sample size of this study was small, our research found a statistically significant correlation between intestinal flora and the anti-hypertensive effect of medication for grade-2 hypertension. Finally, only intestinal flora was analysed in this study, and subsequent analyses of blood and urine specimens from patients are required to reveal the mechanisms by which intestinal flora affects blood pressure. We plan to conduct a large-population multicenter study in the future to improve the credibility and extrapolation of our findings.
## Conclusions
There were significant differences in the intestinal flora of patients enrolled in the controlled blood pressure group compared with those in the uncontrolled group. The ACE genus levels of *Streptococcus and* Akkermansia could provide some prediction of late blood pressure compliance or non-compliance in patients with hypertension.
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|
---
title: 'Lifestyle factors associated with inflammatory bowel disease: data from the
Swiss IBD cohort study'
authors:
- Severin A. Lautenschlager
- Mamadou Pathé Barry
- Gerhard Rogler
- Luc Biedermann
- Philipp Schreiner
- Alexander R. Siebenhüner
- Karim Abdelrahman
- Karim Abdelrahman
- Gentiana Ademi
- Patrick Aepli
- Amman Thomas
- Claudia Anderegg
- Anca-Teodora Antonino
- Eva Archanioti
- Eviano Arrigoni
- Diana Bakker de Jong
- Bruno Balsiger
- Polat Bastürk
- Peter Bauerfeind
- Andrea Becocci
- Dominique Belli
- José M. Bengoa
- Janek Binek
- Mirjam Blattmann
- Stephan Boehm
- Tujana Boldanova
- Jan Borovicka
- Christian P. BellBraeggeri
- Stephan Brand
- Lukas Brügger
- Simon Brunner
- Patrick Bühr
- Bernard Burnand
- Sabine Burk
- Emanuel Burri
- Sophie Buyse
- Dahlia-Thao Cao
- Ove Carstens
- Dahlia-Thao Cao
- Dominique H. Criblez
- Sophie Cunningham
- Fabrizia D’Angelo
- Philippe de Saussure
- Lukas Degen
- Joakim Delarive
- Christopher Doerig
- Barbara Dora
- Susan Drerup
- Mara Egger
- Ali El-Wafa
- Matthias Engelmann
- Jessica Ezri
- Christian Felley
- Markus Fliegner
- Nicolas Fournier
- Montserrat Fraga
- Yannick Franc
- Pascal Frei
- Remus Frei
- Michael Fried
- Florian Froehlich
- Raoul Ivano Furlano
- Luca Garzoni
- Martin Geyer
- Laurent Girard
- Marc Girardin
- Delphine Golay
- Ignaz Good
- Ulrike Graf Bigler
- Beat Gysi
- Johannes Haarer
- Marcel Halama
- Janine Haldemann
- Pius Heer
- Benjamin Heimgartner
- Beat Helbling
- Peter Hengstler
- Denise Herzog
- Cyrill Hess
- Roxane Hessler
- Klaas Heyland
- Thomas Hinterleitner
- Claudia Hirschi
- Petr Hruz
- Pascal Juillerat
- Carolina Khalid-de Bakker
- Stephan Kayser
- Céline Keller
- Christina Knellwolf-Grieger
- Christoph Knoblauch
- Henrik Köhler
- Rebekka Koller
- Claudia Krieger-Grübel
- Patrizia Künzler
- Rachel Kusche
- Frank Serge Lehmann
- Andrew Macpherson
- Michel H. Maillard
- Michael Manz
- Astrid Marot
- Rémy Meier
- Christa Meyenberger
- Pamela Meyer
- Pierre Michetti
- Benjamin Misselwitz
- Patrick Mosler
- Christian Mottet
- Christoph Müller
- Beat Müllhaupt
- Leilla Musso
- Michaela Neagu
- Cristina Nichita
- Jan Niess
- Andreas Nydegger
- Nicole Obialo
- Diana Ollo
- Cassandra Oropesa
- Ulrich Peter
- Daniel Peternac
- Laetitia Marie Petit
- Valérie Pittet
- Rachel Kusche
- Daniel Pohl
- Marc Porzner
- Claudia Preissler
- Nadia Raschle
- Ronald Rentsch
- Alexandre Restellini
- Sophie Restellini
- Jean-Pierre Richterich
- Frederic Ris
- Branislav Risti
- Marc Alain Ritz
- Nina Röhrich
- Jean-Benoît Rossel
- Vanessa Rueger
- Monica Rusticeanu
- Markus Sagmeister
- Gaby Saner
- Bernhard Sauter
- Mikael Sawatzki
- Michael Scharl
- Martin Schelling
- Susanne Schibli
- Hugo Schlauri
- Dominique Schluckebier
- Daniela Schmid
- Sybille Schmid-Uebelhart
- Jean-François Schnegg
- Alain Schoepfer
- Vivianne Seematter
- Frank Seibold
- Mariam Seirafi
- Gian-Marco Semadeni
- Arne Senning
- Christiane Sokollik
- Joachim Sommer
- Johannes Spalinger
- Holger Spangenberger
- Philippe Stadler
- Peter Staub
- Dominic Staudenmann
- Volker Stenz
- Michael Steuerwald
- Alex Straumann
- Bruno Strebel
- Andreas Stulz
- Michael Sulz
- Aurora Tatu
- Michela Tempia-Caliera
- Joël Thorens
- Kaspar Truninger
- Radu Tutuian
- Patrick Urfer
- Stephan Vavricka
- Francesco Viani
- Jürg Vögtlin
- Roland Von Känel
- Dominique Vouillamoz
- Rachel Vulliamy
- Paul Wiesel
- Reiner Wiest
- Stefanie Wöhrle
- Samuel Zamora
- Silvan Zander
- Tina Wylie
- Jonas Zeitz
- Dorothee Zimmermann
journal: BMC Gastroenterology
year: 2023
pmcid: PMC10008613
doi: 10.1186/s12876-023-02692-9
license: CC BY 4.0
---
# Lifestyle factors associated with inflammatory bowel disease: data from the Swiss IBD cohort study
## Abstract
### Background
Various environmental risk factors have been associated with the pathogenesis of inflammatory bowel disease. In this study we aimed to identify lifestyle factors that affect the onset of Crohn’s disease and ulcerative colitis.
### Methods
2294 patients from the Swiss IBD Cohort Study received a questionnaire regarding physical activity, nutritional habits and status of weight. In addition, a control group was formed comprising patients’ childhood friends, who grew up in a similar environment.
### Results
Overall, 1111 questionnaires were returned (response rate: $48.4\%$). Significantly more patients with inflammatory bowel disease reported no regular practice of sport during childhood and beginning of adulthood compared to the control group ($$p \leq 0.0001$$). No association between intake of refined sugar and onset of inflammatory bowel disease was observed. More patients with Crohn’s disease compared to ulcerative colitis and controls suffered from overweight during childhood ($12.8\%$ vs. $7.7\%$ and $9.7\%$, respectively; $$p \leq 0.027$$).
### Conclusions
Our study underlines the relevance of environmental factors in the development of inflammatory bowel disease. Our results imply a protective effect of physical activity regarding the onset of inflammatory bowel disease.
## Introduction
Inflammatory bowel disease (IBD) is a chronic and relapsing inflammatory disorder of the gastrointestinal tract and includes the main subtypes Crohn’s disease (CD) and ulcerative colitis (UC). Incidence and prevalence of IBD in Western countries have been on the rise since the beginning of the twentieth century [1]. In developing countries, an increase of IBD incidence and prevalence occurred over the last 30 years, when Westernized lifestyle and dietary habits were adopted [2, 3]. A contribution of environmental factors to the rapid rise of IBD incidence is highly probable, considering that genetic susceptibility has been present in human beings since thousands of years without significant changes in this short period. Environmental risk factors, which affect the course of IBD are currently studied with epidemiological approaches and some influencing factors, such as breastfeeding, diet or antibiotic use have been identified [4–6].
Diet is widely considered as a key environmental factor. Dietary changes affect the composition of the gut microbiome, which may influence mechanisms of immunological tolerance [7, 8]. Western diet is mainly composed of high-sugar, low-fiber, animal-protein and fat, but low ingestion of vegetables [9, 10]. Epidemiological data suggest, that a ‘Westernization’ of the diet may induced mucosal inflammation in susceptible individuals and may act as promoter in the pathogenesis of IBD [11]. Recent data demonstrated a positive correlation between pro-inflammatory and ultra-processed food intake and the risk of developing IBD [12, 13].
The reason why a dysbiosis of the intestinal microbiota promotes the development of IBD is not fully understood. A plausible explanation is the increase in potentially pathogenic bacterial species combined with a decrease in protective bacteria, resulting a disruption of local immune homeostasis, increased mucosal permeability and loss of immune tolerance [14].
Literature data is scarce regarding the association between IBD and dietary behavior in the childhood period, considering that a subclinical intestinal inflammation can exist a long time before the outbreak of the disease [7]. In addition, not much is known about overweight in childhood and its influence on development of IBD or disease activity. Previous work implies an association between overweight and more severe disease activity in children with IBD [15].
Better knowledge regarding dietary habits would be of utmost importance for patients to reduce flare-ups, but also to prevent the development of IBD.
Likewise, breastfeeding and mode of delivery are considered as an important factor regarding the composition of the microbiome in the early childhood period and have been associated with the development of IBD [16–18]. A review of Ananthakrishnan et al. [ 19] outlined a protective effect of being breastfed for IBD. Concerning the mode of delivery as a risk factor for IBD, findings in literature are inconsistent. While various studies postulated Caesarean section (C-section) as a risk factor for IBD, a population-based study of Bernstein et al. found no association between IBD and mode of delivery [16, 20, 21].
Physical activity levels have been associated as a protective factor regarding the risk of IBD due to a reduction of systemic inflammation [22–26]. Physical activity has various effects on immunomodulatory processes and affects the balance of inflammatory and anti-inflammatory mechanisms [27, 28]. Investigations showed that incidence of autoimmune diseases such as rheumatoid arthritis, multiple sclerosis or psoriasis is higher in patients less engaged in physical activity [29].
According to present experience we have a lack of knowledge concerning nutrition and diet as well as physical activities among patient with IBD and their potential impact on disease development. Increased knowledge of early-life risk factors of IBD ensures better prevention of disease and may reduce the number of IBD patients.
Main goal of this study is to figure out the impact of lifestyle factors such as dietary habits, weight status and physical activity on IBD in Swiss patients, especially regarding childhood period and adolescence.
## Study design
Prospectively obtained data from patients of the Swiss IBD cohort study (SIBDCS), a nationwide cohort study funded by the Swiss National Science Foundation were analyzed. In addition to patients diagnosed with IBD, the cohort has included two related cohorts of patient’s friends and mothers to evaluate the impact of various environmental factors on IBD risk. Clinical and treatment data have been prospectively captured with a yearly follow-up and entered into a database since the establishment of the cohort in 2006. Purposes and methodology of SIBDC have been described elsewhere [30, 31].
Questionnaires regarding various environmental factors were distributed to IBD patients ($$n = 2294$$) between December 2015 and October 2016 in Swiss national languages. Questionnaires returned until January 2018 were included in this study. In addition, identical questionnaires were sent out to up to three matched childhood friends to form a control group (friend’s cohort), which had been exposed to a similar environment as the patients. To double-check information relating to patient’s early childhood period, a questionnaire addressing the patients’ mothers was sent out. Concerning potential influencing factors on IBD such as physical activity, overweight and obesity we focused on childhood period and beginning of adulthood.
Our intention was to obtain information from the point of birth to the age of about 4 years. In the interest of simplification, we defined this period in our questionnaires ‘first years of life’.
We further categorized sugary foods into sugary drinks (Coca-Cola, Fanta etc.), artificial sugary (jelly-babies, bonbons) and natural sugary (e.g. chocolate).
## Statistical analysis
Statistical analyses were performed using the Version 16.0 of the Stata software (College Station, TX 77,845 USA) with univariate, bivariate and multivariate analysis. Univariate analyses were performed to summarize the variables and multivariate analysis was used to evaluate the impact of some explanatory variables on dietary habits. The qualitative variables were summarized with percentages and the quantitative variables with the mean and the standard deviation when they were normally distributed or with the median and the interquartile range when they were not. For bivariate analyses, the Chi-square test was performed to study the relationship between the categorical variables. We performed the Student test to compare the means between the groups when the variables are normally distributed. The Wilcoxon–Mann–Whitney test was used to compare the means when the variables are not normally distributed.
## Clinical characteristics of the study population
Out of 2294 questionnaires handed out to SIBDC patients, 1111 were sent back (response rate: $48.4\%$). Additional information from mother questionnaires were available for 305 out of 1111 responding patients (response rate: $27.5\%$). In addition, we obtained 225 questionnaires from at least one patients’ friend (Response rate: $20.3\%$). A total of 352 friends’ questionnaires were received.
Divided in subtypes, we received 610 questionnaires from patients with CD and 468 with UC/IC. For further clinical and epidemiological parameters of CD and UC/IC we refer to Table 1.Table 1Clinical characteristics of the study populationCD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-valueGender [n (%)] Male271 ($44.4\%$)232 (49.6)130 (35.6)0.001 Female339 ($55.6\%$)236 (50.4)235 (64.4)Age [y]48.5, 15.550, 1436, 90.0001 (Mean, SD, range)18–8719–8518–75BMI [kg/m2]23.6, 3.624.5, 3.721.3, 3.10.224 (Mean, SD, range)18–3018–31.518–25Age at diagnosis [y]31, 1434, 140.0001 (Mean, SD, range)1–758–78Disease duration [y]18, 1116, 100.0034 (Mean, SD, range)0–530–52CDAI20, 41 (Median, IQR, range)0–230MTWAI1, 3 (Median, IQR, range)0–17Smoking status at diagnosis [n (%)] Non-smoker350 (57.7)371 (75.7)0.001 Smoker235 (38.7)93 [19] Unknown22 (3.6)26 (5.3)Current smoking status [n (%)] Non-smoker453 (74.6)319 (85.5)0.001 Smoker151 (24.9)66 (13.5) Unknown3 (0.5)5 [1]Therapy history (ever treated with) [n (%)] 5-ASA369 (60.8)471 (96.0)0.001 Steroids528 [87]398 [81]0.009 Immunomodulators492 (81.1)300 (61.2)0.001 Anti-TNF384 (63.3)169 (34.5)0.001EIM [n (%)] Any380 (62.6)219 (44.7)0.001 Arthritis330 (54.4)173 (35.3)0.001 Uveitis/Iritis79 [13]30 (6.1)0.001 Pyoderma gangraenosum8 (1.3)9 (1.8)0.489 Erythema nodosum62 (10.2)15 (3.1)0.001 Aphtous/Oral ulcers93 (15.3)25 (5.1)0.001 Ankylosing spondylitis38 (6.3)16 (3.3)0.023 PSC8 (1.3)15 (3.1)0.045Complications [n (%)] Perianal fistula164 [27]– Other fistula111 (18.3) Abscess137 (22.6) Stenosis264 (43.5)Surgery history [n (%)] Any313 (51.6)53 (10.8)0.001 Intestinal surgery258 (42.5)46 (9.4)0.001 Fistulas or abscess surgery147 (24.2)10 [2]0.001All tests were two-sided, with a P value of less than 0.05 considered to indicate statistical significance. These results are highlighted in bold
## Physical activity during childhood and beginning of adulthood
For the period of childhood to the begin of adulthood, the report for 257 ($42.1\%$) patients with CD and 181 ($38.7\%$) patients with UC/IC presented no regular activity, whereas only 107 ($29.3\%$) persons from the control group did not practice sport regularly ($$p \leq 0.001$$) (Table 2). More individuals from the control group (179, $49\%$) practiced sport alone or in a club, than patients with CD (248, $40.7\%$) or UC/IC (197, $42.1\%$, $$p \leq 0.032$$). There was a trend that more UC/IC patients (43, $9.2\%$) practiced frequently high-level sport than CD patients (34, $5.6\%$, $$p \leq 0.051$$). Regarding practicing endurance sport no difference between IBD patients and individuals from the control group was observed. Table 2Physical activity during childhood and beginning of adulthoodCD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-value CD versus UC/ICp-value IBD versus controlsRegular practice of sport [n (%)] Yes353 (57.9)287 (61.3)258 (70.7)0.2520.001 No257 (42.1)181 (38.7)107 (29.3)Regular practice of sport alone or in a club [n (%)] Yes248 (40.7)197 (42.1)179 [49]0.6350.032 No362 (59.3)271 (57.9)186 [51]Practice sport more than people in the same age [n (%)] Yes65 (10.7)49 (10.5)51 [14]0.9220.210 No545 (89.3)419 (89.5)314 [86]High level practice of sport frequently [n (%)] Yes34 (5.6)43 (9.2)32 (8.8)0.0510.491 No576 (94.4)425 (90.8)333 (91.2)Endurance sport [n (%)] Yes2 (0.3)3 (0.6)4 (1.1)0.4530.337 No608 (99.7)465 (99.4)361 (98.9)All tests were two-sided, with a P value of less than 0.05 considered to indicate statistical significance. These results are highlighted in bold
## Breastfeeding and mode of birth
A higher number of individuals from the control group (243, $68.1\%$) had been breastfed as compared to patients with IBD, especially with IC/UC (279, $59.9\%$, $$p \leq 0.002$$) (Table 3). Significantly more persons from the control group reported that their baby bottles and teats had been sterilized (139, $43.8\%$) compared to IBD patients (CD: 172, $32.6\%$, UC/IC: 127, $31.4\%$). The number of individuals from the control group born by C-section (50, $13.7\%$) was significantly higher than in the IBD group ($$p \leq 0.001$$). Premature birth has significantly more often occurred in the CD fraction (61, $10.2\%$) than in the control group (28, $7.8\%$). Table 3Breastfeeding and mode of birthCD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-value CD versus UC/ICp-value IBD versus controlsHave you been breastfed [n (%)] Yes398 (65.7)279 (59.9)243 (68.1)0.0330.002 No105 (17.3)112 [24]81 (22.7) Don’t know103 [17]74 (15.9)32 [9] Missing439Sterilization of baby bottles and teats [n (%)] Yes172 (32.6)127 (31.4)139 (43.8)0.2840.001 No62 (11.7)35 (8.6)49 (15.5) Don’t know293 (55.5)243 [60]129 (40.7) Missing826348Sterilization frequency of baby bottles and teats [n (%)] After each meal45 (11.5)30 (9.7)40 (17.5)0.7810.021 *Once a* day45 (11.5)40 [13]42 (18.4) Once per week19 [5]17 (5.5)12 (5.3) Don’t know283 [72]221 (71.8)134 (58.8) Missing218160137Mode of birth Natural childbirth [n (%)]495 (81.2)389 (83.1)291 (79.3)0.4030.446 Caesarean-section [n (%)]49 [8]28 [6]50 (13.7)0.1950.001 Forceps delivery [n (%)]14 (2.3)13 (2.8)6 (1.6)0.6150.554 Delivery with cupping [n (%)]13 (2.1)17 (3.6)10 (2.7)0.1370.330 I don’t know [n (%)]37 (6.1)22 (4.7)7 (1.9)Are you born at term [n (%)] Yes423 (70.7)349 (75.2)268 (74.4)0.1110.017 Premature birth61 (10.2)30 (6.5)28 (7.8) Birth triggered after the term37 (6.2)19 (4.1)32 (8.9) Don’t know75 (12.5)64 (13.8)32 [9] Missing1465All tests were two-sided, with a P value of less than 0.05 considered to indicate statistical significance. These results are highlighted in bold
## Dietary habits
Significantly more individuals from the control group (187, $51.2\%$) reported they had drunk packet cow milk from the supermarket during the first years of life than IBD patients ($$p \leq 0.001$$) (Table 4). On the other hand, more patients suffering from IBD (CD: 158, $25.9\%$, UC/IC: 118, $25.2\%$) drank cow milk directly from the farm during the first years of life than exponents from the control group (62, $17\%$, $$p \leq 0.003$$). Concerning the kind of milk there were slightly more individuals from the controls (53, $14.5\%$) who drank semi-skimmed milk during the first years of life than patients diagnosed with IBD (CD: 57, $9.3\%$, UC/IC: 49, $10.5\%$). Comparing other kind of milk between controls and IBD patients showed no significant difference. Table 4Nutritional habits during the first years of lifeCD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-value CD versus UC/ICp-value IBD versus controlsDid you regularly drink cow’s milk during the first years of your life Packed (super market) [n (%)]208 (34.1)145 [31]187 (51.2)0.2800.001 Directly from the farm [n (%)]158 (25.9)118 (25.2)62 [17]0.7980.003 Yes one or the other [n (%)]92 (15.1)81 (17.3)58 (15.9)0.3240.612 No [n (%)]38 (6.2)32 (6.8)18 (4.9)0.6880.514I don’t know [n (%)]115 (18.8)94 (20.1)39 (10.7)What kind of milk did you drink in the first years Whole milk $3.5\%$ fat [n (%)]305 [50]249 (53.2)198 (54.3)0.2970.371 Semi-skimmed milk $1.5\%$ fat [n (%)]57 (9.3)49 (10.5)53 (14.5)0.5380.04 Skimmed milk < $0.3\%$ [n (%)]5 (0.8)4 (0.9)4 (1.1)0.9500.900 Raw milk [n (%)]88 (14.4)78 (16.7)51 [14]0.3120.478 Milk without lactose [n (%)]3 (0.5)000.1290.128 Soy milk [n (%)]3 (0.5)2 (0.4)4 (1.1)0.8770.412 I don’t know [n (%)]158 [26]110 (23.8)69 (19.1)Did you tolerate the milk well Yes [n (%)]398 (65.3)343 (73.3)279 (76.4)0.0050.001 No [n (%)]131 (21.5)74 (15.8)62 [17]0.0190.041 I don’t know [n (%)]73 [12]44 (9.4)17 (4.7)What are the symptoms that you had after consuming milk Bloating [n (%)]60 (9.8)37 (7.9)32 (8.8)0.2720.541 Pain [n (%)]29 (4.7)17 (3.6)11 [3]0.3660.367 Diarrhea [n (%)]63 (10.3)21 (4.5)19 (5.2)0.0010.001 Nausea [n (%)]30 (4.9)15 (3.2)17 (4.7)0.1630.360 Other [n (%)]83 (13.6)62 (13.3)34 (9.3)0.8640.115 I don’t know [n (%)]90 (14.8)64 (13.7)24 (6.6)All tests were two-sided, with a P value of less than 0.05 considered to indicate statistical significance. These results are highlighted in bold 279 out of 365 controls ($76.4\%$) reported they had tolerated the milk well during the first years of life, while significant less patients suffering from IBD tolerated the milk well ($$p \leq 0.001$$). Comparing the subgroups of IBD there were significant more patients with CD (131, $21.5\%$) complaining about symptoms after drinking milk in the first years of life than patients with UC/IC (74, $15.8\%$, $$p \leq 0.019$$). When investigating the several symptoms in more detail a higher number of CD patients (63, $10.3\%$) complained about diarrhea during the first years of life after drinking milk than patients suffering from UC (21, $4.5\%$, $$p \leq 0.001$$) and controls (19, $5.2\%$).
When observing the analysis of eating behavior of sugary foods until the age of 18 years no significant differences between IBD patients and controls stood out (Table 5). Regardless of the stage of life more patients diagnosed with IBD (CD: 24, $3.9\%$, UC/IC: 17, $3.6\%$) reported to feed their selves on a vegetarian basis than individuals from the control group (Table 6). In addition, more patients diagnosed with CD (88, $14.4\%$) consumed a meat-rich diet than patients with UC/IC (40, $8.6\%$, $$p \leq 0.003$$) and controls (39, $10.7\%$) Considering other special diets such as gluten free or vegan alimentation no significant difference between IBD patients and controls has been observed. Table 5Sugary foods until the age of 18CD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-value CD versus UC/ICp-value IBD versus controlsSugary drinks [n (%)] More than other children24 (4.1)21 (4.6)13 (3.6)0.7160.906 In average like other children285 (48.1)229 [50]176 (48.8) Less than other children283 (47.8)208 (45.4)172 (47.7) Missing18104Eating artificial sugary [n (%)] More than other children33 (5.7)25 (5.5)18 [5]0.7650.278 In average like other children280 [48]228 [50]203 (56.2) Less than other children269 (46.1)203 (44.5)140 (38.8) Missing27124Eating natural sugary [n (%)] More than other children54 (9.2)45 (9.8)36 (9.9)0.7200.427 In average like other children406 (69.1)324 (70.4)266 (73.5) Less than other children128 (21.8)91 (19.8)60 (16.6) Missing2281Table 6Eating habits or special dietsCD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-value CD versus UC/ICp-value IBD versus controlsConsuming food like the average [n (%)] Yes504 (82.6)401 (85.7)303 (83.1)0.1750.369 No106 (17.4)67 (14.3)62 [17]Vegetarian alimentation [n (%)] Yes24 (3.9)17 (3.6)30 (8.2)0.7970.003 No586 (96.1)451 (96.4)335 (91.8)Vegan alimentation [n (%)] Yes2 (0.3)4 (0.9)4 (1.0)0.2490.329 No608 (99.7)464 (99.1)361 (98.9)Gluten free [n (%)] Yes12 [2]10 (2.1)5 (1.4)0.8450.701 No598 [98]458 [98]360 (98.6)Low-lactose or lactose free [n (%)] Yes42 (6.9)27 (5.8)16 (4.4)0.4580.273 No568 (93.1)441 (94.2)349 (95.6)Fastfood or finished products more than 3 times a week [n (%)] Yes8 (1.3)8 (1.7)3 (0.8)0.5920.537 No602 (98.7)460 (98.3)362 (99.2)Meat-rich diet [n (%)] Yes88 (14.4)40 (8.6)39 (10.7)0.0030.009 No522 (85.6)428 (91.5)326 (89.3)All tests were two-sided, with a P value of less than 0.05 considered to indicate statistical significance. These results are highlighted in bold
## Overweight or obesity during childhood and beginning of adulthood
One hundred and four out of 610 patients with CD ($17.2\%$) reported they had insufficient weight or were very thin during childhood compared to children of their age (Table 7). Compared with the controls (35, $9.7\%$) and patients diagnosed with UC/IC (36, $7.7\%$, $$p \leq 0.027$$) significantly more patients with CD suffered from overweight during childhood (77, $12.8\%$, $$p \leq 0.012$$).Table 7Overweight or obesity during childhood and beginning of adulthood compared to children of their ageCD ($$n = 610$$)UC/IC ($$n = 468$$)Controls ($$n = 365$$)p-value CD versus UC/ICp-value IBD versus controlsDuring childhood [n (%)] Insufficient weight/very thin104 (17.2)77 (16.5)54 (14.9)0.0270.012 Weight in the average410 (67.9)341 (73.2)268 [74] Overweight, round, coated77 (12.8)36 (7.7)35 (9.7) Obese5 (0.8)05 (1.4) I don’t know7 (1.2)9 [2]0 Missing763At the beginning of adulthood [n (%)] Insufficient weight/very thin102 (16.9)56 (12.1)35 (9.7)0.0270.014 Weight in the average441 (73.3)376 [81]287 (79.3) Overweight, round, coated53 (8.8)28 [6]37 (10.2) Obese4 [1]2 (0.4)3 (0.8) Missing863All tests were two-sided, with a P value of less than 0.05 considered to indicate statistical significance. These results are highlighted in bold Similarly, at beginning of adulthood, more CD patients (102, $16.9\%$) had insufficient weight or were very thin than UC/IC patients (56, $12.1\%$, $$p \leq 0.027$$) or controls (35, $9.7\%$). In contrary to the childhood period, controls (37, $10.2\%$) tended to have more overweight than patients with CD (58, $8.8\%$) and UC/IC (28, $6\%$).
## Discussion
Based on 1111 questionnaires of IBD patients we aimed to identify associations between environmental factors and the development of IBD. Our data confirm physical activity as a protective factor for IBD. Consuming meat-rich diet was associated with developing CD. On the other hand, no correlation between intake of sugar and development of IBD was observed. Overweight during childhood was associated for CD, but not for UC. Underweight during childhood and adulthood was associated with both, CD and UC/IC.
Our results support the hypothesis of a protective effect of physical activity regarding the development of IBD. Patients diagnosed with IBD reported to be less physically active during childhood and beginning of adulthood than persons from the control group. These findings are in line with the result of a review of meta-analyses recently published, that demonstrated a protective effect of physical activity regarding the development of CD [32]. On the other hand, a Danish prospective cohort study reported no association between physical activity and risk of IBD [33]. A possible explanation for this discrepancy to our result is, that the Danish study did not investigate the association between timing in life of physical activity and risk of IBD. The influence of physical activity on the onset of IBD is still unclear and evidence in literature is scarce. There is consensus in literature that physical activity has an impact on various aspects of the immune system and autoimmune diseases [29]. An investigation of Steensberg et al. [ 34] implied that sporting activity induces a shift in the Th1/Th2 balance to a decrease in Th1 cells. Th1 is responsible for secretion of proinflammatory cytokines as IL-1, IL-2, IL-6 and IL-8, whereas anti-inflammatory cytokines as IL-4, IL-10 and IL-13 are secreted by Th2 cells. Thus, the balance between proinflammatory and anti-inflammatory mechanisms is highly affected by the Th1/Th2 cells ratio and responsible for the types of immune responses that patients develop [35]. Considering other diseases driven by autoimmune processes such as rheumatoid arthritis, multiple sclerosis or psoriasis, studies have shown an increased incidence in patients less engaged in physical activity [29].
Lack of exercise may result in obesity, what is assumed to be a cause for a chronic low-grade inflammation in humans [36]. It is explained, amongst others, by a predominance of pro-inflammatory macrophages in mesenteric visceral adipose tissue, that is responsible for secretion of various inflammatory cytokines, including IL-1 and TNF [37]. Kugathasan et al. [ 38] demonstrated that about 9–$10\%$ of children with CD and 20–$34\%$ of children with UC had an increased BMI above the 85th percentile at diagnosis. In contrary, a recently published study demonstrated no worsened disease activity 1 year after diagnosis of IBD of overweight children compared with normal weight children [39]. Our patients with CD reported to be more often overweight compared to children of their age during childhood. Both, CD and UC patients reported to be underweighted compared to children of their age during childhood and adulthood. A recently published meta-analysis reported an positive association between underweight and the onset of CD, but not for UC [40].
The impact of breastfeeding on the onset of IBD is also under debate. Previous trials demonstrated that alteration in the composition of the microbiota disrupts microbial mediated mechanisms of immunological tolerance [7, 8]. Human milk contains, among others, oligosaccharides with prebiotic effects including growth of Bifidobacteria which may affect the intestinal flora and influence the risk of IBD [41]. Our data support the assumption of a protective effect of breastfeeding, especially considering the onset of UC/IC. Furthermore, our attempt was to obtain information regarding breast milk substitution and identify bottle-feeding and frequency of sterilization of baby bottles and teats as possible risk factors for IBD. Investigations suggest a positive association of breastfeeding and development of IBD, but some controversy remains in literature [6, 17, 42]. Unfortunately, most of patients, friends and mothers had to answer the questions in this part with ‘don’t know’, whereby the data is not conclusive. Therefore, additional investigations are warranted.
Our findings do not support the thesis that C-section enhances the risk of IBD, as more persons from the control group reported to be born via C-section. Confounders may distort the result and the findings have to be treated with caution. A population-based analysis of Bernstein reported no association of C-section and IBD [21]. On the other hand a meta-analysis by Li et al. [ 16] indicated an increased risk for CD but not for UC after Cesarean delivery.
Interestingly, significantly more patients with CD than persons from the control group reported to be born prematurely. Although perinatal mortality has been considerably reduced during the past years, prematurely born infants are still liable to a higher mortality and morbidity rate in comparison with infants born at term [43]. These finding are in line with a work of Sonntag et al. [ 44] that identified preterm birth as a risk factor for CD.
Investigations suggest that diet plays an important role in IBD. Dietary habits have been changed in the western world during recent decades and cause alterations in the composition of the gut microbiota, that may result in aberrant intestinal immune response [45, 46]. A subclinical intestinal inflammation can be present long before occurrence of the first IBD symptoms [7]. For this reason, it is of utmost importance to identify risk factors that affected patients before the clinically manifest disease. Our data indicate that more people from the control group consumed semi-skimmed milk ($1.5\%$ fat) during the first years of life than patients with IBD. It has been shown that high fat intakes cause an accumulation of secondary bile acids, what is responsible for reduction in growths of *Firmicutes phyla* and Bacteroidetes, both associated with IBD-like dysbiosis [47]. On the other hand, a prospective study of Ananthakrishnan et al. [ 48] indicated no association of fat consumption and IBD in women. Interestingly, patients diagnosed with CD reported to have had a milk intolerance during their first years of life, clinically characterized by diarrhea. A positive correlation between IBD and lactose intolerance is well established in literature, but evidence is scarce that a milk intolerance might be already present during the first years of life and a long time before the diagnosis of the disease [49]. The association between sugar intake and risk of IBD remains controversial. Several studies showed a negative effect of consuming sugary food on the onset of IBD [50–52]. In contrast to these findings, a large prospective study could not identify sugar intake as a risk factor for developing UC [53]. Our data suggest no connection between IBD and eating of neither artificial nor natural sugary until the age of 18. Literature is very scarce regarding dietary habits before 18 and development of IBD. Ananthakrishnan et al. [ 54] published an investigation regarding high school diet and risk of CD and UC, which demonstrated no association between intake of carbohydrates and onset of IBD.
Previous studies have shown a correlation between diet rich in animal protein and development of IBD [55, 56]. Our data support these findings and we report that statistically significant more CD patients consume meat-rich diet in comparison to the control group. A plausible explanation is that high intake of protein result in increased production of potentially toxic bacterial metabolites, what may lead to an impaired epithelial repair process [57]. As well, our data implicate a reduced consumption of vegetarian alimentation in IBD patients, what confirms the assumption that diets in high animal proteins lead to increased risk of IBD.
Our study has strengths as well as weaknesses. A strength is the large amount of IBD patients in our cohort with in total 1111 returned questionnaires. Furthermore, we aimed to reduce the ‘recall bias’ due to 305 returned questionnaires from patients’ mothers.
One limitation is the low overall return of questionnaires. The low response rate of the patient’s friends resulted in a small control group compared to the large number of IBD patients. Probably it was more difficult than expected for patients to reach childhood friends who grew up in a similar environment. Even though a high response rate is preferable, previous studies could demonstrate that there is no evidence of more accurate measurement in surveys with higher response rates [58, 59].
Furthermore, we are aware that our methodology is of risk of ‘recall bias’. Even though the data of patient’s mothers reduce the ‘recall bias’, incorrect memories of behavior during childhood may exist. To confirm our findings further prospective randomised trials are needed. As well, our analyses were not matched for potential confounders. We performed a multivariate analysis included all the investigated risk factors matched for sex, age and smoking status. As there was no significant difference in the results to our published data, we decided to exclude the multivariate analysis.
In conclusion, our data demonstrate the possibility that lifestyle factors such as physical activity, dietary habits and weight status affect the onset of IBD and may play a crucial role in preventing IBD (Table 8). This study indicates that education and prevention strategies may reduce the increasing incidence of patients with inflammatory bowel disease. Table 8Overview of the most important findings regarding effects in regard to the development of CD and UC/IC (Statistical analyses are seen in Tables 2, 3, 4, 5, 6, 7)Environmental factorCDUC/ICPhysical activityProtectiveProtectiveBreastfeedingNo significant effectProtectivePremature birthNegative effectNo significant effectSugar intakeNo significant effectNo significant effectMeat-rich dietNegative effectNo significant effectVegan-/vegetarian alimentation, gluten free diet,No conclusive dataNo conclusive dataOverweight during childhoodNegative effectNo significant effectUnderweight during childhood and adulthoodNegative effectRisk factor
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|
---
title: Rare sugar l-sorbose exerts antitumor activity by impairing glucose metabolism
authors:
- Hui-Lin Xu
- Xiaoman Zhou
- Shuai Chen
- Si Xu
- Zijie Li
- Hideki Nakanishi
- Xiao-Dong Gao
journal: Communications Biology
year: 2023
pmcid: PMC10008635
doi: 10.1038/s42003-023-04638-z
license: CC BY 4.0
---
# Rare sugar l-sorbose exerts antitumor activity by impairing glucose metabolism
## Abstract
Rare sugars are monosaccharides with low natural abundance. They are structural isomers of dietary sugars, but hardly be metabolized. Here, we report that rare sugar l-sorbose induces apoptosis in various cancer cells. As a C-3 epimer of d-fructose, l-sorbose is internalized via the transporter GLUT5 and phosphorylated by ketohexokinase (KHK) to produce l-sorbose-1-phosphate (S-1-P). Cellular S-1-P inactivates the glycolytic enzyme hexokinase resulting in attenuated glycolysis. Consequently, mitochondrial function is impaired and reactive oxygen species are produced. Moreover, l-sorbose downregulates the transcription of KHK-A, a splicing variant of KHK. Since KHK-A is a positive inducer of antioxidation genes, the antioxidant defense mechanism in cancer cells can be attenuated by l-sorbose-treatment. Thus, l-sorbose performs multiple anticancer activities to induce cell apoptosis. In mouse xenograft models, l-sorbose enhances the effect of tumor chemotherapy in combination with other anticancer drugs. These results demonstrate l-sorbose as an attractive therapeutic reagent for cancer treatment.
The rare sugar L-sorbose is shown to decrease cell viability and increase apoptotic cells in culture and to enhance the effect of tumor chemotherapy in combination with sorafenib in mouse xenograft models.
## Introduction
Sugars, in particular glucose, serve as sources of energy and organic carbon in mammalian cells. In normal tissues, glucose is metabolized via glycolysis, the tricarboxylic acid (TCA) cycle, and oxidative phosphorylation. However, metabolic pathways are altered by cellular and environmental conditions. For example, many types of cancer cells are dependent on aerobic glycolysis in that glucose is metabolized via glycolysis and lactic acid fermentation; this phenomenon is known as the Warburg effect1,2. Remodeling of the metabolic pathway is required for cancer cells to maintain their rapid proliferation and viability3. Therefore, in cancer cells, rate-limiting enzymes for glycolysis, including hexokinase (HK), phosphofructokinase, and pyruvate kinase, are often upregulated4. Targeting the remodeling could be an attractive therapeutic strategy for cancer treatment.
Some sugars are known to modulate glycolytic processes and influence the proliferation of cancer cells. For example, mannose is known to inhibit the growth of cancer cells5. Mannose exhibits antiproliferative activity because mannose-6-phosphate, which is generated by HK, inhibits glycolytic enzymes including HK itself. Although mannose does not induce cancer cell death, it can enhance the effect of tumor chemotherapy5. In contrast to mannose, fructose is reported to activate the proliferation of cancer cells6–8. Fructose is a monosaccharide widely used as a sweetener. After internalization in cells via the glucose/fructose transporter GLUT2 or the fructose-specific transporter GLUT5, the monosaccharide is phosphorylated to produce fructose-1-phosphate (F-1-P)9. Accumulation of F-1-P leads to inactivation of pyruvate kinase M2 (PKM2). The inhibitory effect of F-1-P is most likely mediated by its direct binding to PKM2. In cells under hypoxic conditions, such as small intestinal epithelial cells and tumors, inactivation of PKM2 is beneficial for their proliferation. Indeed, a previous study showed that feeding a high-fructose diet in mice caused intestinal tumor progression6.
Ketohexokinase (KHK) is the enzyme that produces F-1-P from fructose. Intriguingly, alternative splicing of the KHK gene can generate two distinctive functional isoforms termed KHK-A and KHK-C10. KHK-C has a much greater affinity for fructose than KHK-A; phosphorylation of fructose is primarily mediated by KHK-C11. KHK-A can mediate the phosphorylation of proteins besides sugars12,13. The selective autophagy receptor p62 is one of the substrates of the kinase. Phosphorylation of p62 at Ser28 results in the activation of nuclear factor erythroid 2–related factor 2 (Nrf2) via degradation of its inhibitor Kelch-like ECH-associated protein 1 (Keap1)14. Nrf2 is a transcription factor that induces antioxidant genes to counteract ROS. The expression of KHK-A is upregulated in various cancer cells10,14,15. Thus, KHK-A is involved in the proliferation of cancer cells irrespective of the activity to produce F-1-P. The function of KHK-A in normal tissues remains elusive.
Rare sugars are defined as monosaccharides and their derivatives are rarely found in nature. Recent progress in the large-scale production of rare sugars enables us to analyze their biological activities16. Previous reports have shown that rare sugars exhibit beneficial activities including anti-obesity, anti-diabetic, anti-pathogenic microorganism, and antitumor effects17–20. For example, d-allose exhibits antitumor activity, although its mechanism remains elusive21–24. Here, we report that another rare sugar, l-sorbose, uniquely induces apoptotic death in cancer cells. It enters cells primarily through GLUT5 and is then converted to S-1-P by KHK. S-1-P inhibits the activity of HK, which induces mitochondrial ROS production and apoptotic cell death. In addition, S-1-P downregulates the expression of KHK-A by modulating the splicing mechanism, which results in attenuation of the Nrf2 antioxidation pathway. This founding reveals another antitumor effect of l-sorbose. Furthermore, l-sorbose enhances the effect of tumor chemotherapy in combination with sorafenib in mouse xenograft models, which can largely decrease the dosage of sorafenib during the treatment.
## l-Sorbose triggers mitochondrial apoptosis
The rare sugar d-allose was previously reported to target several cancer cells. To determine whether other rare sugars have potential in cancer therapy, a cell viability screening employing six rare sugars was performed on six cancer cell lines (Fig. 1a). From this screening, we found that l-sorbose could kill various cancer cell lines. In particular, the growth of the liver cancer cell lines Huh7 and HepG2 was significantly inhibited by treatment with 25 mM l-sorbose; however, their viabilities were not influenced by treatment with the same concentration of d-allose (Fig. 1a). l-Sorbose exhibited half-maximum inhibitory concentration (IC50) values of 33.82 mM (24 h), 27.32 mM (48 h), and 30.88 mM (72 h) in Huh7 cells and 27.68 mM (24 h), 34.89 mM (48 h) and 22.60 mM (72 h) in HepG2 cells (Fig. 1b). Furthermore, long-term colony formation assay showed that l-sorbose treatment impaired the proliferation of these two liver cancer cell lines (Fig. 1c).Fig. 1l-Sorbose induces cell apoptosis.a, Cells were treated with or without (ctrl) 25 mM rare sugars for 48 h, and the viabilities were detected by CCK-8 assay. $$n = 5$.$ b, Huh7 or HepG2 cells were incubated with various concentrations of l-sorbose for 24 h, 48 h and 72 h, and their viability rates were measured. $$n = 5$.$ c, Cells were incubated in 0, 12.5, 25 or 50 mM l-sorbose. Long-term survival (14 days) was assayed by staining with crystal violet and the number of colonies was measured by ImageJ. $$n = 3$.$ d, Cells treated with 0, 12.5, 25, or 50 mM l-sorbose for 24 h were stained with Annexin V-FITC and PI, and the ratios of apoptosis were measured by flow cytometry. Left panels: Representative dot plots. Right panels: Quantification of the ratio of early (annexin V-positive, PI-negative) and total (both annexin V- and PI-positive cells) apoptotic cells. $$n = 3$.$ e, Left panels: Western blot showing the expression levels of BAX, Bcl2 and cleaved caspase 3 in Huh7 cells after 24 h with 0, 12.5, 25 or 50 mM l-sorbose treatment. Right panels: Quantification of the BAX/Bcl2 ratio and relative intensities of cleaved caspase 3. The BAX/Bcl2 ratio detected in Huh7 cells without l-sorbose treatment was defined as 1, and the relative ratios are shown. The intensity of cleaved caspase 3 normalized to β-actin detected in cells without l-sorbose treatment was defined as 1. $$n = 3$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test or one-way ANOVA with Dunnett’s test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$
To examine whether the cell death induced by l-sorbose is apoptosis, cells treated with l-sorbose were stained with annexin V and propidium iodide (PI), and the ratio of apoptotic cells was measured. The ratio of early stage apoptosis (annexin V-positive, PI-negative cells) and total apoptosis (both annexin V- and PI-positive cells) were then revealed to be increased in the cells treated with 25 mM and 50 mM l-sorbose (Fig. 1d). In addition, the levels of cleaved caspase 3 and the ratio of BAX/Bcl2, which are the indicators for the mitochondrial apoptosis, were found to be increased in Huh7 cells following the l-sorbose concentration depend way (Fig. 1e).
Mitochondrial apoptosis can be induced by mitochondrial dysfunction and reactive oxygen species (ROS) production25,26. Indeed, the mitochondrial membrane potential (MMP) decreased significantly when Huh7 and HepG2 cells were treated with 25 mM l-sorbose for 3 h (Fig. 2a, b). ROS levels were significantly elevated after the treatment with 25 mM l-sorbose for 6 h followed by a significantly decreased cell viability (Fig. 2c–e). To determine whether l-sorbose-induced apoptosis is attributable to ROS production, Huh7, and HepG2 cells were treated with the antioxidant N-acetyl-L-cysteine (NAC). As shown in Fig. 2f, l-sorbose-induced cell death was abrogated by NAC treatment. To further verify that l-sorbose treatment causes an increased ROS production in mitochondria, intracellular ROS were stained with dichlorodihydrofluorescein diacetate (DCFH-DA) in Huh7 cells incubated with l-sorbose for 6 h (Fig. 2g). Fluorescence-activated cell sorting (FACS) analysis showed that the levels of DCFH-DA staining were higher in l-sorbose-treated cells (Fig. 2h). These higher DCFH-DA staining were decreased in the presence of mitoquinone mesylate (mitoQ), which is a reducer for mitochondrial produced ROS27 (Fig. 2h). Similar results can be also confirmed when the dihydrorhodamine 123 was directly used to monitor mitochondrial ROS (Supplementary Fig. 1). On the other hand, the biomass of mitochondria remained no changes after the treatment of l-sorbose (Fig. 2i). In sum, our results suggest that l-sorbose can promote mitochondrial ROS production in cancer cells, which induces apoptotic cell death. Fig. 2l-Sorbose triggers mitochondrial dysfunction and enhances ROS accumulation in cancer cells.a, b, JC-1 dye was used to detect the mitochondrial membrane potential by flow cytometry. Left panels: Representative histograms. Right panels: Quantification of the FL2/FL1 MFI values. The data were normalized to the cells without l-sorbose treatment group ($100\%$). Cells treated with 0, 12.5, 25, and 50 mM l-sorbose for 24 h (a). Cells treated with 25 mM l-sorbose at different time points (b). $$n = 3$.$ c, d, The ROS level was detected by flow cytometry using DCFH-DA staining. Left panels: Representative histograms. Right panels: Quantification of the MFI values. The data were normalized to the cells without l-sorbose treatment group ($100\%$). Cells treated with 0, 12.5, 25 or 50 mM l-sorbose treatment for 24 h (c). Cells treated with 25 mM l-sorbose at different time points (d). $$n = 3$.$ e, Cell viabilities were analyzed after 25 mM l-sorbose treatment at different time points. $$n = 3$.$ f, Cells pretreated with or without NAC (10 mM) for 30 min were incubated with vehicle or 25 mM l-sorbose for 24 h, and their viabilities were measured. $$n = 3$.$ g, Representative images of Huh7 cells stained with DCFH-DA (green) and the mitochondrial marker MitoBright (red). Nuclei were stained with hoechst33342 (blue). Scale bar, 10 μm. h, Huh7 cells incubated with or without 500 nM mitoQ and 25 mM l-sorbose for 24 h were stained with DCFH-DA and subjected to FACS analysis. Left panels: Representative histograms. Right panels: Quantification of the MFI values. The data were normalized to the cells without l-sorbose and mitoQ treatment group ($100\%$). $$n = 3$.$ i, Cells were strained with a mitochondria marker, MitoBright, and analyzed by FACS. Left panels: Representative histograms. Right panels: Quantification of the MFI values. The MFI value detected in cells without l-sorbose treatment was defined as 1. $$n = 3$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ NS, not significant.
## l-Sorbose enhances the anticancer effect of sorafenib
Sorafenib is a multi-kinase inhibitor used to treat liver, kidney, and thyroid cancers28,29. The IC50 values of sorafenib in Huh7 and HepG2 cells were 8.275 μM and 6.731 μM, respectively (Fig. 3a). To examine whether l-sorbose could enhance the anticancer effect of sorafenib, we then treated these cells with 2 or 4 μM sorafenib in combination with the l-sorbose (12.5 or 25 mM). As shown in Fig. 3b, the combination of l-sorbose and low-dose sorafenib (2 μM) markedly decreased the cell viability compared with either alone. l-Sorbose- and sorafenib-treated cells died due to the induction of apoptosis (Fig. 3c). In line with this notion, this cell death was alleviated by incubation with the pancaspase inhibitor z-VAD-FMK (Fig. 3c, d), suggesting a synergistic anticancer effect between sorafenib and l-sorbose. This synergistic anticancer effect is l-sorbose specific because cell death was not observed when the cancer cells were incubated even with 25 mM of any other rare sugars and 2 µM sorafenib (Fig. 3e). To test whether l-sorbose share this synergistic effect with other chemotherapeutic drugs, we treated Huh7 and HepG2 cells with cisplatin (50 μM), doxorubicin (100 nM), lenvatinib (2 μM) and paclitaxel (1 μM) in combination with 25 μM of l-Sorbose. As shown in Fig. 3f, l-sorbose also exhibited a synergistic effect to kill cancer cells with these chemotherapeutic drugs. Fig. 3l-Sorbose enhances antitumor activity of sorafenib in vitro.a, Huh7 or HepG2 cells were incubated with various concentrations of sorafenib for 24 h, and their viability rates were measured and IC50s were calculated. $$n = 3$.$ b, Cells treated with l-sorbose (0, 12.5 or 25 mM) were incubated with sorafenib (0, 2 or 4 μM) for 24 h, and cell viability rates were measured. $$n = 3$.$ c, Huh7 cells were treated with or without 12.5 mM l-sorbose, 2 μM sorafenib, and 50 μM z-VAD-FMK for 24 h. The levels of apoptosis were measured. Left panels: Representative dot plots. Right panels: Quantification of the ratio of apoptotic cells. $$n = 6$.$ d, Huh7 cells were treated with or without 12.5 mM l-sorbose, 2 μM sorafenib, and 50 μM z-VAD-FMK for 24 h, and cell viability was measured. $$n = 5$.$ e, Huh7 and HepG2 cells were treated by 2 μM sorafenib in combination with different rare sugars (25 mM) for 24 h, and their viabilities were measured. $$n = 3$.$ f, Viabilities of cells treated with l-sorbose and other drugs. The concentrations of drugs used in this assay were as follows: cisplatin, 50 μM; doxorubicin, 100 nM; lenvatinib, 2 μM; paclitaxel, 1 μM. $$n = 5$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test or two-way ANOVA with Dunnett’s test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ NS, not significant.
To examine whether l-sorbose can enhance the therapeutic effect of sorafenib in vivo, nude mice subcutaneously injected with Huh7 cells were treated with l-sorbose and sorafenib either alone or in combination. The mice were given either normal drinking water or $20\%$ l-sorbose by gavage for 4 weeks. Sorafenib was intragastrically administered to mice at a dose of 50 mg/kg every day from the eleventh day. We found that l-sorbose treatment resulted in growth inhibition of xenografts, as well as reductions in tumor weight. Noteworthy, a greater inhibitory effect on tumor growth was observed when sorafenib was administered in combination with l-sorbose in mouse xenograft models. After the trial, the tumor volume became less than half by the combined use of l-sorbose and sorafenib compared to l-sorbose or sorafenib treatment alone (Fig. 4a–c, Supplementary Fig. 2). In addition, the ratio of BAX/Bcl2 increased (Fig. 4d) and the ROS level was evaluated in the sorafenib combined with l-sorbose groups (Fig. 4e), while no notable body weight loss was observed in mice treated with l-sorbose and/or sorafenib (Fig. 4f). Furthermore, the glucose and insulin levels in plasma were in same levels, showing that l-sorbose does not impact mice glycemia levels (Fig. 4g, h).Fig. 4l-Sorbose enhances antitumor activity of sorafenib in vivo.a–c BALB/c nude mice were injected subcutaneously with Huh7 cells and received either normal drinking water or $20\%$ l-sorbose by oral gavage every day from the fifth day after tumor transplantation. Sorafenib was intragastrically administered to mice at a dose of 50 mg/kg every day from the eleventh day. The number of mice was $$n = 8$$ per group. Images show tumors of all mice (a). Tumor volume (b) and weight (c) were measured. d, Left panels: Western blot showing the expression levels of BAX and Bcl2 mouse tumor tissues. Right panels: Quantification of the Bax/Bcl2 ratio. The Bax/Bcl2 ratio detected in the group without l-sorbose and sorafenib administration was defined as 1. $$n = 3$.$ e, The mitochondrial ROS level was detected by Dihydrorhodamine 123 in mouse tumors. Left panels: Representative histograms. Right panels: Quantification of the MFI values. The data were normalized to the group without l-sorbose and sorafenib administration ($100\%$). $$n = 4$.$ f, Mouse weight was measured every day from the fifth day after tumor transplantation. The number of mice was $$n = 8$$ per group. g, h, *Fasting plasma* glucose (g) and fasting plasma insulin (h) of tumor-bearing mice. $$n = 8$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test or two-way ANOVA with Dunnett’s test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ NS not significant.
## l-Sorbose internalizes cell through GLUT5, and accumulates as l-sorbose-1-phosphate
Structurally, l-sorbose is the C-3 epimer of d-fructose30 (Fig. 5a). Fructose is transported into cells mainly via GLUT5, the specific transporter for fructose. Upon entering the cells, fructose is phosphorylated by KHK to d-fructose-1-phosphate (F-1-P)31 (Fig. 5a). We found that the addition of d-fructose alleviated the cytotoxicity of l-sorbose (Fig. 5b), indicating the possibility that l-sorbose and fructose may compete the same transporter for internalizing cell or the KHK for phosphorylation (Fig. 5a).Fig. 5l-Sorbose internalizes cells through GLUT5 and accumulates as l-sorbose-1-phosphate.a, Schematic diagram of first two steps of fructose metabolism and the hypothesis for l-sorbose. b, Viabilities of Huh7 and HepG2 cells treated with or without l-sorbose (25 mM) and the indicated concentrations of d-fructose for 24 h. $$n = 3$.$ c, Western blot to detect GLUT5 in wild-type and SLC2A5-KO cells. d, Cell viabilities of wild-type or SLC2A5-KO cells treated with the indicated concentrations of l-sorbose for 24 h. $$n = 5$.$ e, Huh7 cells with or without overexpression of KHK-C were cultured in the presence or absence (ctrl) of l-sorbose for 24 h. The intracellular amount of l-sorbose-1-phosphate (l-S-1-P) was detected by LC-MS. $$n = 3$.$ f, Western blot verified the overexpression of KHK-C in Huh7 cells. g, Recombinant KHK-C was incubated with 200 μL 50 mM Tris–HCl buffer (pH 7.5) containing 5 g/L l-sorbose, 3 mM MnCl2, 3 mM MgCl2 and 25 mM ATP. Top panels: The KHK-C reaction equation. Bottom panels: TLC analysis was performed to measure the production of l-S-1-P. Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$
To test our hypothesis, we examined whether GLUT5 is required for the uptake of l-sorbose. SLC2A5 (the gene name of GLUT5) was knocked out in Huh7 and HepG2 cells; loss of GLUT5 protein in SLC2A5 KO cells was verified by western blotting (Fig. 5c). Our results confirmed that SLC2A5 KO cells showed greater tolerance to l-sorbose even under 100 mM l-sorbose (Fig. 5d), suggesting that, similar with the d-fructose, the cellular uptake of l-sorbose is mediated by GLUT5.
After internalizing cells, monosaccharides are often phosphorylated in the first step of their metabolism. Accordingly, accumulation of l-sorbose-1-phosphate (S-1-P) was observed in cells incubated with l-sorbose (Fig. 5e). Since ketohexokinase (KHK) can phosphorylate furanoses including fructose, we examined whether phosphorylation of l-sorbose was mediated by this kinase. KHK has two isoforms KHK-A and KHK-C; KHK-C exhibits greater activity to phosphorylate fructose11. Thus, a plasmid containing human KHK-C gene was transformed into Huh7 and HepG2 cells (Fig. 5f). It was observed that S-1-P level was increased in cells with overexpression of KHK-C (Fig. 5e). Furthermore, we performed an in vitro phosphorylation assay using a recombinant human KHK-C prepared from E. coli cells. This experiment provided direct evidence that KHK-C can catalyze the conversion of l-sorbose into S-1-P (Fig. 5g).
## The expression level of KHK dictates l-sorbose sensitivity
We confirmed that, by overexpression of KHK-C, Huh7 and HepG2 cells became more sensitive to l-sorbose treatment and the levels of apoptotic cell death were elevated (Fig. 6a, b). In contrast, the knockout of KHK in these cells prevented the cell lines from being affected by l-sorbose cytotoxicity (Fig. 6c, d). These results revealed KHK as a critical enzyme to dictate sensitivity to l-sorbose. Fig. 6The expression level of KHK dictates l-sorbose sensitivity.a, Cells with or without overexpression of KHK-C were treated with the indicated concentration of l-sorbose for 24 h and their viabilities were assayed. $$n = 5$.$ b, Cells with or without overexpression of KHK-C were treated with 25 mM l-sorbose for 24 h and their apoptotic levels were measured. Left panels: Representative dot plots. Right panels: Quantification of the ratio of apoptotic cells. $$n = 3$.$ c, Viabilities of wild-type and KHK-KO cells treated with the indicated concentrations of l-sorbose for 24 h. $$n = 5$.$ d, Western blot to detect KHK in wild-type and KHK-KO cells. e, Left panels: Western blot of the endogenous KHK-C in different cancer cells. Right panels: Relative intensities of KHK-C. The intensity of KHK-C normalized to β-tubulin detected in A549 cells was defined as 1. $$n = 3$.$ f, Cell viabilities on different cancer cells after 25 mM l-sorbose treatment or not for 12 h. $$n = 3$.$ g, MCF7 cells with or without overexpression of KHK-C were incubated with the indicated concentration of l-sorbose for 24 h, and their viabilities were measured. $$n = 5$.$ h, MCF7 cells with or without overexpression of KHK-C were incubated with the indicated concentration of l-sorbose for 24 h, and their apoptotic levels were measured. Left panels: Representative dot plots. Right panels: Quantification of the ratio of apoptotic cells. $$n = 3$.$ i, T24 cells with or without overexpression of KHK-C were incubated with the indicated concentration of l-sorbose for 24 h, and their viability was measured. $$n = 5$.$ j, Left panels: Western blot showing the expression levels of KHK in mouse tumor tissues. Right panels: Quantification of relative intensities of KHK. The intensity of KHK normalized to β-tubulin detected in the group without l-sorbose and sorafenib administration was defined as 1. $$n = 3$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ NS, not significant.
To further support the above conclusion, we analyzed the expression level of endogenous KHK-C in various l-sorbose treated cancer cell lines and compared their cell viabilities (Fig. 6e, f). The results showed that the l-sorbose sensitivity of these cells depends on their KHK-C expression levels. For instance, under the treatment with 25 mM l-sorbose, A549 cells with the highest expression of KHK-C revealed <$50\%$ viability, while MCF7 and T24 cells were almost resistant to the l-sorbose treatment (Fig. 6e, f). We speculated that the resistance of these cells to is attributable to lower expression levels of KHK-C. In line with this hypothesis, we proved that overexpression of KHK-C in MCF7 and T24 cells is able to recover the sensitivity to l-sorbose, resulting in a decrease in their cell viability (Fig. 6g, i) and an increase in apoptosis levels as well (Fig. 6h). As another line of evidence, we found that expression level in mice tumor was increased in the l-sorbose treated group and the combination group, indicating the importance of KHK-C in l-sorbose treatment (Fig. 6j).
## l-Sorbose-1-phosphate interferes with glucose metabolism by targeting the hexokinase activity
To gain insight into the molecular basis for the anticancer activity of l-sorbose, we performed central carbon metabolomic analyses. The results showed that treatment with l-sorbose in Huh7 cells led to marked decreases in several intermediate metabolites in glycolysis and the tricarboxylic acid (TCA) cycle, including glucose-6-phosphate (G-6-P), 3-phosphoglyceric acid, l-lactic acid, and α-ketoglutarate (α-KG) (Fig. 7a). The decrease in G-6-P levels was of particular interest because hexokinase (HK), the enzyme required for converting glucose to G-6-P, is a viable target for cancer therapy. Then, we measured the metabolic flux of several intermediate metabolites in glycolysis. It was confirmed that the flux of the first metabolite of glycolysis G-6-P and the final metabolite of glycolysis lactic acid were decreased, indicating that l-sorbose treatment interfered with the glycolysis in l-sorbose-treated Huh7 cells (Supplementary Fig. 3). Indeed, the kinase activity in lysates of Huh7 and HepG2 cells was decreased by l-sorbose treatment (Fig. 7b).Fig. 7S-1-P interferes with glucose metabolism by targeting the activity of hexokinase.a, Differential metabolite analysis using central carbon metabolomics on Huh7 cells treated with or without (ctrl) 25 mM l-sorbose for 12 h. $$n = 4$.$ b, Total activity of HK in cells treated with or without (ctrl) 25 mM l-sorbose for 12 h. $$n = 3$.$ c, Cell viabilities on Huh7 cells cultured in glucose-free DMEM supplemented with the indicated sugars after l-sorbose treatment for 24 h. $$n = 5$.$ d, The ROS level was detected by DCFH-DA in cells cultured in glucose-free DMEM supplemented with d-galactose after 25 mM l-sorbose treatment at different time points. Left panels: Representative histograms. Right panels: Quantification of the MFI values. The data were normalized to the cells without l-sorbose treatment group ($100\%$). $$n = 3$.$ e, Top panels: Western blot to analyze HK2 protein expression levels in cells. Bottom panels: Relative intensities of HK2. The intensity of HK2 normalized to β-actin. $$n = 3$.$ f, Left panels: Western blot to analyze HK2 protein expression levels in mice tumor tissues. Right panels: Relative intensities of HK2. The intensity of HK2 normalized to β-actin. $$n = 4$.$ g, Dose response inhibition of HK2 activity by S-1-P. $$n = 3$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ NS, not significant.
To further verify that the cytotoxic effect of l-sorbose is attributable to a decrease in HK activity, we assessed whether the effect of l-sorbose was abolished by galactose because, unlike glucose and mannose, galactose enters glycolysis without using HK32,33. As shown in Fig. 7c, l-sorbose treatment resulted in substantial decreases in cell viability of Huh7 and HepG2 cells cultured with glucose or mannose; however, the cytotoxicity was significantly alleviated by culturing cells in glucose-free DMEM supplemented with galactose, and so does the ROS level (Fig. 7d). Meanwhile, the galactose itself does not influence the cell viability (Supplementary Fig. 4). Our results suggest that the apoptotic cell death induced by l-sorbose treatment is attributable to the glycolytic HK activity.
In many cancer cells, the Type II isoform of hexokinase (HK2) is overexpressed. We then characterized the HK2 protein in Huh7 and HepG2 cells. It was found that the expression, localization and phosphorylation of HK2 were not altered by l-sorbose treatment (Fig. 7e, f, Supplementary Fig. 5), excluding their involvement in reducing HK2 activity. We then incubated the recombinant human HK2 enzyme with different concentrations of S-1-P for confirming its inhibitory effect on HK activity. As shown in Fig. 7g, S-1-P inhibited HK2 activity with 4.35 mg/mL of IC50. These results demonstrated that the cytosolic S-1-P in cancer cells produced by l-sorbose treatment directly inhibits the HK2 activity.
## l-Sorbose treatment inactivates Nrf2-regulated antioxidant defense
In cancer cells, KHK-A isoform is generally upregulated to support their survival by counteracting ROS production14. Intriguingly, we found that KHK-A levels were decreased in cancer cell lines Huh7 and HepG2 treated with l-sorbose, while KHK-C levels were elevated (Fig. 8a, b). In contrast, these effects were not observed in T24 cells, which is insensitive to l-sorbose (Fig. 8b), as well as in the Huh7 cells cultured with galactose supplemented DMEM (Supplementary Fig. 6). These observations suggested KHK-A as an alternative target of l-sorbose treatment. Fig. 8l-Sorbose treatment downregulates the expression of KHK-A and inactivates Nrf2-regulated antioxidant defense.a The mRNA levels of KHK-A and KHK-C in cells with or without (ctrl) 25 mM l-sorbose treatment for 24 h. $$n = 3$.$ b Left panels: Western blotting was performed to analyze KHK-A and KHK-C expression in cells after l-sorbose treatment for 24 h. Right panels: Relative intensities of KHK-A and KHK-C. The intensity of KHK-A and KHK-C normalized to β-tubulin detected in cells without l-sorbose treatment were defined as 1. $$n = 3$.$ c Left panels: Western blot of Nrf2 in the nuclear fractions of Huh7, HepG2 and WRL68 cells treated with or without (ctrl) 25 mM l-sorbose for 24 h. Right panels: Relative intensities of Nrf2. The intensity of Nrf2 normalized to PCNA detected in cells without l-sorbose treatment was defined as 1. $$n = 3$.$ d Levels of mRNA expression of Nrf2-regulated antioxidative genes in Huh7 and HepG2 cells treated with or without (ctrl) 25 mM l-sorbose for 24 h. The mRNA level detected in cells without l-sorbose treatment was defined as 1, and relative mRNA levels detected in l-sorbose-treated cells are shown. $$n = 3$.$ Data are presented as the mean ± s.d. and were analyzed by unpaired two-tailed Student’s t test. * $P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ NS not significant.
Since KHK-A serves as a positive regulator to induce antioxidative genes via activation of Nrf2, a decrease in KHK-A levels may be another cause of apoptosis induction in l-sorbose-treated cancer cells. Thus, we assessed whether l-sorbose treatment affects the Nrf2 pathway. In cancer cell lines, Nrf2 is predominantly detected in the nuclear fraction. However, when the cells were treated with l-sorbose, the levels of Nrf2 detected in the nuclear fraction were significantly decreased (Fig. 8c). In addition, antioxidative genes regulated by Nrf2, including heme oxygenase-1 (HO-1), glutamate-cysteine ligase catalytic (GCLC), quinone oxidoreductase 1 (NQO1), and phosphogluconate dehydrogenase (PGD), were downregulated in l-sorbose-treated cells (Fig. 8d). Notably, KHK-A levels were not decreased by l-sorbose treatment in the normal hepatocyte cell line WRL68 (Fig. 8b), nor does the cell viability (Supplementary Fig. 7). Nrf2 levels detected in the nuclear fraction were not altered by l-sorbose treatment in WRL68 cells (Fig. 8c). These results suggest that l-sorbose modulates the splicing switch to generate KHK isoforms in cancer cells, which helps to induce apoptosis. A previous study showed that mannose exhibits anticancer activity via HK inactivation5. However, KHK-A levels were not altered by mannose and neither KHK-C (Supplementary Fig. 8). Thus, modulation of KHK splicing would be a unique anticancer activity for l-sorbose.
## Discussion
l-*Sorbose is* the intermediate of a fermentation process for manufacturing vitamin C34. Although it is rare in nature, l-sorbose can be efficiently produced by the bioconversion from d-sorbitol in Gluconobacter or Acetobacter35. Here, we show that the rare sugar l-sorbose induces apoptotic cell death in cancer cells. Our results show that l-sorbose exhibits two anticancer activities: ROS production induced by HK inactivation and attenuation of a cellular antioxidant defense mechanism by KHK-A downregulation. Our results suggest that l-sorbose-mediated apoptotic cell death is induced by the synergy of these effects (Fig. 9).Fig. 9Model of the antitumor mechanism of l-sorbose.l-Sorbose enters cells primarily through GLUT5 and is then converted to l-sorbose-1-phosphate (S-1-P) by KHK. S-1-P inhibits the activity of HK, which induces mitochondrial ROS production and apoptotic cell death. Furthermore, S-1-P downregulates the expression of KHK-A by modulating the splicing mechanism, which results in attenuation of the Nrf2 antioxidation pathway.
In cancer cells, various metabolites, including glucose-6-phosphate (G-6-P), 3-phosphoglyceric acid, l-lactic acid, and α-ketoglutarate (α-KG), are decreased by l-sorbose treatment (Fig. 7a). This result suggests that a number of enzymes required to produce the metabolites are inactivated by l-sorbose treatment. Nevertheless, we conclude that the anticancer activity is primarily attributable to inactivation of HK because cell death induced by l-sorbose treatment was significantly reduced by culturing cells in galactose-containing media (Fig. 7d). Galactose is converted to G-6-P without using HK32. HK is a rate-limiting enzyme in glycolysis, and its activity is generally elevated to satisfy their high metabolic demands in cancer cells36. Depletion of glucose or inactivation of critical glycolytic enzymes in cancer cells is known to induce oxidative stress37. Thus, ROS production in l-sorbose-treated cells is most likely attributable to a decrease in HK activity and a subsequent reduction in glucose metabolism. In l-sorbose treated cells, levels of HK2 were not altered, while total activity of HK2 in the cells was decreased, indicating that enzymatic activity of HK is inhibited by l-sorbose treatment (Fig. 7b, g). KHK is required for l-sorbose to exhibit anticancer activity, suggesting that inhibition of HK is mediated by S-1-P or its derivatives. A previous report showed that S-1-P inhibited HK in dialyzed bovine brain extract38. Although the detailed mechanism for HK inhibition by S-1-P remains unknown, S-1-P could serve as a direct inhibitor of the kinase (Fig. 7g). Given that KHK is a pivotal enzyme for l-sorbose to perform its anticancer activity, the expression levels of this kinase could be a marker to indicate sensitivity to l-sorbose. In support of this hypothesis, l-sorbose exhibits cytotoxicity in a manner dependent on the levels of KHK expression (Fig. 6).
Similar to l-sorbose, mannose suppresses the proliferation of cancer cells because phosphorylated mannose inhibits HK activity5. However, unlike l-sorbose, mannose does not induce apoptotic cell death. l-sorbose exhibits more severe anticancer activity than mannose, presumably because the rare sugar performs an additional function; that is, conversion of KHK isoforms. Incubation of cancer cells with l-sorbose results in elevation of KHK-C levels (Fig. 8a, b). Genes involved in fructose metabolism are known to be induced by the uptake of fructose. Thus, like fructose, l-sorbose may be able to induce fructolytic genes. In contrast to KHK-C, KHK-A is significantly decreased by the l-sorbose treatment (Fig. 8a, b). Downregulation of KHK-A causes attenuation of the Nrf2 antioxidation pathway, which should make cancer cells sensitive to ROS. Given that KHK-C and -A are generated via alternative splicing, these results indicate that the regulation of KHK splicing is modulated by l-sorbose treatment. c-Myc, and heterogeneous nuclear ribonucleoproteins H1 and H2 are known to be involved in the splicing switch from KHK-C to KHK-A; thus, this pathway may be affected in l-sorbose-treated cells10. It is well known that some tumors prefer using other nutrition like glutamine or lactate to support their quick growth39–41. Even in those cancers, the effect of l-sorbose on the antioxidation pathway is still effective, revealing its wild application in cancer therapy.
We demonstrate that l-sorbose alone or in combination with other chemotherapy reagents exhibits anticancer activity in vitro and in vivo (Figs. 3, 4). It is notable that l-sorbose is reported to induce hemolysis in dogs due to inactivation of glycolysis in erythrocytes42–44. However, in erythrocytes derived from other animals, including humans, hemolysis is not induced by inactivation of glycolysis42. Indeed, no obvious health impact was observed in our mouse experiments. The toxicity of l-sorbose for humans has not been reported thus far. Thus, although further examinations are required regarding the safety and therapeutic effects of l-sorbose in vivo, the rare sugar would be an attractive reagent used for anticancer treatment.
## Plasmids
The oligonucleotides and plasmids used in this study are listed in Supplementary Table 1 and Supplementary Table 2. *For* gene overexpression, human KHK-C was cloned into pLVX-puro. *For* gene knockout, guide RNA sequences of KHK or SLC2A5 were cloned into the pLVX-CRISPR-v2-puro vector. Human KHK was cloned into the pET28a vector for recombinant KHK expression in E. coli. All plasmids were sequenced and verified.
## Cell lines and cell culture conditions
The cell line WRL68 was purchased from Mingzhou Biotechnology. Other cell lines were obtained from the Cell Bank of the Type Culture Collection of Chinese Academy of Sciences. Huh7, HepG2, A549, HeLa, K562, MCF7 and HEK293T cells were cultured in DMEM high glucose medium (Biological Industries, 0023119), and T24 and WRL68 cells were cultured in RPMI 1640 medium (Biological Industries, 2035127) with $10\%$ fetal bovine serum (FBS) (Biological Industries, 1841924) in $5\%$ CO2 at 37 °C. All cells were identified without mycoplasma infection.
To overexpress genes in cultured cells, expression plasmids were stably transfected with a retrovirus-mediated transfection method. For this, HEK293T cells were transfected with pLVX-puro (empty plasmid) or pLVX-KHK-C-puro and the two packaging plasmids, psPAX.2 and pMD2.G. Two days later, the virus particles were collected, filtered, and used to infect the target cells. After 48 h of infection, transfected cells were selected with 2 μg/mL puromycin. Cells stably transfected with pLVX-puro were used as a control cell line.
## CRISPR–Cas9-mediated gene knockout
HEK293T cells were transfected with pLVX-CRISPR-v2-puro (empty plasmid), pLVX-CRISPR-v2-SLC2A5-puro or pLVX-CRISPR-v2-KHK-puro and the two packaging plasmids, psPAX.2 and pMD2.G. Two days later, the virus particles were collected, filtered, and used to infect the target cells. After 48 h of infection, 2 μg/mL puromycin (InvivoGen, QLL-41-03) was used to select positive cells. The selected cells were subjected to limiting dilution to obtain knockout cells. Western blotting was used to verify the expression of the target genes. Selected knockout clones were analyzed and verified by DNA sequencing.
## Metabolomic analysis
For central carbon metabolomic analyses, cells were seeded on 10 cm culture plates and cultured to a density sufficient for 70–$80\%$ confluence. After l-sorbose treatment for 12 h, the cells were collected and added to 400 μL water. Cells were vortexed for 1 min after the addition of 500 μL precooled chloroform/methanol ($\frac{1}{3}$, v/v) and then homogenized for 4 min at 35 Hz and sonicated for 10 min. The homogenization and sonication circles were repeated three times, vibrating at 4 °C for 15 min, and incubating at −80 °C for 1 h. The samples were centrifuged at 9000 × g for 10 min. The supernatants were collected and dried under a gentle stream of nitrogen. Then, they were dissolved in 200 μL ultrapure water. Reconstituted samples were vortexed before filtration through a centrifuge tube filter and subjected to HPIC-MS/MS analysis.
The HPIC separation was performed using a Thermo Scientific Dionex ICS-6000 HPIC System (Thermo Scientific) equipped with Dionex IonPac AS11-HC (2 × 250 mm) and AG11-HC (2 mm × 50 mm) columns. Mobile phase A was 100 mM NaOH in water, and mobile phases C and D were methanol and water, respectively. Another pumping system was used to supply the solvent (2 mM acetic acid in methanol), and the solvent was mixed with the effluent before entering electrospray ionization (ESI) (flow rate of 0.15 mL/min). The column temperature was 30 °C. The temperature of the autosampler was 4 °C and the injection volume was 5 μL. An AB SCIEX 6500 QTRAP + triple quadrupole mass spectrometer (AB Sciex) equipped with an ESI interface was used for analysis and development. Typical ion source parameters were as follows: ion spray voltage = −4500 V, curtain gas = 30 psi, ion source gas 1 = 45 psi, ion source gas 2 = 45 psi, and temperature = 450 °C. By injecting a standard solution of a single analyte into the API source of the mass spectrometer, flow injection analysis was used to optimize the MRM parameters for each target analyte. AB SCIEX Analyst Workstation Software (1.6.3 AB SCIEX), MultiQuant 3.0.3. software and Chromeleon 7 were employed for MRM data collection and processing.
## Quantitative analysis of intracellular l-sorbose-1-phosphate
Cells were seeded on 35 mm culture plates and cultured to a density of ~$70\%$. After l-sorbose treatment for 24 h, the medium was aspirated, and 1 mL of $80\%$ methanol:$20\%$ water mixture was added to extract metabolites. The plate was placed at −20 degrees for 20 min. Then, the cell material was scraped into a 1.5 mL test tube prechilled on ice. The cell debris was pelleted by centrifugation at 15,000 × g at 4 °C for 10 min, and the supernatant was transferred to a new tube and stored at −20 °C until analysis.
HPLC separation was carried out using a WATERS ACQUITY UPLC System (WATERS) equipped with HILIC-Z 2.7 μm (2.1 × 100 mm) columns. The mobile phases were acetonitrile and $0.1\%$ formic acid. The column temperature was 45 °C. The injection volume was 5 μL. The mass spectrometric data were collected on a MALDI SYNAPT Q-TOF mass spectrometer (WATERS) connected with an electrospray ionization interface in positive ion mode (ESI+). Typical ion source parameters were as follows: capillary voltage of 3.5 kV, cone voltage of 30 V, source block temperature of 100 °C, desolvation temperature of 400 °C, desolvation gas flow of 700 l/h, cone gas flow of 50 l/h, and collision energy of $\frac{6}{20}$ eV. The mass range of m/z 20–2000 was scanned. MassLynx V4.2 software (WATERS) was employed for data acquisition and processing.
## Metabolic flux analysis using 13C6-glucose
Cells were seeded on 10 cm culture plates. The day after seeding, cells were washed three times with D-PBS before adding glucose-free DMEM (supplemented with $10\%$ FBS, 4 mM glutamine, and 4.5 g/L 13C6-glucose). After l-sorbose treatment for 12 h, the cells were collected and added 500 μL of MeOH/H2O ($\frac{3}{1}$, v/v). Vortexed for 30 s and precooled in dry ice, repeated freeze-thaw three times in liquid nitrogen. The samples were vortexed for 30 s and sonicated for 15 min in the ice-water bath. Followed by incubation at −40 °C for one hour and centrifugation at 13,800 × g and 4 °C for 15 min. A 400 μL aliquot of the clear supernatant was collected and dried by spin. Then the residue was reconstitution with ultrapure water according to the cell count. Reconstituted samples were vortexed before filtration through the centrifuge tube filter, and were subsequently transferred to inserts in injection vials for HPIC-QE-MS analysis after centrifugation at 13,800 × g and 4 °C for 15 min.
The HPIC separation was carried out using a Thermo Scientific Dionex ICS-6000 HPIC System (Thermo Scientific), equipped with Dionex IonPac AS11-HC (2 × 250 mm) and AG11-HC (2 mm × 50 mm) columns. The mobile phase A was 100 mM NaOH, and D was water, respectively. Another pumping system was used to supply the solvent (2 mM acetic acid in methanol) and solvent mixed with effluent before entering the ion source (flow rate of 0.15 mL/min). The column temperature was set at 30 °C. The auto-sampler temperature was set at 4 °C and the injection volume was 5 μL. The QE mass spectrometer was used for its ability to acquire MS spectra in full ms mode in the control of the acquisition software (Xcalibur 4.0.27, Thermo). In this mode, the acquisition software continuously evaluates the full scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 30 Arb, Aux gas flow rate as 10 Arb, capillary temperature 350 °C, full MS resolution as 70,000, spray Voltage as −3.8 kV (negative). The raw data were converted to the mzXML format using ProteoWizard (Massconvert) and processed with an in-house program.
## Xenograft model
All procedures were conducted in compliance with all the relevant ethical regulations and were approved by the Jiangnan University Animal Welfare and Ethical Review Body. Male BALB/c nude mice aged 5 weeks were purchased from Charles River Laboratories and placed in the Animal Experiment Center of Jiangnan University. Mice were placed five per cage with free access to water and food (chow diet). After habituation for a week, mice were inoculated subcutaneously with Huh7 cells (1.0 × 107 cells in 100 μL PBS per mouse). When the tumors reached 50–100 mm3, the mice were randomly assigned to different groups. Sorafenib was dissolved in dimethyl sulfoxide (DMSO) and diluted in $5\%$ sodium carboxymethyl cellulose. Mice received $20\%$ l-sorbose in water according to their body weights (200 μL/20 mg) by oral gavage once every day. Sorafenib was intragastrically administered to mice at a dose of 50 mg/kg every day. Mice were sacrificed when the xenograft tumor size reached 1000 mm3. None of the mice showed severe weight loss or signs of infection or wounds. The tumor volume was measured every other day until the endpoint and calculated according to the equation: Volume = Length × Width2 × $\frac{1}{2.}$
## Measurement of mouse plasma glucose and insulin levels
At the end of the xenograft model experiment, tail blood samples were collected after 10 h of food restriction and used for the measurement of plasma glucose using a glucometer (yuwell) and plasma insulin with a mouse insulin ELISA kit (Sangon Biotech, D721197).
## Immunohistochemistry
Mice tumor tissues were fixed in $4\%$ paraformaldehyde overnight, then dehydrated in ethanol and embedded in paraffin. IHC was performed using the anti-HK2 antibody (Proteintech, 22029-1-AP, 1:200). Images were obtained with a Nikon C2 Eclipse Ti-E microscope equipped with NIS-Element AR software and analyzed by FlowJo software.
## Western blot
Cells were lysed in RIPA buffer (Solarbio, R0020) supplemented with protease inhibitor cocktail (MCE, HY-K0010) on ice for 30 min. After centrifugation at 15,000 × g for 10 min at 4 °C, the protein lysates were collected. Then the protein lysates were separated by SDS–PAGE and transferred onto PVDF membranes. The membranes were blocked with $5\%$ nonfat powdered milk for 1 h and incubated with primary antibodies against β-actin (Proteintech, 23660-1-AP, 1:1000), BAX (CST, 2772 T, 1:1000), Bcl-2 (CST, 3498 T, 1:1000), β-tubulin (Proteintech, 10068-1-AP, 1:1000), PCNA (BBI, D220014, 1:1000), Nrf2 (Abcam, ab137550, 1:1000), HK2 (Proteintech, 66974-1-lg, 1:5000), p-Tyr (CST, 9411, 1:1000), GLUT5 (Santa Cruz, sc-271005, 1:1000), KHK (Santa Cruz, sc-377411, 1:1000), KHK-A (SAB, 21709, 1:500), and KHK-C (SAB, 21708, 1:500) at 4 °C overnight and then probed with the appropriate secondary antibodies for 1 h at room temperature. The bands were visualized with western ELC substrate (BIO-RAD, 1705060), and the images were captured on a Tanon-5200Multi visualization instrument.
## Quantitative real-time PCR
Total RNA was isolated from cells using CellAmpTM Direct RNA Prep Kit for RT–PCR (TaKaRa, 3732), and complementary DNA was synthesized from 5 μg total RNA using PrimeScriptTM RT Master Mix (TaKaRa, RR036A). qPCR was performed using TB Green® Premix Ex TaqTM II (Tli RNaseH Plus) (TaKaRa, RR420A) on a Prism 7000 Sequence Detection System (Applied Biosystems) according to the manufacturer’s instructions. Primer sequences for qPCR are listed in Supplementary Table 3.
## Cell viability assay
Cells were seeded in 96-well culture plates at a density of 5.0 × 103 cells/well and grown overnight. After treatment with l-sorbose or other reagents, the cells were incubated with CCK-8 (Cell Counting Kit-8, DOJINDO Laboratories, CK04) solution and cultured at 37 °C for another 1 h. The absorbance was detected at 450 nm wavelength using a microplate reader (BIO-RAD).
## Colony formation assay
Cells were seeded in six-well plates at a density of 1000 cells/well. Cells were incubated for 24 h to allow attachment to the plates, after which l-sorbose was added to the cells and incubated for 24 h. The cells were cultured for 14 days in the absence of l-sorbose and fixed with a $4\%$ paraformaldehyde fix solution (Beyotime Biotechnology, P0099) for 20 min. Then, the cells were stained with crystal violet (Beyotime Biotechnology, C0121) solution diluted in water. After 10 min, the plate was washed with water left to dry and scanned.
## Cell apoptosis assay
The cellular apoptosis rate was determined using the Annexin V/PI double-staining Kit (Dojindo Laboratories, AD10). Cells were seeded in 6-well culture plates at a density of 2.0 × 105 cells/well. After incubation with different concentrations of l-sorbose for 24 h, the cells were washed twice with annexin V binding buffer and double-stained with annexin V and PI. Cell apoptosis was examined on a FACSCalibur flow cytometer (BD Accuri C6) and analyzed by FlowJo software.
## Measurement of ROS and mitochondrial volume
The Intracellular ROS levels were detected by DCFH-DA (Beyotime Biotechnology, S0033S). The Intracellular mitochondrial ROS levels were detected by Dihydrorhodamine 123 (MCE, HY-101894). Cells were cultured in six-well plates at a density of 2.0 × 105 cells/well. After being treated with l-sorbose, the cells were gently washed with D-PBS (BBI, E607009) followed by incubation with DCFH-DA or Dihydrorhodamine 123 at 37 °C for 30 min.
To obtain microscopy images, cells were seeded on a confocal dish overnight and treated with l-sorbose for 6 h. After being treated with l-sorbose, the cells were gently washed with D-PBS followed by incubation with MitoBright LT (Dojindo Laboratories, MT11) and DCFH-DA (Dojindo Laboratories, CK04) at 37 °C for 30 min, or Dihydrorhodamine 123 alone. Then the cells were washed with D-PBS twice. Images were obtained with a Nikon C2 Eclipse Ti-E inverted confocal microscope equipped with NIS-Element AR software.
To measure the volume of mitochondria, cells were cultured in six-well plates at a density of 2.0 × 105 cells/well. After being treated with l-sorbose, the cells were gently washed with D-PBS followed by incubation with MitoBright at 37 °C for 30 min. Then the cells were washed twice with D-PBS. The fluorescence of the cells was measured immediately on a FACSCalibur flow cytometer and analyzed by FlowJo software.
## Measurement of mitochondrial membrane potential
Cells were cultured in 6-well plates at a density of 2.0 × 105 cells/well. After being treated with l-sorbose, the cells were gently washed with D-PBS. The changes in mitochondrial membrane potential were detected by staining with JC-1 dye (Beyotime Biotechnology, C2006). After incubating with 10 μM JC-1 staining solution in a cell incubator at 37 °C for 15 min, the cells were washed twice with JC-1 staining buffer and then analyzed by flow cytometry.
## Immunofluorescence assay
Cells were fixed in cold $4\%$ PFA for 20 min and $1\%$ Triton X-100 for 15 min. After being washed twice in 1× PBS for 5 min, cells were incubated in $1\%$ BSA for 30 min. Then being washed in 1× PBS and incubated in $1\%$ BSA containing primary antibodies for 30 min at 37 °C. Cells were washed three times in 1× PBS for 5 min and incubated in $1\%$ BSA containing second antibodies for 30 min at 37 °C. Then being washed and incubated in hoechst33342 for 30 min. Images were obtained with a Nikon C2 Eclipse Ti-E inverted confocal microscope equipped with NIS-Element AR software.
## Intracellular HK activity measurement
Cells were seeded into six-well dishes at a density of 2.0 × 105 cells/well. Cells were treated with l-sorbose for 24 h and then collected for the measurement of HK activity using a Hexokinase Activity Detection Kit (Solarbio, BC0740) according to the manufacturer’s protocol. HK activity was calculated and normalized to the cell number.
## Production of recombinant ketohexokinase
The plasmid pET28a-KHK was transformed into *Escherichia coli* BL21 cells. BL21 cells were grown to an absorbance of 0.6-0.8 at 600 nm and then induced with 0.1 mM IPTG for 20 h at 16 °C. Cells were lysed in lysis buffer (50 mM Tris-HCl pH 7.5), and the cell lysate was precipitated by centrifugation. The enzyme was purified by batch binding to Ni-NTA resin (Qiagen, 30721). The resin was then washed with lysis buffer containing 250 mM imidazole, and 2His-tagged KHK was eluted with 500 mM imidazole. The purified enzyme was concentrated and desalted with an Amicon Ultra centrifugal filter (10 kDa) using 50 mM Tris-HCl (pH 7.5). The amounts of purified protein were determined by BCA protein assay kit (Beyotime Biotechnology, P0011).
## Thin-layer chromatography (TLC) analysis
The samples were spotted at the bottom of the plate (~1.5 cm above the edge), and the TLC plates (Merck, 1057350001) were developed in a vertical developing chamber with the developing agent glacial acetic acid:isopropanol:water = 2:2:1. After development, the spots were detected by spraying $5\%$ anisaldehyde solution and heat to color development.
## In vitro enzyme inhibition assay
Hexokinase activity was assayed through a coupled reaction with glucose-6-phosphate dehydrogenase (G6PDH) followed by NADP + detected at 340 nm. Briefly, 10 μL recombinant HK2 (1 μM) and 10 μL S-1-P were incubated together at 37 °C for 10 min. Then 80 μL assay mix containing 100 mM Tris HCl pH 8.0, 200 mM Glucose, 5 mM MgCl2, 0.8 mM ATP, 1 mM NAD+, 0.25 Units of G6P-DH, was added. Different S-1-P concentrations were added to the incubation mixture described above to investigate the inhibitory effects.
## Statistics and reproducibility
GraphPad Prism8 was used for statistical analysis. At least three independent or parallel experiments were performed for statistical analysis. Data were analyzed by the unpaired Student’s t test, one-way ANOVA with Dunnett’s test, or two-way ANOVA with Dunnett’s test. Three levels of significance were determined: *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Peer Review file supplementary Figures Description of Additional Supplementary Files reporting summary The online version contains supplementary material available at 10.1038/s42003-023-04638-z.
## Peer review information
Communications Biology thanks Lake Ee Quek, Pedro Gonzalez-Menendez and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling Editors: Toshiro Moroishi and Joao Valente. Peer reviewer reports are available.
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|
---
title: Transmembrane BAX inhibitor motif containing 6 suppresses presenilin-2 to preserve
mitochondrial integrity after myocardial ischemia-reperfusion injury
authors:
- Li Ma
- Lihan Liao
- Na Zhou
- Huikang Tao
- Hao Zhou
- Ying Tan
- Weidan Chen
- Fan Cao
- Xinxin Chen
journal: International Journal of Biological Sciences
year: 2023
pmcid: PMC10008687
doi: 10.7150/ijbs.81100
license: CC BY 4.0
---
# Transmembrane BAX inhibitor motif containing 6 suppresses presenilin-2 to preserve mitochondrial integrity after myocardial ischemia-reperfusion injury
## Abstract
Myocardial ischemia-reperfusion (I/R) damage is characterized by mitochondrial damage in cardiomyocytes. Transmembrane BAX inhibitor motif containing 6 (TMBIM6) and presenilin-2 (PS2) participate in multiple mitochondrial pathways; thus, we investigated the impact of these proteins on mitochondrial homeostasis during an acute reperfusion injury. Myocardial post-ischemic reperfusion stress impaired myocardial function, induced structural abnormalities and promoted cardiomyocyte death by disrupting the mitochondrial integrity in wild-type mice, but not in TMBIM6 transgenic mice. We found that TMBIM6 bound directly to PS2 and promoted its post-transcriptional degradation. Knocking out PS2 in mice reduced I/R injury-induced cardiac dysfunction, inflammatory responses, myocardial swelling and cardiomyocyte death by improving the mitochondrial integrity. These findings demonstrate that sufficient TMBIM6 expression can prevent PS2 accumulation during cardiac I/R injury, thus suppressing reperfusion-induced mitochondrial damage. Therefore, TMBIM6 and PS2 are promising therapeutic targets for the treatment of cardiac reperfusion damage.
## Introduction
During the pathology underlying myocardial infarction, coronary artery occlusion reduces fresh blood and oxygen to the cardiac myocyte 1. Although reperfusion and revascularization are the standard treatments to reduce myocardial ischemic injury caused by myocardial infarction, reperfusion itself can damage the heart and therefore myocardial ischemia-reperfusion (I/R) damage seems to be a complication induced by revascularization treatments 2. Clinical evidence has revealed that myocardial reperfusion challenge is closely linked to the degree of perioperative complications following myocardial infarction 3. However, there are no effective approaches to alleviate the additional damage induced by myocardial revascularization stress, since the molecular pathways underlying I/R-induced myocardial dysfunction are not fully understood.
Recent observations depicted the importance of mitochondria in the pathogenesis of myocardial revascularization stress 4-6. Although mitochondria are metabolic centers that determine the rate of oxidative phosphorylation in cardiomyocytes, they also manage numerous extracellular and intercellular signals, including those involved in the inflammatory response, calcium homeostasis, metabolic reprogramming, autophagy, oxidative stress, endoplasmic reticulum (ER) function and cell death 7-12. Mild mitochondrial injury promotes oxidative stress and impairs adenosine triphosphate (ATP) metabolism, thus reducing the contraction/relaxation capacities of cardiomyocytes 13-15. Severe mitochondrial dysfunction leads to cardiomyocyte death, followed by pro-inflammatory cell recruitment and abnormal inflammatory response activation 16, 17. Therefore, mitochondria have been regarded as potential drug targets during cardiac I/R injury.
Presenilin-2 (PS2) is a component of the γ-secretase complex, which was originally reported to cleave amyloid precursor protein. Now, ample evidence showed that PS2 is linked to Alzheimer's disease 18, and mutations in PS2 are considered to be reliable genetic markers of Alzheimer's disease 19. Recent studies have also indicated that PS2 disrupts mitochondrial homeostasis by altering mitochondrial calcium input 20, mitochondria-ER coupling 21, mitochondrial phenotypes 22, the mitochondrial oxidative capacity 23, mitochondrial oxygen consumption, the mitochondrial membrane potential 24 and mitochondria-induced cell death 25. More importantly, mutations in PS2 have been linked with the development of dilated cardiomyopathy and heart failure 26. PS2 protein expression was found to increase significantly in low-glucose- or hypoxia-treated cardiomyocytes 27, and knocking out PS2 was reported to increase cardiomyocyte contraction by enhancing the peak amplitudes of calcium transients 28. However, the influence of PS2 on myocardial revascularization stress has not been determined.
Transmembrane BAX inhibitor motif containing 6 (TMBIM6) is a calcium channel-like protein that is primarily localized on the surface of the ER 29. TMBIM6 is also termed as Bax inhibitor-1, since it was originally found to prevent Bax-induced mitochondrial membrane hyper-permeability and apoptosis 30. TMBIM6 has subsequently been reported to influence mitochondrial bioenergetics 31, the mPTP opening rate 32 and mitochondrial morphology 33. Genetic overexpression of TMBIM6 was recently shown to reduce myocardial revascularization stress by preserving the mitochondrial integrity 34, although the downstream effectors of TMBIM6 in cardiomyocytes have not been defined. In this study, we investigated whether PS2 is a downstream signal of TMBIM6 and thus disturbs the mitochondrial integrity in the setting of myocardial revascularization stress.
## Animals and I/R model
TMBIM6 transgenic (TMBIM6Tg) mice and PS2 knockout (PS2KO) mice (Jackson Laboratory) were genotyped via PCR analysis of mouse tail DNA. For the I/R experiments, ischemia was achieved through occlusion of the LAD through a 7.0 silk suture for 45 min. Then, removal of the silk suture and then restore the fresh blood of LAD to induce the reperfusion for 4 hrs, while the re-opened ligature was left in place to facilitate future analysis of the infarcted tissue 35. Mice treated with the same procedures without LAD occlusion and reperfusion were used as the sham group. TTC staining was performed to assess the infarcted area. Specifically, the mouse was anesthetized, the chest was re-opened, the LAD was re-occluded to promote the TTC perfusion into the myocardium 36. During contraction, the dye circulated and was distributed through the heart. Then, we isolated the heart which was following rinsed with cold PBS, and sliced at 1-mm intervals. Afterwards, the sections were incubated in a $1\%$ TTC (Sigma-Aldrich) solution to visualize the infarcts and viable myocardium 37. The nonischemic area and infarcted area, were determined using computerized planimetry. These areas were comprehensively analyzed in serial sections from each mouse using ImageJ software. Mice that died within 24 hours of surgery were treated as technical errors and excluded from subsequent analyses. Mice in which the open ligatures were lost after the end of reperfusion were also excluded to ensure the accuracy of the TTC measurements 38.
## Histology
The hearts of the mice were placed in Hank's balanced solution, and then treated with $4\%$ paraformaldehyde. Subsequently, the samples were dehydrated and treated with paraffin. Finally, an 8-µm sections of heart tissue were generated through a Microtome 6. Subsequently, HE staining was used to stain these heart sections to further observe the myocardial fiber in the presence of reperfusion stress according to a standard protocol. and then were mounted. For each group, a representative image was selected to show the average or median level of the group based on histological features and observed under a Hamamatsu NanoZoomer 2.0-HT Slide Scanner 39.
## Immunofluorescence
Mouse hearts were placed in Hanks' balanced solution and then treated with $4\%$ paraformaldehyde. Subsequently, samples were saturated in 5, 10, 15 and $20\%$ sucrose in PBS. Then, a 6-µm sections of heart tissues were generated through the Leica CM 3050S Cryostat (Leica Microsystems) 40. After blockade with $10\%$ donkey serum, the sections were treated with primary antibodies including Gr1 (1:1000, Abcam, #ab25377), caspase-12 (1:1000, Abcam, #ab235180), caspase-9 (1:1000, Abcam, #ab222231) or PS2 (1:1000, Abcam, #ab51249) overnight at 4 °C. Subsequently, sections were treated with $0.1\%$ PBST three times at room temperature and then reacted with secondary antibodies. To validate antibody specificity, the IgG isoform control from the same species was used instead of the primary antibody, and immunofluorescence was evaluated as described above 39. To distinguish genuine target staining from the background, secondary antibody-only controls were performed without the addition of the respective primary antibodies. Immunofluorescence pictures were observed under a Zeiss LSM 880 Airy Scan Confocal Microscope 41. The representative pictures were captured to show the average or median level of the group based on the fluorescence features.
## Cell culture and transfection
HL-1 cells were purchased from the Chinese Academy of Sciences (Shanghai, China). In vitro, HL-1 cells were subjected to hypoxic conditions for 45 min, followed by normal oxygen conditions for two hours, as previously described 42. Cell survival rate was determined using a CCK-8 assay based on our previous studies. HL-1 cells were transfected with siRNA to knowkout the expression of PS2 in the presence of hypoxia/reoxygenation condition using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA). The TMBIM6 adenovirus was transfected into HL-1 cells based on a previous report 43.
## ELISA
ELISAs from MyBioSource, Inc. were applied to observe the changes of caspase-9 activities (Mouse Caspase 9 ELISA Kit, catalog #MBS451593), caspase-12 activities (Mouse Caspase-12 ELISA Kit, catalog #MBS9425959), ATP concentrations (Mouse Adenosine Triphosphate ELISA Kit, catalog #MBS724442), mitochondrial respiratory complex I activities (Mouse Mitochondrial Respiratory Chain Complex I ELISA Kit, catalog #MBS912812), mitochondrial respiratory complex II activities (Mouse Mitochondrial Respiratory Chain Complex II ELISA Kit, catalog #MBS108909) and mitochondrial respiratory complex III (Mouse Mitochondrial Respiratory Chain Complex III ELISA Kit, catalog #MBS108907). LDH content was also detected using an ELISA (CyQUANTTM LDH Cytotoxicity Assay Kit, catalog #C20300, ThermoFisher) 34.
## Mitochondrial membrane potential and cellular oxidative stress
The relative immunofluorescence of a JC-1 probes (#ab113850, Abcam) was applied to analyze the mitochondrial potential, based on a previous study 40. Intracellular ROS levels were assessed with a Total Reactive Oxygen Species Assay Kit (#88-5930-74, ThermoFisher, Inc.) 41.
## mPTP opening detection and TUNEL staining
The mPTP opening rate was analyzed using an ELISA Kit (#ab239704, Abcam) 35. Cell death rate was detected using a TUNEL assay (#25879, CST) 6.
## Western blots
Proteins were extracted according to the conventional method 44. In brief, after blocked by $5\%$ milk, the membranes were incubated overnight at 4℃ with PS2 antibody (#ab51249, Abcam). The protein signals were visualized using Tanon Image software (version 5100; Tanon, Shanghai, China). The signal intensity of the target protein was normalized against GAPDH, and then the fold change was calculated relative to the control group.
## Immunoprecipitation
For crosslinking immunoprecipitation, cells were co-treated with antibodies and dithiobis (succinimidyl propionate) for 40 min and then treated with $10\%$ neutral buffered formalin (Sigma-Aldrich) 45. Before co-immunoprecipitation, siRNA or Ad-TMBIM6 were transfected into HL1 cells for 48 hours. The cells were then lysed in an immunoprecipitation buffer. The immunoprecipitation was then performed as described previously 45.
## RNA extraction, reverse transcription and qPCR
Total RNA was extracted using TRIzol™ reagent (Invitrogen) and reverse-transcribed using a Transcriptor First Strand cDNA Synthesis Kit (Roche Diagnostics, Risch-Rotkreuz, Switzerland) 46. Then, qPCR was conducted through the Fast Start Universal SYBR® Green Master Mix (Roche Diagnostics). The mRNA levels detected in each sample were normalized to GAPDH levels. The following primers were used: TNFα (Forward, 5'-AGATGGAGCAACCTAAGGTC-3'; Reverse, 5'-GCAGACCTCGCTGTTCTAGC-3'), IL-6 (Forward, 5'-CAGACTCGCGCCTCTAAGGAGT-3'; Reverse, 5'-GATAGCCGATCCGTCGAA-3'), MCP1 (Forward, 5'-GGATGGATTGCACAGCCATT-3'; Reverse, 5'-GCGCCGACTCAGAGGTGT-3').
## Statistical analysis
Data were presented as the mean ± SEM and analyzed using GraphPad Prism 9.0 software. For the analysis of in vivo experiments, data with sample sizes < 6 were subjected to nonparametric tests, as a normal distribution cannot be assessed accurately with a low sample size. The tests applied to assess significance are described in each figure legend, and the precise p-values of significant changes are indicated on the graphs. $P \leq 0.05$ was significant.
## TMBIM6 overexpression reduces cardiomyocyte death upon H/R treatment
To investigate the impact of TMBIM6 on cardiac reperfusion dysfunction, we transfected HL-1 cells with a TMBIM6 adenovirus (Ad-TMBIM6) and then subjected the cells to hypoxia-reoxygenation (H/R) injury. Subsequently, a CCK-8 assay showed that cellular viability was lower in the H/R injury group than in the control group (Figure 1A). Accordingly, a lactate dehydrogenase (LDH) release assay indicated that LDH release from HL-1 cells into the medium was greater in the H/R injury group than in the control group (Figure 1B). TMBIM6 overexpression protected cardiomyocyte viability (Figure 1A) and prevented LDH leakage (Figure 1B) following H/R injury.
At the molecular level, cardiomyocyte death primarily occurs via caspase-9-induced mitochondrial apoptosis or caspase-12-induced endoplasmic reticular apoptosis. To determine whether TMBIM6 overexpression improved cardiomyocyte survival by inhibiting one of these pathways, we used enzyme-linked immunosorbent assays (ELISAs) to evaluate caspase-9 and caspase-12 activity levels in HL-1 cells. The activities of caspase-$\frac{9}{12}$ were both significantly elevated upon H/R injury exposure; however, Ad-TMBIM6 transfection mainly prevented caspase-9 upregulation after H/R, suggesting that TMBIM6 inhibits mitochondrial apoptosis (Figure 1C and D).
To validate these results, we performed immunofluorescence assays. As shown in Figure 1E-G, the immunofluorescence signals of caspase-9 and caspase-12 in HL-1 cells were significantly elevated upon H/R injury. TMBIM6 overexpression reduced caspase-9 levels following H/R injury, but had no influence on caspase-12 levels (Figure 1E-G). These data confirmed that TMBIM6 suppresses mitochondria-induced cardiomyocyte death during cardiac post-ischemic dysfunction.
## TMBIM6 overexpression attenuates myocardial I/R damage
To translate our in vitro findings, we subjected TMBIM6 transgenic (TMBIM6Tg) or control (TMBIM6flox) mice to myocardial I/R damage or a sham operation in vivo. As shown in Figure 2A, I/R injury augmented the infarction area in TMBIM6flox control mice, but not in TMBIM6Tg mice. TUNEL staining showed that reperfusion elevated the ratio of dysfunctional cells in control heart tissues, but not in TMBIM6Tg heart tissues (Figure 2B and C).
Next, we used HE staining to observe changes underlying the myocardium. Compared with the sham-operated group, I/R-treated TMBIM6flox mice exhibited disorganized and swollen myocardial fibers, while this change was not observed in I/R-treated TMBIM6Tg mice (Figure 2D). We then used electron microscopy to detect ultrastructural changes in the myocardium. In TMBIM6flox control mice, I/R injury induced mitochondrial swelling and rupture (Figure 2E), followed by cytoplasmic vacuolization; however, these structural alterations were not noted in I/R-treated TMBIM6Tg mice.
Excessive cardiomyocyte death and myocardial fiber swelling can induce a cardiac inflammatory response; thus, we used immunofluorescence to evaluate inflammatory cell accumulation in the myocardium. I/R promoted Gr1-positive cells recruitment within the myocardium in TMBIM6flox mice, but not in TMBIM6Tg mice (Figure 2F and G). These results indicated that TMBIM6 overexpression reduced cardiomyocyte death, improved the myocardial structure and repressed the cardiac inflammatory response during I/R injury.
## TMBIM6 overexpression maintains heart function
Myocardial infarction often reduces cardiac contraction/relaxation; thus, we used echocardiography to assess cardiac function in hearts. In control TMBIM6flox mice, reperfusion disrupted the cardiac systolic capacity, as evidenced by a lower ejection fraction (EF), impaired fractional shortening (FS) and augmented left ventricular systolic dimension (LVSd) (Figure 3A-C). I/R injury also impaired the myocardial diastolic capacity in these mice, as evidenced by an increased ratio of early to late transmitral flow velocities (E/A), elevated ratio of mitral peak velocity of early filling to early diastolic mitral annular velocity (E/e') and amplified left ventricular diastolic dimension (LVDd) (Figure 3D-F). However, TMBIM6 overexpression normalized the cardiac systolic and diastolic capacities following reperfusion damage (Figure 3A-F).
Besides, we conducted ex vivo analyses of cardiomyocytes isolated from the mice after reperfusion model. I/R injury or TMBIM6 overexpression negatively affected on the resting lengths of cardiomyocytes (Figure 3G). In cardiomyocytes from TMBIM6flox control mice, I/R injury reduced the peak shortening (PS), impaired the maximal velocity of shortening (+dL/dt), repressed the time to peak shortening (TPS), blunted the maximal velocity of relengthening (-dL/dt) and elevated the time to $90\%$ relengthening (TR90) (Figure 3H-L). However, these functional abnormalities were alleviated in cardiomyocytes isolated from I/R-treated TMBIM6Tg mice (Figure 3H-L).
## TMBIM6 overexpression sustains the mitochondrial integrity of cardiomyocytes
Mitochondrial dysfunction is an important contributor to cardiac I/R injury 47-50; thus, we investigated whether TMBIM6 overexpression could normalize mitochondrial function in HL-1 cells during H/R injury. We first measured ATP production, which is primarily carried out by the mitochondrial respiratory complexes. H/R treatment reduced the ATP content in HL-1 cardiomyocytes (Figure 4A). Moreover, ELISAs indicated that H/R injury significantly repressed mitochondrial respiretory complex activity in these cells (Figure 4B-D). However, TMBIM6 overexpression maintained mitochondrial respiratory complex activity (Figure 4B-D) and therefore enhanced ATP production (Figure 4A) in HL-1 cells following H/R injury.
We then performed immunofluorescence assays, which revealed that reoxygenation stress dissipated the mitochondrial potential in HL-1 cells, whereas TMBIM6 overexpression reversed this effect (Figure 4E and F). TMBIM6 overexpression also neutralized H/R-induced cellular oxidative stress (Figure 4G and H). Moreover, H/R injury augmented the mPTP opening rate in HL-1 cells, while TMBIM6 overexpression repressed it (Figure 4I). These results demonstrated that TMBIM6 overexpression normalized mitochondrial homeostasis in the setting of cardiac revascularization stress.
## TMBIM6 binds directly to PS2 to promote its degradation
To determine the molecular mechanism whereby TMBIM6 preserved cardiomyocytes and their mitochondria function against reoxygenation stress, we focused on protein-protein interactions. First, we used the inBio Discover platform (https://inbio-discover.com) to analyze the potential protein network underlying TMBIM6 (Figure 5A). PS2, one of the potential interactive proteins, has been regarded as a regulator of mitochondrial homeostasis. Therefore, we assessed whether TMBIM6 could bind directly to PS2 to preserve mitochondrial function and integrity. A molecular docking analysis revealed several possible binding sites between TMBIM6 and PS2 (Figure 5B-D). The crosslinks between TMBIM6 and PS2 were validated through co-immunoprecipitation analyses (Figure 5E).
Next, we evaluated the effects of TMBIM6 overexpression on PS2 in HL-1 cells. Quantitative real-time PCR (qPCR) analyses demonstrated that neither reoxygenation stress nor TMBIM6 overexpression altered PS2 transcription (Figure 5F). However, Western blotting indicated that reoxygenation stress elevated PS2 protein content, whereas TMBIM6 overexpression inhibited this effect (Figure 5G). Immunofluorescence analyses confirmed that reoxygenation stress rapidly upregulated PS2 content in HL-1 cells, whereas TMBIM6 overexpression suppressed this increase (Figure 5H and I). These results illustrated that TMBIM6 binds directly to PS2 and promotes its degradation at the protein level.
## PS2 deficiency sustains heart function during reperfusion dysfunction
To figure out the action of PS2 on cardiac reperfusion dysfunction, we used echocardiography to compare the myocardial function of PS2 knockout (PS2KO) and wild-type (WT) mice. As shown in Figure 6A-C, I/R injury reduced the EF, suppressed the FS and augmented the LVSd in WT mice; however, these phenotypic alterations were lessened in PS2KO mice. Similarly, I/R injury impaired the diastolic capacity of the heart (E/A, E/e' and LVDd) in WT mice, but not in PS2KO mice (Figure 6D-F).
We then isolated cardiomyocytes from I/R-treated WT and PS2KO mice to evaluate their contraction properties. I/R injury blunted the PS, +dL/dt and TPS values in cardiomyocytes from WT mice, but not from PS2KO mice (Figure 6G-L). Moreover, cardiomyocytes isolated from PS2KO mice could maintain normal relaxation function after I/R injury, in contrast to those from WT mice (Figure 6G-L). These results demonstrated that knocking out PS2 normalized heart function during I/R injury.
## Knocking out PS2 attenuates I/R-induced damage in the heart
We then assessed the effects of PS2 deficiency on cardiac damage after reperfusion dysfunction. HE assay revealed that reperfusion caused myocardial fiber swelling in WT mice, whereas this structural change was significantly ameliorated in PS2KO mice (Figure 7A). Electron microscopy indicated that I/R injury led to mitochondrial morphological disorder in myocardia from WT mice, but not from PS2KO mice (Figure 7B). Moreover, qPCR analysis of pro-inflammatory cytokines demonstrated that IL-6, MCP1 and TNFα were significantly elevated in the myocardium following I/R injury (Figure 7C-E), while loss of PS2 prevented pro-inflammatory cytokine activation.
In vitro, small interfering RNA (siRNA) against PS2 was transfected into HL-1 cells prior to H/R injury. A CCK-8 assay demonstrated that reoxygenation suppressed the viability of HL-1 cells, while PS2-siRNA negated this effect (Figure 7F). Although H/R injury promoted the activation of capsase-9 and caspase-12, PS2-siRNA treatment prevented caspase-9 activation (Figure 7G and H). These results confirmed that PS2 inhibition could alleviate I/R-induced cardiac injury.
## PS2 silencing maintains mitochondrial function in H/R-treated cardiomyocytes
Finally, to determine whether PS2 inhibition protected mitochondria during cardiac I/R injury, we further analyzed mitochondrial function in HL-1 cells following PS2-siRNA treatment. As shown in Figure 8A, H/R injury repressed ATP production in cardiomyocytes, while PS2-siRNA treatment preserved ATP synthesis. PS2-siRNA also prevented mitochondrial respiratory complex inactivation (Figure 8B-D) and sustained the mitochondrial potential in reoxygenation-treated cardiomyocytes (Figure 8E and F). Moreover, PS2 inhibition markedly suppressed cellular oxidative stress (Figure 8G and H) and prevented mPTP opening (Figure 8I) following H/R treatment. Therefore, we concluded that PS2 silencing normalized mitochondrial function in H/R-treated cardiomyocytes.
## Discussion
The molecular mechanisms underlying cardiac reperfusion dysfunction were fully delineated, so there are not yet effective treatment approaches for cardiac reperfusion dysfunction in clinical practice. Our present study had three main findings: 1) TMBIM6 overexpression exerts cardioprotective effects by normalizing mitochondrial function and cardiomyocyte viability during myocardial I/R injury; 2) abnormal PS2 upregulation seems to augment reperfusion-motivated cardiac damage by disrupting the mitochondrial integrity and promoting cardiomyocyte death; 3) TMBIM6 downregulates PS2 by binding directly to it, thereby preserving the mitochondrial integrity and cardiac function. Our findings identified TMBIM6/PS2 as a novel signaling pathway in the pathogenesis of cardiac reperfusion dysfunction. Thus, stabilization of TMBIM6 expression, inhibition of PS2 activation and preservation of mitochondrial function are promising therapeutic strategies to reduce reperfusion-caused cardiomyocyte dysfunction and heart failure.
Mitochondrial dysfunction is well known to induce or exacerbate cardiac I/R injury 39, 49-52. I/R injury causes excessive mitochondrial fission, and the resulting fragmented mitochondria exhibit reduced ATP production 39, 53, 54. I/R injury also impairs mitochondrial function by inhibiting mitochondrial autophagy, thus triggering cardiomyocyte death and cardiac dysfunction 39, 47, 55-57. A reduced mitochondrial potential as well as augmented mitochondria-derived ROS generation lead to cardiomyocyte oxidative stress 58. In addition, excessive mitochondrial calcium uptake interrupts mitochondrial oxidative phosphorylation and promotes the opening of the mPTP, an early marker of cardiomyocyte necrosis 59. Therefore, mitochondria seem to be a key treatment target during myocardial reperfusion dysfunction 60. Herein, we found that reperfusion promoted mitochondrial ROS overloading, reduced the mitochondrial membrane potential, inactivated the mitochondrial respiratory complexes and suppressed ATP production. These effects worked together to induce mitochondrial dysfunction in the reperfused heart.
Our results identified PS2 as a novel inducer of mitochondrial damage, as increased PS2 expression was associated with reduced mitochondrial integrity. Accordingly, previous research described the mitochondrial involvement of PS2 in Alzheimer's Disease 61. Mutations in PS2 were found to promote the accumulation of amyloid-β 62. PS2 deficiency was reported to induce mitochondria-ER interactions and facilitate the transfer of calcium from the mitochondria into the ER 20. Abnormal calcium signaling in mitochondria due to PS2 was found to alter the mitochondrial morphology and activate mitochondrial apoptosis 63. PS2 was also shown to enhance mitochondria-ER contact by binding to the mitochondrial fusion protein mitofusin 2 64.
In the present study, we used PS2 knockout mice to investigate the influence of PS2 on myocardial reperfusion dysfunction. We found that loss of PS2 improved the mitochondrial integrity and favored cardiomyocyte viability during I/R injury. PS2 deficiency maintained the mitochondrial potential, reduced mitochondria-derived ROS production and inhibited mitochondria-induced cardiomyocyte death, thus enhancing the viability of cardiomyocytes and elevating their resistance to reperfusion injury. To date, this is the first exploration to demonstrate that PS2 induces mitochondrial dysfunction during I/R injury.
Our results also showed that TMBIM6 is an upstream inhibitor that binds to and post-transcriptionally downregulates PS2. Through molecular docking and protein-protein interaction analyses, we demonstrated that TMBIM6 can prevent PS2 accumulation in cardiomyocytes during I/R injury. However, in our reperfusion model, TMBIM6 was markedly downregulated in the myocardium, while PS2 was upregulated. Overexpression of TMBIM6 was able to reduce PS2 expression, thereby preserving the mitochondrial integrity and enhancing cardiac function upon I/R injury.
Previous studies have had similar findings regarding TMBIM6 65. Zhou et al. reported that myocardial I/R injury suppressed TMBIM6 expression by upregulating the DNA-dependent protein kinase catalytic subunit, which recognizes double-stranded DNA damage in cardiomyocytes 34. Sufficient TMBIM6 expression was identified as a prerequisite for the activation of mitochondrial autophagy and the mitochondrial adaptive stress response, two mitochondrial quality control mechanisms that alleviate mitochondrial injury 66. Ample TMBIM6 expression was found to enhance calcium-related mitochondrial bioenergetics 31 and mitochondrial glucose metabolism 67. Moreover, TMBIM6 was shown to inhibit Bax-induced mitochondrial apoptosis 68. These results illustrate that TMBIM6 can protect the heart by attenuating mitochondrial damage and preserving myocardial function.
Overall, our data demonstrated that TMBIM6 downregulation and PS2 upregulation are pathological contributors to mitochondrial damage during cardiac I/R injury. Restoring sufficient TMBIM6 expression can prevent PS2 accumulation, thus interrupting reperfusion-induced mitochondrial damage for the benefit of the heart. Therefore, TMBIM6 and PS2 are potential therapeutic targets in patients with cardiac I/R injury.
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|
---
title: Cthrc1 deficiency aggravates wound healing and promotes cardiac rupture after
myocardial infarction via non-canonical WNT5A signaling pathway
authors:
- Di Wang
- Yaping Zhang
- Tianbao Ye
- Runlei Zhang
- Lili Zhang
- Dongmei Shi
- Taixi Li
- Guofang Xia
- Kaifan Niu
- Zhe Zhao
- Yu Chen
- Weijun Pan
- Liang Liu
- Xian Jin
- Chengxing Shen
journal: International Journal of Biological Sciences
year: 2023
pmcid: PMC10008688
doi: 10.7150/ijbs.79260
license: CC BY 4.0
---
# Cthrc1 deficiency aggravates wound healing and promotes cardiac rupture after myocardial infarction via non-canonical WNT5A signaling pathway
## Abstract
Cardiac fibroblasts are crucial for scar formation and cardiac repair after myocardial infarction (MI). Collagen triple helix repeat containing 1 (CTHRC1), an extracellular matrix protein, is involved in the pathogenesis of vascular remodeling, bone formation, and tumor progression. However, the role and underlying mechanism of CTHRC1 in post-MI wound repair are not fully clear. Bioinformatics analysis demonstrated CTHRC1 up-regulation in cardiac fibroblasts after ischemic cardiac injury. Serum levels of CTHRC1 were increased in MI mice and CTHRC1 expression was up-regulated in cardiac fibroblasts after MI. In vitro results showed that the induction of CTHRC1 expression in cardiac fibroblasts was mediated by canonical TGFβ1-Smad$\frac{2}{3}$ signaling axis. Moreover, CTHRC1 improved wound healing and boosted cardiac fibroblast activation in vitro. Cthrc1 deficiency aggravated cardiac function and reduced collagen deposition as well as increased mortality attributable to cardiac rupture after MI. Consistent with above phenotypes, reduced the levels of myocardial CD31, α-smooth muscle actin, collagen I, and collagen III was observed, whereas myocardial expression of matrix metalloproteinase 2 and matrix metalloproteinase 9 were increased in Cthrc1 knockout mice post-MI. Above effects could be partly reversed by rCTHRC1 protein or rWNT5A protein. Our study indicates that cardiac fibroblast-derived, canonical TGFβ1-Smad$\frac{2}{3}$-dependent CTHRC1 could improve wound repair and prevent cardiac rupture after MI via selectively activating non-canonical WNT5A-PCP signaling pathway.
## Introduction
Acute myocardial infarction (AMI) has been regarded as the most severe clinical manifestation of coronary heart disease and remains the leading cause of morbidity and mortality worldwide 1, 2, in spite of substantial improvements in clinical prognosis over the past decade due to the optimal medical treatment and the extensive use of percutaneous coronary intervention as well as coronary artery bypass graft surgery. Patients who survived MI still suffer from risk of heart failure because of excessive inflammation, impaired wound healing and deformed scar formation, as well as enhanced cell loss and contractile dysfunction, consequently facilitating infarct expansion, cardiac rupture, and adverse remolding 3. Therefore, it is of importance to identify novel potential therapeutic targets to alleviate inflammation and promote wound repair after MI.
As is known to all, the reparative and remodeling process after MI can be separated into three distinct phases: the inflammatory phase (3 hours to 3 days), the proliferative phase (3 days to 14 days) and the scar stabilization and maturation phase (14 days to 2 months) 4. In the dynamic changed environment of the ischemic heart, cardiac fibroblasts display a functional diversity which may reflect their phenotypic heterogeneity and therefore contribute essentially to wound healing after MI. During the inflammatory phase of cardiac repair, pro-inflammatory cardiac fibroblasts can secrete multiple cytokines and chemokines to recruit and stimulate leukocytes, and release matrix metalloproteinases (MMPs) to facilitate extracellular matrix degradation as well as release of pro-inflammatory matrix fragments. Removal of deribs from dead cells in the infarcted heart activates anti-inflammatory signals, thereby resulting in resolution of inflammation and transformation into the proliferative phase of cardiac repair. Cardiac fibroblasts migrate as well as undergo myofibroblast differentiation with incorporation of α-smooth muscle actin (α-SMA) into stress fibers and activated myofibroblasts deposit an abundance of extracellular matrix proteins to prevent cardiac rupture. During scar stabilization and maturation, cardiac fibroblasts disassemble α-SMA-decorated stress fibers and secrete matrix-crosslinking enzymes, which can further promote extracellular matrix crosslinking and remolding. Up to now, the molecular basis for the phenotypic transformation of cardiac fibroblasts in the phase of cardiac repair remains unclear. Thus, understanding the endogenous mechanisms of cardiac fibroblast phenotypic transition may help discover novel promising therapeutic targets post-MI.
To identify the functional candidate genes potentially related to cardiac fibroblast activation after ischemic cardiac injury, we resorted to the multicellular transcriptional dataset 5 downloaded from the Gene Expression Omnibus (GEO) database and a prior published single-cell RNA sequencing data 6 to screen differentially expressed genes (DEGs) in cardiac fibroblasts post-MI. The up-regulated molecule collagen triple helix repeat containing 1 (CTHRC1) attracted our attention owing to its high conservatism and association with vascular remolding, which is worthy of further investigation in ischemic heart disease. CTHRC1, a secreted extracellular matrix glycoprotein that is highly conserved from lower chordates to mammals 7, plays an essential role in biological functions. Prior studies showed that CTHRC1 participated in varieties of physiological as well as pathological processes, containing vascular remodeling 7-10, bone formation 11-15, developmental morphogenesis 16-20, rheumatoid arthritis 21-24, glucose and lipid metabolism 25-27, as well as organ fibrosis (such as dermal 28-30, lung 31-33, and liver fibrosis 34, 35). A rapidly growing number of studies have demonstrated that CTHRC1 was highly expressed in a wide range of human solid tumors and functionally related to tumor cell proliferation, migration, invasion, metastasis, as well as tumor angiogenesis 36-39. More recently, Adrián et al. 40 used single-cell RNA sequencing to identify the new CTHRC1+ sub-population of cardiac fibroblasts in post-MI mice, which localized into the scar and was characterized by profibrotic action. They showed that the absence of Cthrc1 induced lethality caused by cardiac rupture in mice after MI. However, the precise role and potential underlying mechanisms of CTHRC1 in wound healing after MI are not fully elucidated. Therefore, this study aimed to explore the effect of CTHRC1 on post-infarction cardiac repair and its underlying molecular mechanisms.
## Materials and Methods
An expanded and detailed materials and methods section of this study is available in the Online Data Supplement.
## Ethics statement
All experiments involving animals were conducted according to the ethical policies and procedures approved by the ethics committee of Shanghai Jiao Tong University Affiliated Sixth People's Hospital, China (Approval no. DWLL2022-0462).
## Statistical Analysis
All values are presented as the mean±standard error of the mean (SEM) of independent experiments or independent samples with given n sizes. Statistical analysis was performed with GraphPad Prism 7.0 (Graph Pad Prism Software, Inc, San Diego, CA). Detailed statistical analysis is provided in the Online Data Supplement.
## Serum levels of CTHRC1 were elevated in AMI patients and in MI mice and CTHRC1 expression was increased in cardiac fibroblasts after MI
To identify the novel candidate genes potentially correlated with the phenotypic transition of cardiac fibroblasts after MI, the multicellular transcriptional dataset 5 GSE95755 downloaded from the GEO database was analyzed. DEGs were presented by a heat map (Figure S1A) and a volcano pot (Figure S1B). Selected up-regulated DEGs showed that CTHRC1 expression was significantly up-regulated in cardiac fibroblasts post-MI (Figure S1C). Furthermore, the prior published single-cell RNA sequencing 6 performed on four main cardiac cell types, including cardiac fibroblasts, cardiomyocytes, endothelial cells, as well as macrophages under homeostatic and ischemic conditions, which revealed cardiac fibroblasts highly expressed CTHRC1 3 days post-ischemia/reperfusion, in comparison with other cardiac cell populations (Figure S1D). To evaluate the clinical relevance of CTHRC1 in human AMI, serum levels of CTHRC1 were measured in AMI patients ($$n = 40$$) compared to healthy people ($$n = 40$$). We found that serum CTHRC1 levels of human were increased at day 7 after MI (Figure S2), which is consistent with those in MI mice (Figure 1A). As depicted in Figure 1B, early inflammatory phase after MI is characterized by massive cellular infiltration and tissue digestion, lasting up to 3 days in mice, subsequently followed by a phase of active resolution of inflammation and wound repair with myofibroblast conversion (lasting≈3 days-14 days in mice) 4. To clarify the function of CTHRC1 in MI, we first investigated CTHRC1 expression levels in the myocardium at different time points after MI (Figure 1C). In comparison with sham controls, CTHRC1 increased after MI, peaking at day 7 post-MI (Figure 1D-1H). Moreover, CTHRC1 expression were significantly higher in the infarct and border zones in comparison with the remote zone in WT mice (Figure 1D). Consistently, immunohistochemistry analyses of heart tissue samples confirmed that CTHRC1 was predominantly expressed in the infarct as well as border areas in conformity to the localization of vimentin, with low expression in the remote area (Figure 1I and S3A). Western blotting results using the isolated primary cardiac fibroblasts and cardiomyocytes from WT mice with or without MI further confirmed that CTHRC1 was mainly expressed in cardiac fibroblasts in the post-MI heart tissue (Figure 1K, 1L, and S3B). Immunofluorescence co-localization for CTHRC1 with vimentin, cTnI, and CD31 in infarcted heart tissue from WT mice collected at day 7 post-MI revealed the enrichment of CTHRC1 in cardiac fibroblasts (Figure 1J), the low expression in endothelial cells (Figure S3C) but not in cardiomyocytes (Figure S3D). These data elucidate CTHRC1 enrichment in cardiac fibroblasts in the infarcted tissue during the proliferative phase of MI, implying a potential role of CTHRC1 in modulating wound healing and cardiac fibroblast activation post-MI.
## Induction of CTHRC1 by canonical TGFβ1-Smad2/3 signaling axis in cardiac fibroblasts
It is well established that TGF-β1 is the primary factor that activate fibroblasts and drives fibrosis acting through a canonical Smad signaling pathway that involves Smad4 binding to the phosphorylation of Smad2 and Smad3 by the TGFβ receptor I/II 41. Since CTHRC1 was predominantly expressed in cardiac fibroblasts, we then explored whether TGF-β1 can induce CTHRC1 expression in cardiac fibroblasts in vitro. Primary cardiac fibroblasts from WT mice were treated with serial concentrations of TGFβ1 and stimulated by TGF-β1 at different time points. The results showed that CTHRC1 was induced by TGFβ1 in a dose-dependent (Figure 2A and 2B) and in a time-dependent manner (Figure 2C and 2D), which was also found in neonatal mouse primary cardiac fibroblasts treated with TGFβ1 (Figure 2E). LY2109761, the specific inhibitor for TGFβ receptor I/II or E-SIS3, the specific inhibitor for the phosphorylation of Smad3 were used to interrupt the canonical TGFβ1 signaling pathway. As expected, TGFβ1 induced CTHRC1 up-regulation was inhibited in primary cardiac fibroblasts (Figure 2F through 2I). In summary, these data indicate the induction of CTHRC1 by canonical TGFβ1-Smad$\frac{2}{3}$ signaling pathway in cardiac fibroblasts.
## CTHRC1 improved wound healing and promoted cardiac fibroblast activation in vitro
In view of CTHRC1 levels was increased in cardiac fibroblasts, we further investigated the effect of CTHRC1 on cardiac fibroblast migration and activation in vitro. Firstly, the impact of CTHRC1 on cardiac fibroblast migration was examined using an in vitro wound healing model. Compared with untreated primary cardiac fibroblasts, treatment of primary cardiac fibroblasts with 1 μg/mL of rCTHRC1 protein increased their migratory capacity, as evaluated by the covered area at 24 h after the initial scratch (Figure 3A and 3B). Stimulation with rCTHRC1 protein resulted in a significant up-regulation of α-SMA expression (Figure 3C and 3D). Immunofluorescence staining further confirmed that rCTHRC1 protein induced α-SMA expression in the primary cardiac fibroblasts (Figure 3E and 3F). To summarize, these results indicate that CTHRC1 can improve wound healing and promote cardiac fibroblast activation in vitro.
## Cthrc1 deficiency aggravated cardiac function and exacerbated MI-induced cardiac rupture as well as reduced collagen-rich scar formation after MI
To explore the in vivo role of CTHRC1, we first examined whether Cthrc1 deficiency would impact cardiac function after MI (Figure 4A). C1KO and WT male mice at 8-10 weeks of age were subjected to a permanent ligation of the LAD. Echocardiographic parameters measured in C1KO mice showed that Cthrc1 deficiency did not affect cardiac function in comparison with that in WT mice at baseline (Figure S9). Cardiac function was assessed at day 1 and day 14 post-MI. No significant differences in cardiac function between CIKO mice and WT mice were observed at day 1 after MI (Figure 4B and 4D), but EF, FS, LVESV, LVEDV, LVESD, and LVEDD were significantly exacerbated in survived C1KO mice compared with survived WT mice at day 14 post-MI (Figure 4C and 4D). TTC staining and masson's trichrome staining performed on serial heart cross sections demonstrated a similar increase of infarct size between WT and C1KO mice at day 7 after MI (Figure S4A, 5D, and 5E). Moreover, the ratio of heart weight to body weight at day 14 after MI was significantly higher in C1KO mice than that in WT mice (Figure 4E and 4F), suggesting their compromised cardiac function compared with that in WT mice.
Next, we investigated the influence of CTHRC1 on the post-MI survival. We found that the survival rate in C1KO mice was lower than that in WT mice within day 14 after MI ($36\%$ vs $80\%$; $$P \leq 0.0613$$; Figure 5A). The rate of cardiac rupture was significantly higher in C1KO mice, as compared with WT mice ($64\%$ vs $20\%$; $$P \leq 0.0318$$; Figure 5B and 5C). Similarly, masson's trichrome staining performed on sequential heart transverse sections demonstrated Cthrc1 deficiency did not affect infarct size between WT and C1KO mice at day 7 after MI, but resulted in decreased wall thickness in the infarct zone in comparison with that in WT mice on day 7 after MI (Figure 5D and 5E). To further clarify mechanism underlying the increased incidence of cardiac rupture post-MI in C1KO mice, collagen density in the infarct zone was evaluated by masson's trichrome staining and picrosirius red staining performed on cross sections of heart at apical as well as papillary level. The results demonstrated that collagen volume fraction in the infarct zone was significantly lower in C1KO hearts than that in WT hearts at day 14 post-MI (Figure 5F and 5G). However, collagen density in the remote area was similar between WT and C1KO mice (Figure S5A through S5C). To conclude, this finding of higher risk of cardiac rupture in C1KO mice post-MI may be contributed to reduced collagen-rich scar formation in C1KO hearts.
To explore the mechanism by which Cthrc1 deficiency promotes cardiac rupture, biomarkers for wound repair in the early phase of MI were analyzed (Figure 6A). A variety of evidence supports the importance of MMP2 and MMP9 in cardiac repair after MI via breaking down collagens 42-44. Western blotting analysis demonstrated up-regulated MMP2 as well as MMP9 expression in the infarct area of C1KO hearts at day 7 post-MI, when compared with WT mice (Figure 6B and 6C). During the proliferative phase of cardiac repair, cardiac fibroblasts undergo myofibroblast transformation, incorporating α-SMA into stress fibers and activated myofibroblasts produce extracellular matrix proteins such as collagen I and collagen III, which boosts granulation tissue formation with greater tensile strength as well as more stress fibers to prevent post-infarction cardiac rupture 3. As expected, significantly reduced α-SMA protein levels (Figure 6D and 6E) as well as decreased gene expression of collagen I and collagen III (Figure 6H) were observed in C1KO hearts at day 7 after MI, as compared with WT mice. Moreover, less extracellular matrix proteins deposition was evidenced in C1KO mice, as reflected by immunofluorescence staining of collagen I as well as collagen III on day 7 after MI (Figure 6I). Less α-SMA+ myofibroblasts deposition was also evidenced in the infarct area of C1KO hearts (Figure 6I). Post-infarction angiogenesis has been demonstrated to be essential for cardiac repair post-MI 3. In line with this, immunoblot analysis revealed that protein levels of CD31 were significantly lower in C1KO mice than that in WT control mice at day 7 after MI (Figure 6F and 6G). In addition, angiogenesis level determined by immunofluorescence staining of CD31 was lower at day 7 post-MI in C1KO mice (Figure 6J). These results further suggest retarded cardiac repair post-MI may contribute to the higher cardiac rupture incidence in C1KO mice.
## CTHRC1 improved cardiac repair after MI via selectively activating non-canonical WNT5A-PCP signaling pathway
In order to further elucidate the molecular mechanisms underlying the effects of CTHRC1 on post-infarction wound repair, the transcriptomes of WT and C1KO left ventricle tissues at 7 days after MI were examined using RNA sequencing. Bioinformatic analysis demonstrated that Cthrc1 deficiency significantly changed the expression of genes in MI hearts (Figure S6A and S6B). Further KEGG analysis revealed the top 20 enriched KEGG pathways of the DEGs with Cthrc1 deficiency (Figure S6C) and several DEGs were selected based on the RNA-seq data (Figure S6D). Among them, FZD6 is regarded as the receptor of WNT signaling pathway, which has been reported to be involved in the development of myocardial infarction 45-53. To confirm these results. We also used online available databases STRING and GeneMANIA to predict the protein-protein interactions. The prediction results (Figure S7) revealed that CTHRC1 could interact with ROR2, WNT5A, FZD3, and FZD6, all of which participate in the non-canonical WNT5A-planar cell polarity (PCP) signaling pathway, which is a known pathway of developmental processes. Thereby, we analyzed the key signals and target genes downstream of non-canonical WNT5A-PCP signaling pathway. The expressions of its target genes, such as ROR2, DVL2, and p-JNK/JNK were differently expressed between C1KO mice and WT mice post-MI (Figure 7A and 7B). It has been reported that FZD3 and FZD6 were also involved in the canonical WNT3A-β-catenin signaling axis. Thus, to bear out the impacts of CTHRC1 on canonical WNT3A-β-catenin signaling pathway, its target proteins such as p-GSK3β/GSK3β, active-β-catenin/β-catenin, p-LRP6 and LRP6 were also detected by immunoblotting. Actually, these proteins were not affected in the absence of Cthrc1 (Figure 7C and 7D). Furthermore, in vitro, the isolated primary cardiac fibroblasts were treated with rCTHRC1 protein. In contrast, the expression levels of ROR2, DVL2, and p-JNK/JNK were increased in comparison with that of the untreated cardiac fibroblasts (Figure 7E and 7F). Consistent with the in vivo experiments, its downstream proteins of the canonical WNT3A-β-catenin signaling pathway still remained unchanged with the stimulation of rCTHRC1 protein (Figure 7G and 7H). Based on the above results, we focused on the role of non-canonical WNT5A signaling pathway in post-infarction wound healing. Previous study has borne out that CTHRC1 was a WNT co-factor that selectively activates the non-canonical WNT5A-PCP signaling pathway by forming a stabilized CTHRC1-WNT5A-FZD$\frac{3}{6}$-ROR2 complex to enhance the interaction of WNT5A with FZD$\frac{3}{6}$-ROR216. To verify whether CTHRC1 mediates its effects on WNT5A-FZD$\frac{3}{6}$-ROR2 via a direct interaction, we performed an immunofluorescence co-staining in isolated primary cardiac fibroblasts treated with TGFβ1. The results revealed that CTHRC1 and WNT5A, ROR2, as well as FZD$\frac{3}{6}$ co-localized in cardiac fibroblasts, respectively (Figure 7I). To sum up, these results demonstrate that CTHRC1 interacts with WNT5A, ROR2, and FZD$\frac{3}{6}$ to selectively activates non-canonical WNT5A signaling pathway in cardiac fibroblasts, subsequently promoting cardiac fibroblast activation and improving wound healing after MI.
To further confirm that CTHRC1 improved post-MI wound healing partly by non-canonical WNT5A signaling pathway, rWNT5A protein was continuously injected into C1KO mice for 7 days (0.1 μg/d) after MI (Figure 8A). In agreement with our expectations, rWNT5A protein injection had lower frequency of cardiac rupture compared with PBS control groups (Figure 8B). Interestingly, rWNT5A protein therapy promoted more collagen-rich scar formation than those in C1KO mice treated with PBS (Figure 8C and 8D), as was also evidenced by the increased α-SMA expression in the infarct area of C1KO hearts at day 7 after MI (Figure 8E and 8F). Furthermore, the expression of downstream proteins involved in non-canonical WNT5A-PCP signaling pathway was up-regulated caused by rWNT5A protein injection (Figure 8G and 8H). Taken together, rWNT5A protein treatment reversed Cthrc1 loss-induced post-MI cardiac rupture, which further supports that CTHRC1 improved post-MI cardiac repair partly by non-canonical WNT5A signaling pathway.
We next tested CTHRC1 as a therapy for acute MI. To assess the therapeutic potential of CTHRC1 in the early phase of acute MI, we induced MI by coronary artery ligation followed by a continuous intraperitoneal injection of rCTHRC1 protein for 7 days (1 μg/d) (Figure 8I). Intriguingly, rCTHRC1 protein therapy could prevent cardiac rupture after MI in C1KO mice (Figure 8J). Moreover, C1KO mice treated with CTHRC1 developed bigger infarct scars compared with PBS control group (Figure 8K and 8L). In line with this, rCTHRC1 protein injection increased the α-SMA expression in the infarct zone of C1KO post-MI hearts (Figure 8M and 8N). Similarly, rCTHRC1 protein treatment up-regulated downstream proteins of non-canonical WNT5A-PCP signaling pathway (Figure 8O and 8P). All those above results suggest that rCTHRC1 protein may represent a promising novel therapeutic agent in the early phase of acute MI.
## Discussion
In the absence of ischemic injury, cardiac fibroblasts remain quiescent but play an important part in maintaining extracellular matrix network. During the proliferative phase of cardiac repair, cardiac fibroblasts migrate and undergo myofibroblast transdifferentiation as well as express α-SMA and secret extracellular matrix proteins in abundance to maintain the structural integrity of the post-infarction ventricle 3. CTHRC1 is one such extracellular matrix protein with a short collagen triple helix repeat domain, which was initially identified in screening for differentially expressed genes in balloon-injured versus normal arteries and involved in vascular remodeling 7-9. Here, we demonstrated a crucial role of CTHRC1 in post-MI cardiac repair. In the present study, we reported that, first, serum levels of CTHRC1 were elevated in MI mice and CTHRC1 expression was increased in cardiac fibroblasts after MI; second, TGFβ1 induced CTHRC1 expression in cardiac fibroblasts via canonical TGFβ1-Smad$\frac{2}{3}$ signaling pathway; third, CTHRC1 improved wound healing and promoted cardiac fibroblast activation in vitro; fourth, Cthrc1 deficiency aggravated cardiac function and exacerbated MI-induced cardiac rupture as well as reduced collagen-rich scar formation after MI; fifth, CTHRC1 improved cardiac repair after MI via selectively activating non-canonical WNT5A-PCP signaling pathway; finally, Cthrc1 loss-induced cardiac rupture after MI was partly reversed by rWNT5A or rCTHRC1 protein. Together, these findings reveal the protective function of CTHRC1 in MI setting and provide novel insights into the molecular mechanisms underlying the wound healing after MI.
The heart contains stores of latent TGF-β1 that can be rapidly activated following MI 54. Active TGFβ1 binds to TGFβ receptors I/II, further promoting the phosphorylation of Smad$\frac{2}{3.}$ Then activated Smad$\frac{2}{3}$ integrates Smad4 into it, which enables this complex to translocate into the cell nucleus and further transcribe fibrosis-related genes 41. Our study identified CTHRC1 as a key factor for cell-specific canonical TGFβ1-Smad$\frac{2}{3}$ signaling axis in the heart. We revealed that TGFβ1 induced CTHRC1 expression in nonmyocyte cells (mainly in cardiac fibroblasts). However, the expression of CTHRC1 was only mild in endothelial cells and not at all in cardiomyocytes.
In this study, CTHRC1 released from cardiac fibroblasts could promote cardiac fibroblast migration and activation, subsequently boosting scar formation as well as enhancing wound repair post-MI. In agreement with our observations, CTHRC1 producing from lung fibroblasts also contributed to the activation of fibroblasts in the lung 33. As of now COVID-19 still in tough situation. Intriguingly, researchers have dissected that expansion of CTHRC1+ pathological lung fibroblasts resulted in rapidly progressing pulmonary fibrosis in patients with COVID-1955, indicating that CTHRC1 might be identified as a novel potential therapeutic target for COVID19 in the future. Of course, CTHRC1 is also regarded as a “selfless” protein that CTHRC1 acted on other cell populations such as osteoblasts, macrophages, and endothelial cells to participate in tissue reparative response. For instance, CTHRC1 produced by osteoclasts might function as a novel guidance molecule to recruit stromal or osteogenic cells into bone resorption sites, thereby facilitating the bone formation activity 12. Previous study conducted by our colleagues found that CTHRC1 increased M2 macrophage via activating the TGF-β and Notch signaling pathways to further improve acute wound healing in a polyvinylalcohol sponge implantation mouse model 56. The latest interesting study published in ATVB certified that the healthy (young) aortic valve was sufficient to repair mild endothelial injury through restoration of endothelium barrier function in short term, which was mediated by TGFβ1-CTHRC1 signaling pathway 57.
Using a permanent mouse model of acute MI, we further illuminated a protective role for cardiac fibroblast-derived CTHRC1 in post-infarction wound repair. Confirming this was the fact that CTHRC1 deficiency increased post-MI cardiac rupture by up-regulating the expression of MMPs and preventing cardiac fibroblast activation as well as reducing collagen deposition. The protein levels of collagen I and collagen III as well as α-SMA were down-regulated in MI C1KO mice, suggesting a lower level of myofibroblast transformation in C1KO mice. As is known to all, activated myofibroblasts have been recognized as the major matrix-synthetic cells in the ischemic heart. They can migrate into the damaged tissue post-MI and express α-SMA as well as produce a great mass of extracellular matrix proteins, thereby resulting in collagen-rich scar formation to prevent cardiac rupture. Moreover, post-MI angiogenesis plays significant roles in the granulation tissue formation 3. As expected, Cthrc1 deficiency also affected angiogenesis after MI, as evidence by lower CD31+ endothelial cells and the reduced protein levels of CD31 in C1KO mice. To my relief, two recent studies have also identified CTHRC1 as consistently increased with MI in mice through a great quantity of gene expression datasets 58, 59. During the course of our study, using single-cell RNA sequencing, CTHRC1 was also bioinformatically identified as a critical cardiac fibroblast-derived secreted factor following ischemic cardiac injury potentially affecting cardiac fibroblast migration in vitro. Interestingly, Cthrc1 deficiency led to the obvious lethality owing to cardiac rupture in post-MI mice 40. However, the underlying molecular mechanisms for the momentous role of CTHRC1 in cardiac repair post-MI remains elusive.
The WNT signaling pathways consist of canonical WNT3A-β-catenin signaling pathway and non-canonical WNT5A-Ca2+ signaling pathway as well as non-canonical WNT5A-PCP signaling pathway. After WNT5A binds to FZD$\frac{3}{6}$-ROR2, the DSH-Rac as well as DSH-Rho complexes activate JNK to modulate the PCP signaling pathway 60. WNT5A signaling pathway has been demonstrated to mediate cardiac fibroblast migration and activation 45, 46, 52. It has been reported that CTHRC1 positively activated the non-canonical WNT5A-PCP signaling pathway by binding directly to FZD$\frac{3}{6}$ other than canonical WNT3A-β-catenin signaling axis in the inner ear and hair follicle development 16, 18. To dissect the underlying molecular mechanisms with regard to the effects of CTHRC1 on cardiac repair post-MI, RNA sequencing of WT and C1KO left ventricle tissues 7 days after MI was performed to screen the differently expressed genes. Besides, the predicted functional associations between proteins obtained from the online available databases STRING and GeneMANIA revealed that CTHRC1 could interact with ROR2, WNT5A, FZD3, and FZD6, which was further confirmed in our study by immunofluorescence co-localization. Moreover, using a permanent mouse model of acute MI and ex vivo isolated primary cardiac fibroblasts treated with rCTHRC1 protein, we observed the reduced expression of downstream proteins involved in non-canonical WNT5A-PCP signaling pathway in C1KO mice, whereas the levels of these proteins were up-regulated in the isolated primary cardiac fibroblasts from WT mice, which was stimulated with rCTHRC1 protein. However, the canonical WNT3A-β-catenin signaling pathway was unaffected. Importantly, rWNT5A protein treatment could reverse Cthrc1 deficiency-induced post-MI cardiac rupture, which further supports that CTHRC1 promoted post-infarction cardiac repair via selectively activating non-canonical WNT5A-PCP signaling pathway other than canonical WNT3A-β-catenin signaling axis. In accordance with our study, professor Zhang, as our sincere friend, testified that CTHRC1 participated in the progression of human colorectal cancer as well as gastrointestinal stromal tumors by promoting cancer cell migration and invasion, which was regulated by activating non-canonical WNT-PCP signaling axis but not canonical WNT-β-catenin signaling pathway 61, 62.
Translation of animal models to clinical practice is often challenging. Thereby, we firstly tested rCTHRC1 protein as a therapy for acute MI in animal models by continuous injection of rCTHRC1 protein within 7 days post-MI. Intriguingly, rCTHRC1 protein therapy could improve post-MI wound healing and reduce cardiac rupture by promoting collagen-rich scar formation, which suggests that rCTHRC1 protein may represent a promising novel therapeutic agent in the early phase of acute MI. To uncover whether CTHRC1 has a similar role in humans, we detected CTHRC1 level in peripheral blood samples obtained from patients with AMI and control group. It was gratifying that serum CTHRC1 levels were increased in patients with AMI, compared with control subjects, which implies that CTHRC1 may be regarded as a predominant mediator during AMI in humans. However, further clinical studies including large samples and reliable follow-up data should urgently be considered.
Several limitations exist in our study. Firstly, this study is limited by the lack of availability of a mouse model where Cthrc1 can be conditionally deleted specifically in cardiac fibroblasts. Up till the present moment, no large-scale effective anti-inflammatory therapeutic strategies for AMI have been successfully translated into clinical practice such as the CANTOS study, the CIRT study, and the COLCOT study. Therefore, another consideration that should be highlighted is that more work will be necessary to further define therapeutic dosing and time window for in vivo regulation of post-infarction wound healing by exogenous rCTHRC1 protein treatment. Moreover, our study focuses on cardiac repair in the early phase of MI, the effect of CTHRC1 on chronic ventricular remolding after MI remains to be further investigated.
In conclusion, we bear out that cardiac fibroblast-derived, canonical TGFβ1-Smad$\frac{2}{3}$-dependent extracellular matrix protein CTHRC1 promotes collagen-rich scar formation in the infarcted heart. Through direct effects on cardiac fibroblast migration and activation in the infarcted tissue, CTHRC1 enhances post-infarction wound repair and reduces cardiac rupture as well as the mortality rate via selectively activating non-canonical WNT5A-PCP signaling pathway. Further understanding of how to regulate CTHRC1 secretion from cardiac fibroblasts during ischemic cardiac injury may provide novel insights on improving wound healing in patients with AMI.
## Author Contributions
D.W., Y.Z., and T.Y. designed and performed the experiments. D.W. analyzed the data and wrote the manuscript. R.Z. and L.Z. contributed to data analysis and figure production. D.S established the mice model of MI. T.L., G.X., K.N., Z.Z., Y.C., and W.P. provided technical assistance and material support. L.L., X.J., and C.S. contributed to funding acquisition, study supervision and manuscript revision. All of the authors approved the manuscript and this submission.
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|
---
title: Mitophagy alleviates cisplatin-induced renal tubular epithelial cell ferroptosis
through ROS/HO-1/GPX4 axis
authors:
- Qisheng Lin
- Shu Li
- Haijiao Jin
- Hong Cai
- Xuying Zhu
- Yuanting Yang
- Jingkui Wu
- Chaojun Qi
- Xinghua Shao
- Jialin Li
- Kaiqi Zhang
- Wenyan Zhou
- Minfang Zhang
- Jiayi Cheng
- Leyi Gu
- Shan Mou
- Zhaohui Ni
journal: International Journal of Biological Sciences
year: 2023
pmcid: PMC10008689
doi: 10.7150/ijbs.80775
license: CC BY 4.0
---
# Mitophagy alleviates cisplatin-induced renal tubular epithelial cell ferroptosis through ROS/HO-1/GPX4 axis
## Abstract
Cisplatin is widely recommended in combination for the treatment of tumors, thus inevitably increasing the incidence of cisplatin-induced acute kidney injury. Mitophagy is a type of mitochondrial quality control mechanism that degrades damaged mitochondria and maintains cellular homeostasis. Ferroptosis, a new modality of programmed cell death, is characterized by iron-dependent phospholipid peroxidation and oxidative membrane damage. However, the role of mitophagy in ferroptosis in kidney disease is unclear. Here, we investigated the mechanism underlying both BNIP3-mediated and PINK1-PARK2-mediated mitophagy-induced attenuation of ferroptosis in cisplatin-induced acute kidney injury. The results showed that cisplatin induced mitochondrial injury, ROS release, intracellular iron accumulation, lipid peroxidation and ferroptosis in the kidney, which were aggravated in Bnip3 knockout, Pink1 knockout or Park2 knockout cisplatin-treated mice. Ferrstatin-1, a synthetic antioxidative ferroptosis inhibitor, rescued iron accumulation, lipid peroxidation and ferroptosis caused by inhibition of mitophagy. Thus, the present study elucidated a novel mechanism by which both BNIP3-mediated and PINK1-PARK2-mediated mitophagy protects against cisplatin-induced renal tubular epithelial cell ferroptosis through the ROS/HO1/GPX4 axis.
## Introduction
Drug-induced acute kidney injury is the second most common cause of acute kidney injury in hospitalized patients, especially in the intensive care unit1, 2. Among all types of acute kidney injury, drug-induced acute kidney injury accounts for 14-$37.5\%$ in several studies 3, 2. Nephrotoxic drugs, such as antibiotics (gentamicin), diuretics (furosemide), chemotherapeutic drugs (cisplatin) and calcineurin inhibitors (tacrolimus), induce acute kidney injury 1. Cisplatin is widely recommended in combination for the treatment of lung cancer 4, bladder cancer 5 and gastric cancer 6. During cisplatin treatment, approximately $20\%$-$30\%$ of patients develop acute kidney injury 7. The mechanism of cisplatin-induced acute kidney injury includes oxidative stress, mitochondrial dysfunction, and endoplasmic reticulum stress, which result in apoptosis, necrosis and ferroptosis of renal tubular epithelial cells, causing rapid loss of kidney function 8-11. Elucidating the precise molecular mechanisms underlying cisplatin-induced acute kidney injury will provide evidence for future treatment.
Mitophagy, a type of specific autophagy that eliminates and degrades damaged mitochondria to ensure the quality control of mitochondria, is essential for cellular homeostasis and mammalian survival 12, 13. There are two major mitophagy pathways, namely, PARK2-dependent and PARK2-independent signaling 14. In the PARK2-dependent pathway, PINK1 activates PARK2 to target many mitochondrial proteins, including NDP52, OPTN and p62, and it combines with LC3 to deliver damaged mitochondria to autophagosomes 15. BNIP3 mediates the PARK2-independent pathway of mitophagy by directly binding with LC3 to initiate mitophagy 15. Mitophagy prevents excessive reactive oxygen species (ROS) accumulation, activates the mitochondrial apoptotic cascade and inhibits mtDNA and damage-associated molecular pattern release, which reduces renal tubular epithelial cell injury and acute kidney injury 16. Our recent studies have confirmed that mitophagy, both PINK1-PARK2-mediated and BNIP3-mediated, protect against contrast-induced acute kidney injury by reducing oxidative stress 17, 18. In cisplatin-induced acute kidney injury, Wang et al. showed that the PINK1-PARK2 pathway decreases apoptosis 19. However, BNIP3-mediated mitophagy and the downstream pathway and mechanism are poorly understood in cisplatin-induced acute kidney injury.
Ferroptosis is a new modality of programmed cell death different from apoptosis, necrosis and pyroptosis, and it is characterized by iron-dependent phospholipid peroxidation and oxidative membrane damage 20. Multiple pathways regulate ferroptosis through redox homeostasis, mitochondrial dysfunction and various signaling pathways, such as Nrf2/Keap1 signaling and p53 signaling 21. GPX4 is a key suppressing factor of ferroptosis via the phospholipid hydroperoxide (PLOOH)-neutralizing enzyme 22. Upregulation of HO1 and loss of GPX4 directly inhibit cystine import, block GSH and promote accumulation of PLOOHs, causing rapid and unrepairable damage to membranes and ferroptosis 23, 24. Moreover, the iron-dependent Fenton reaction, which is induced by excessive Fe2+ and hydrogen peroxide, increases ROS and PLOOH, further promoting ferroptosis 25. In kidney studies, ferroptosis has been reported to exacerbate ischemia reperfusion or folic acid-induced acute kidney injury 26-29. In addition, several researchers have shown that cisplatin activates ferroptosis, thereby aggravating acute kidney injury 11, 30, 31. Unfortunately, the upstream pathway is not completely known in cisplatin-induced acute kidney injury, and it remains unclear whether autophagy, oxidative stress and mitochondrial dysfunction regulate cisplatin-induced ferroptosis.
Here, we hypothesized that mitophagy protects against cisplatin-induced acute kidney injury by reducing renal tubular epithelial cell ferroptosis. The present study was designed to investigate BNIP3-mediated and PINK1-PARK2-mediated mitophagy and its regulation of ferroptosis in a cisplatin-induced acute kidney injury model using Pink1, Park2 and Bnip3 knockout mice. The aim of the present study was to provide a new viewpoint for the treatment of chemotherapeutic drug-induced acute kidney injury.
## 1. Cisplatin induces renal tubular epithelial cell ferroptosis in vivo and in vitro
*We* generated a cisplatin-induced acute kidney injury mouse model as previously described 30, 32. Briefly, male mice (6-8 weeks old and 20-25 g) were intraperitoneally injected with cisplatin (20 mg/kg body weight) and sacrificed after 72 hours (Figure 1A). At the perfusion time, we recorded the body and kidney weights of the mice to calculate the kidney/body weight ratio. After cisplatin treatment, the body weight was significantly decreased, but there was no significant difference in kidney weight (Supplemental Figure 1A-1C), which resulted in an increase in kidney/body weight in the cisplatin group (Figure 1B). The blood urea nitrogen (BUN) and serum creatinine levels were significantly in the cisplatin group, which confirmed that cisplatin induced acute kidney injury in vivo (Figure 1C, 1D). Hematoxylin and eosin (HE) and periodic acid-Schiff (PAS) staining showed more cast formation and intraepithelial vacuolar degeneration as well as a higher tubular injury score in cisplatin-treated kidneys compared to Ctrl kidneys (Figure 1E-1G). F$\frac{4}{80}$, a marker of macrophages, suggested severe inflammatory infiltration after cisplatin injection (Figure 1H, 1I). Transmission electron microscopy (TEM) analysis indicated that cisplatin induced rupture of the outer mitochondrial membrane, disappearance of mitochondrial cristae and vacuolization in mitochondria (Figure IJ, red arrow), which are mitochondrial characteristics of ferroptosis 33, 34. In addition, cisplatin upregulated malondialdehyde (MDA) release and iron levels but downregulated superoxide dismutase (SOD) and glutathione (GSH) levels (Supplemental Figure 1D-1G). Immunoblot analysis showed an increase in transferrin receptor 1 (TFRC) and heme oxygenase 1 (HO1) but a decrease in glutathione peroxidase 4 (GPX4), a specific ferroptosis inhibition protein (Figure 1K, 1L; Supplemental Figure 1H, 1I). These data indicated the presence of cisplatin-induced acute kidney injury, resulting in an increase in ROS, uptake of cellular iron and cell ferroptosis.
In the in vitro study, we first used different concentrations of cisplatin in the cell culture medium from 10 to 80 μM and evaluated cell viability by a CCK8 assay (Figure 1M). To further evaluate the function of cisplatin on the transferrin receptor and ferroptosis, we quantified TFRC, HO1 and GPX4 protein expression by immunoblot analysis, which demonstrated that cisplatin induced ferroptosis in the HK-2 cell line (Figure 1N-1Q).
## 2. Mitophagy is increased in cisplatin-induced acute kidney injury
PINK1-PARK2-mediated mitophagy in cisplatin-induced acute kidney injury has been previously described 19. We verified these data and detected BNIP3-mediated mitophagy in cisplatin-treated mice. Cisplatin treatment upregulated the expression levels of BNIP3, PINK1, PARK2 and LC3B II but downregulated COX IV (mitochondria inner membrane marker) and VDAC (mitochondria outer membrane protein) in the renal cortex (Figure 2A-2G). TEM analysis revealed the formation of mitophagosomes in the renal tubular epithelial cells of cisplatin-treated mice (Figure 2H). In an in vitro study, immunoblot analysis also showed an increase in BNIP3 and LC3B II but a decrease in COX IV and VDAC (Figure 2I-2M). MitoTracker and LysoTracker probes demonstrated that cisplatin treatment promoted mitophagolysosome formation (Figure 2N). These data indicated that mitophagy is increased both in vivo and in vitro in cisplatin-induced acute kidney injury.
## 3. Bnip3 deficiency aggravates cisplatin-induced acute kidney injury
To clarify the function of BNIP3 and BNIP3-mediated mitophagy in cisplatin-induced acute kidney injury, we intraperitoneally injected cisplatin into Bnip3 knockout mice (Figure 3A). In WT mice, cisplatin injection caused a significant reduction in body weight but no difference in kidney weight. Compared to WT cisplatin-treated mice, however, Bnip3 knockout cisplatin-treated mice showed no difference in body weight but had larger kidneys (Figure 3B, 3C; Supplemental Figure 2A, 2B). The kidney/body weight ratio was increased due to relatively smaller body size after cisplatin injection in WT mice, and Bnip3 knockout resulted in a larger kidney size compared to the WT cisplatin-treated group (Figure 3D). The serum creatinine and BUN levels suggested more serious renal damage in Bnip3 knockout cisplatin-treated mice than in WT cisplatin-treated mice (Figure 3E, 3F). The tubular injury marker, kidney injury molecule 1 (KIM1), was also increased in Bnip3 knockout cisplatin-treated mice compared to the WT cisplatin-treated group (Supplemental Figure 2C). HE and PAS staining showed severe epithelial flattening, exfoliation of epithelial cells, tubular basement membrane nakedness and cast formation in Bnip3 knockout cisplatin-treated mouse kidneys as well as higher tubular injury score than the WT cisplatin-treated group (Figure 3G-I). Inflammation in the kidney was evaluated by immunohistochemical staining of F$\frac{4}{80}$, which showed more macrophage infiltration in Bnip3 knockout cisplatin-treated mice than in WT cisplatin-treated mice (Figure 3J, 3K).
## 4. Cisplatin-induced ROS, lipid peroxidation and RTEC ferroptosis are increased in Bnip3 knockout kidneys
The ROS level was measured by MDA and SOD levels. The data suggested higher MDA level and lower SOD level in the kidneys of Bnip3 knockout cisplatin-treated mice compared to WT cisplatin-treated mice (Figure 4A, 4B). The GSH level revealed that Bnip3 knockout further aggravated the reduction in glutathione caused by cisplatin (Figure 4C). Kidney iron levels showed that cisplatin-induced iron release was aggravated in Bnip3 knockout mice compared to WT mice (Figure 4D). TEM analysis showed that mitochondrial injury was increased in Bnip3 knockout cisplatin-treated mice, which was characterized by increased vacuolization in mitochondria and rupturing of mitochondrial cristae (Figure 4E). Immunoblot analysis and quantification of HO1 and GPX4 revealed that cisplatin-induced ferroptosis was exacerbated in Bnip3 knockout kidneys (Figure 4F-4H), which was confirmed by immunohistochemical staining of GPX4 (Figure 4I, 4J). These data indicated that inhibition of BNIP3-mediated mitophagy aggravates cisplatin-induced ferroptosis in renal tubular epithelial cells.
## 5. Silencing BNIP3 upregulates ROS and cell death, which are rescued by ferrstatin-1
We used siRNA to knockdown BNIP3 levels and evaluated cisplatin-induced ROS and cell death levels in HK-2 cells (Figure 5A). The CCK8 assay demonstrated that silencing BNIP3 aggravated cisplatin-induced cell death, but these effects were rescued by ferrstatin-1, a synthetic antioxidative ferroptosis inhibitor (Figure 5B). The MDA level was increased by BNIP3 silencing in the absence of ferrstatin-1 but was reduced in the ferrstatin-1 pretreatment group (Figure 5C). Cisplatin reduced the SOD and GSH levels in HK-2 cells, whereas ferrstatin-1 rescued the SOD and GSH levels (Figure 5D, 5E). These data demonstrated that cisplatin induces ROS and cell death in HK-2 cells, which are aggravated by inhibition of BNIP3-mediated mitophagy, and that ferrstatin-1 rescues ROS and injury caused by BNIP3 knockdown.
## 6. Silencing BNIP3 exacerbates cisplatin-induced lipid peroxidation and ferroptosis
To detect iron metabolism and lipid peroxidation, FerroOrange and BODIPY $\frac{581}{591}$ C11 probes were added after cisplatin incubation. FerroOrange staining showed that cisplatin upregulated Fe2+ in the cytoplasm, which was further increased by silencing BNIP3 in HK-2 cells (Figure 6A). The BODIPY $\frac{581}{591}$ C11 probe revealed that oxidized lipids were increased in the cisplatin group and were aggravated by silencing BNIP3 (Figure 6B). Both Fe2+ release and lipid peroxidation caused by inhibition of BNIP3-mediated mitophagy were rescued by ferrstatin-1 (Figure 6A, 6B). Immunoblot analysis and quantification of HO1 and GPX4 were used to evaluate ferroptosis in vitro. The results indicated that cisplatin upregulated HK-2 cell ferroptosis and that BNIP3 silencing exacerbated cisplatin-induced ferroptosis, which was reversed by ferrstatin-1 (Figure 6C-6E). These data demonstrated that BNIP3-mediated mitophagy limits the aggravation of cisplatin-induced ferroptosis through the ROS/HO1/GPX4 axis.
## 7. Pink1 deficiency aggravates cisplatin-induced RTEC ferroptosis in vivo
Because we demonstrated that BNIP3-mediated mitophagy protected against cisplatin-induced ferroptosis, we further investigated the role of the PARK2-dependent mitophagy pathway in ferroptosis by intraperitoneally injecting cisplatin into both Pink1 knockout mice and Park2 knockout mice (Figure 7A, 9A). The body size did not significantly differ between Pink1 knockout cisplatin-treated mice and WT cisplatin-treated mice (Figure 7B), which was confirmed by the body weights (Supplemental Figure 3A, 3B). The kidney weight and kidney/body weight ratio showed larger kidneys as well as increased serum creatinine, BUN and KIM1 levels in Pink1 knockout cisplatin-treated mice, which indicated that Pink1 knockout aggravated kidney injury (Figure 7D-7F; Supplemental Figure 3C). HE and PAS staining indicated increased cast formation, exfoliation of epithelial cells and tubular basement membrane nicks as well as higher tubular injury scores in Pink1-knockout cisplatin-treated kidneys than in WT cisplatin-treated kidneys (Figure 7G-7I). F$\frac{4}{80}$ staining showed severe inflammatory infiltration in Pink1 knockout cisplatin-treated mice, which also demonstrated that Pink1 knockout aggravated cisplatin-induced kidney injury.
To further verify the involvement of PINK1 in ferroptosis, we evaluated SOD, MDA, GSH and iron levels (Figure 8A-8D). The results showed higher MDA levels and lower SOD and GSH levels as well as increased iron release in Pink1 knockout cisplatin-treated mice compared to the WT cisplatin-treated group. Mitochondrial atrophy and vacuole formation were observed in Pink1 knockout cisplatin-treated mice (Figure 8E). The upregulation of HO1 and downregulation of GPX4 indicated that cisplatin-induced ferroptosis was increased in the Pink1 knockout group (Figure 8F-8H). Immunohistochemistry indicated less GPX4-positive staining in Pink1 knockout cisplatin-treated kidneys than in WT cisplatin-treated kidneys (Figure 8I, 8J). These data demonstrated that inhibition of Pink1 in vivo aggravates cisplatin-induced ROS, ferroptosis and kidney injury.
## 8. Park2 deficiency aggravates cisplatin-induced RTEC ferroptosis in vivo
We intraperitoneally injected cisplatin into Park2 knockout mice to elucidate the role of PARK2-mediated mitophagy and ferroptosis (Figure 9A). The body weight and body weight change were not significantly different in Park2 knockout mice and WT mice treated with cisplatin (Figure 9B; Supplemental Figure 4A, 4B). The kidney appearance, kidney weight and kidney/body weight ratio showed that Park2 knockout cisplatin-treated kidneys were larger than those of the WT cisplatin-treated group (Figure 9B-9D). The serum creatinine, BUN and KIM1 levels were also increased in the Park2 knockout group (Figure 9E, 9F; Supplemental Figure 4C). HE and PAS staining showed increased cast formation and intraepithelial vacuolar degeneration as well as a higher tubular injury score in Park2 knockout cisplatin-treated mice compared to WT cisplatin-treated mice (Figure 9G-9I). Immunohistochemical staining of F$\frac{4}{80}$ demonstrated more inflammatory infiltration in Park2 knockout kidneys (Figure 9J, 9K). These data indicated that Park2 knockout aggravates cisplatin-induced kidney injury.
The role of Park2-mediated mitophagy and ferroptosis was evaluated by ROS, ferroptosis-related mitochondrial damage and key protein levels. Park2 knockout upregulated MDA and iron levels but downregulated SOD and GSH levels, which suggested that PARK2 deficiency resulted in an increase in ROS, loss of glutathione and iron release (Figure 10A-10D). TEM analysis showed increased severe mitochondrial outer membrane rupture and vacuolization in mitochondria in Park2 knockout renal tubular epithelial cells (Figure 10E). Immunoblot analysis and quantification of HO1 and GPX4 indicated that ferroptosis was increased in Park2 knockout cisplatin-treated mice (Figure 10F-10H). Immunohistochemical staining of GPX4 also showed less positive staining in the tubules of Park2 knockout cisplatin-treated mice compared to WT cisplatin-treated mice (Figure 10I, 10J). Taken together, these data demonstrated that PINK1-PARK2-mediated mitophagy protects against cisplatin-induced acute kidney injury by inhibiting renal tubular epithelial cell ferroptosis.
## Discussion
In the present study, the effect of mitophagy on cisplatin-induced ferroptosis was examined using Bnip3 knockout, Pink1 knockout and Park2 knockout mice. The present results showed that cisplatin induced mitochondrial injury, ROS release, intracellular iron accumulation, lipid peroxidation, HO1 signaling upregulation, and GPX4 loss, which resulted in renal tubular epithelial cell ferroptosis and acute kidney injury. Additionally, inhibiting BNIP3-mediated or PINK1-PARK2-mediated mitophagy increased oxidative stress and lipid peroxidation, which aggravated ferroptosis. Ferrstatin-1, a synthetic antioxidative ferroptosis inhibitor, rescued iron accumulation, lipid peroxidation and ferroptosis caused by inhibition of mitophagy. Importantly, Bnip3 knockout, Pink1 knockout and Park2 knockout mice showed more severe kidney injury than WT mice, suggesting the important roles of mitophagy and mitochondrial quality control in cisplatin-induced acute kidney injury (Figure 11). Notably, we confirmed a new mechanism by which mitophagy protects against cisplatin-induced acute kidney injury by decreasing renal tubular epithelial cell ferroptosis, suggesting that mitophagy and ferroptosis are new therapeutic targets for the prevention and treatment of acute kidney injury.
Autophagy is considered a double-edged sword 35, 36. In kidney research, most studies have supported the protective role of autophagy in kidney diseases 16, 37, 38. Other studies have also shown that persistent activation of autophagy in kidney tubular cells promotes renal interstitial fibrosis 39, 40. Mitophagy plays a protective role in cell survival by degrading dysfunctional mitochondria and eliminating cytochrome c and excessive ROS released from damaged mitochondria 19, 41. In acute kidney injury research, Tang et al. 42, 43 showed the protective roles of the PINK1-PARK2 and BNIP3 pathways in ischemia reperfusion-induced acute kidney injury. Wang et al. 44 showed the protective roles of PINK1-PARK2 in septic acute kidney injury. Moreover, our laboratory has demonstrated the roles of PINK1-PARK2- and BNIP3-mediated mitophagy in contrast-induced acute kidney injury 17, 18. Several studies have shown that PINK1-PARK2-mediated mitophagy protects against cisplatin-induced acute kidney injury 19, 45, 46. The previous elucidated mechanisms have mostly focused on mitophagy eliminating damaged mitochondria and decreasing dynamin-related protein 1 (DRP1) as well as mitochondrial fission, cell necrosis and cell apoptosis. The present findings also demonstrated the protective role of PINK1-PARK2-mediated mitophagy (Figure 7, 9). However, few studies have emphasized the BNIP3-independent pathway. Zhou et al. 46 reported that BNIP3L protein is upregulated in cisplatin-induced acute kidney injury rats, and they suggested that the BNIP3/BNIP3L pathway may participate in cisplatin-induced mitophagy. Here, we intraperitoneally injected cisplatin into Bnip3 knockout mice, which demonstrated that serum creatinine, pathological injury and inflammatory infiltration were exacerbated in cisplatin-treated Bnip3 knockout mice (Figure 3). Moreover, we demonstrated the protective function of BNIP3-mediated mitophagy in vitro by transfecting BNIP3 siRNA into the HK-2 cell line (Figure 5). Further, the present study used Pink1 knockout, Park2 knockout and Bnip3 knockout mice to confirm the protective role of both PINK1-PARK2-mediated and BNIP3-mediated mitophagy in cisplatin-induced acute kidney injury. Taken together, these data indicated that both PARK2-dependent and PARK2-independent mitophagy pathways protect against acute kidney injury. Thus, additional studies focusing on mitophagy regulation will help to improve the prognosis of acute kidney injury.
Ferroptosis is a type of programmed cell death driven by iron-dependent phospholipid peroxidation 22. Recent studies have demonstrated that ferroptosis is increased in acute kidney injury. Inducible Gpx4 knockout mice directly aggravate lipid-oxidation-induced acute kidney injury and associated death 33. Melatonin treatment significantly alleviates ischemia reperfusion-induced ferroptosis and acute kidney injury 47. Folic acid induces ferroptosis, not necroptosis, in acute kidney injury 27, which can be rescued by quercetin through inactivation of transcription factor 3 28. Several studies have also demonstrated ferroptosis in cisplatin-induced acute kidney injury. Deng et al. 11 showed that cisplatin-induces ferroptosis through myo-inositol oxygenase regulated GSH and GPX4 activity as well as ferritinophagy 11. Fan et al. 48 indicated that hemopexin is a mediator of iron toxicity in the kidney, and deferoxamine alleviates cisplatin-induced acute kidney injury. Kim et al. 30 used genetic and pharmacological methods to demonstrate that farnesoid X receptor regulates the transcription of ferroptosis genes and reduces cisplatin-induced acute kidney injury. Additionally, the vitamin D receptor, ferrostatin-1 and small GTPase (Ras homolog enriched in brain) also decrease cisplatin-induced nephrotoxicity by ferroptosis 31, 49. The present results confirmed these previously findings. The present study also showed renal tubular epithelial cell ferroptosis in vivo and in vitro using models of cisplatin-induced acute kidney injury, and we demonstrated that the ROS/HO1/GPX4 axis contributes to ferroptosis (Figure 1). Moreover, ferrostatin-1, a synthetic antioxidant, inhibited cisplatin-induced ferroptosis by reducing ROS, Fe2+ release and lipid peroxidation (Figure 5, 6). Taken together, these findings indicated that ferroptosis aggravates acute kidney injury, suggesting that ferroptosis inhibitors may be therapeutic targets for clinical acute kidney injury treatment.
Recent published studies have focused on autophagy and ferroptosis. The mTOR signaling inhibition upregulates autophagy-mediated GPX4 degradation, thereby promoting ferroptosis of bladder cancer cells 50. The AMPK pathway phosphorylates BECN1 at Ser$\frac{90}{93}$/96, enhancing BECN1-SLC7A11 complex formation and subsequently inducing lipid peroxidation and ferroptosis 51. New ferroptosis-related autophagy includes ferritinophagy, lipophagy and clockophagy52, 53, which should be investigated in future kidney disease studies. The controversy between autophagic cell death and cell death accompanied by autophagy remains unclear 54. The results of the present study support the latter opinion. In the present study, cisplatin upregulated both ferroptosis and mitophagy, specifically autophagy (Figure 1,2), and genetic inhibition of BNIP3- or PINK1-PARK2-mediated mitophagy resulted in aggravation of ferroptosis in cisplatin-induced acute kidney injury (Figure 3, 5, 7,9). Thus, the present findings suggested that mitophagy is an upstream protective mechanism that prevents excessive ferroptotic cell death.
In the present study, we focused on mitophagy and ferroptosis in cisplatin-induced acute kidney injury. Few studies have investigated mitophagy and ferroptosis in the kidneys. Previous studies on other diseases have suggested that mitophagy-dependent ferroptosis contributes to cell death. Yu et al. 55 showed that inhibition of O-GlcNAcylation enhances mitophagy, releases labile iron and renders cells more sensitive to ferroptosis. Rademaker et al. 56 indicated that the mitophagy inhibitor, Mdivi1, decreases myoferlin-related ROS accumulation, lipid peroxidation and ferroptosis. Basit et al. 57 and Li et al. 58 also reported mitophagy-dependent ferroptosis in melanoma cells and Alzheimer's disease. The present study also found that cisplatin induced the upregulation of mitophagy, ROS production, lipid peroxidation and ferroptosis (Figure 1). However, previous studies have explained this phenomenon as mitophagy-dependent ferroptosis. Thus, we used genetic mitophagy gene (Bnip3, Pink1 and Park2) knockout mice or knockdown cell lines to inhibit mitophagy combined with the cisplatin-induced acute kidney model in vivo and in vitro to further discuss the relationship between mitophagy and ferroptosis. The results showed that ferroptosis was aggravated and that ROS production, Fe2+ release and lipid peroxidation were increased during mitophagy inhibition (Figure 4, 6, 8, 10). Additionally, we showed that ferrostatin-1 pretreatment rescued the ROS/HO1/ferroptosis axis, which was upregulated by BNIP3 silencing (Figure 6). Importantly, we demonstrated that mitophagy protected against cisplatin-induced ferroptosis via the ROS/HO1/GPX4 pathway and proposed a new hypothesis of ferroptosis with mitophagy upregulation, which was different from the previously proposed mitophagy-related ferroptosis. Li et al. 59 demonstrated that hypoxia inducible factor (HIF) promotes mitophagy and modulates redox homeostasis, which may protect against ferroptosis in acute kidney injury. Additional studies on hypoxia, mitophagy and ferroptosis are currently ongoing.
The renal volume of acute kidney injury is significantly larger than that of healthy individuals. In the present study, we used kidney/body weight to evaluate the renal volume. As shown in Figure 3, 7 and 9, the renal volume of the WT cisplatin-treated group was larger than that of the WT ctrl group, and the Bnip3 knockout, Pink1 knockout and Park2 knockout groups had higher kidney/body weight and creatinine levels than the WT cisplatin-treated group, suggesting that the renal volume was correlated with kidney injury. Interestingly, although the kidney/body weight ratio had a similar trend in the above two comparison groups, the detailed comparisons showed many differences. Compared to the WT ctrl group, WT cisplatin-treated mice had lower body weights due to cisplatin injection, but the kidney weights were not significantly different. Nevertheless, the body weight of Bnip3/Pink1/Park2 knockout cisplatin-treated mice was not different from that of WT cisplatin-treated mice. The kidney weight of Bnip3/Pink1/Park2 knockout cisplatin-treated mice was much higher than that of WT cisplatin-treated mice. Accordingly, cisplatin injection reduced body weight 60 without causing kidney weight loss in 72 hours, which caused cisplatin-induced acute kidney injury. However, the aggravation of cisplatin-induced acute kidney injury in Bnip3/Pink1/Park2 knockout mice was not attributed to only the pharmacological action of cisplatin, which indicated that mitophagy may have an effect on renal volume. This hypothesis will be further investigated in the future.
The present study had some limitations. The Bnip3-/-, Pink1-/- and Park2-/- mice were all global knockout mice. Tubular-specific knockout mice will better show the protective role of mitophagy in cisplatin-induced ferroptosis and kidney injury.
In summary, the present study demonstrated that mitophagy alleviates cisplatin-induced tubular cell ferroptosis through the ROS/HO-1/GPX4 axis. Moreover, renal tubular epithelial cell ferroptosis exacerbates cisplatin-induced acute kidney injury. Mitophagy is activated to reduce excessive ferroptosis and kidney injury, while inhibition of BNIP3- or PINK1-PARK2-mediated mitophagy aggravates ROS release, lipid peroxidation, cell ferroptosis and cisplatin nephrotoxicity. The present findings indicated that ferroptosis inhibitors and mitophagy agonists are potential therapeutic targets for the clinical prevention and treatment of chemotherapeutic drug-induced acute kidney injury.
## Animals and cisplatin-induced acute kidney injury model
Bnip3 knockout mice were constructed at Shanghai Model Organisms Center, Inc. as previously described 61 and verified in our previous study 18. Pink1 knockout mice [017946] and Park2 knockout mice [006582] with a C56BL/6 genetic background were purchased from Jackson Laboratory and described in our previously published report17. All animal experiments were approved by the Animal Care Committee of Renji Hospital, School of Medicine, Shanghai Jiao Tong University. Male mice, aged 6-8 weeks and weighing 20-25 g, were administered cisplatin (20 mg/kg, Jiangsu Hansoh, H20040813) or normal saline through intraperitoneal injection (ip)30, 32. The mice were sacrificed after 72 hours to harvest serum and kidneys for subsequent analyses.
## In vitro cisplatin treatment
The human renal proximal tubular cell line (HK-2 cell line) was obtained from the American Type Culture Collection (ATCC®, CRL-2190) and cultured in Dulbecco's modified Eagle's medium (DMEM)/F-12 (Thermo Fisher Scientific, 11330057, New York, USA) supplemented with $1\%$ penicillin‒streptomycin (Thermo Fisher Scientific, 10378016) and $10\%$ fetal bovine serum (Thermo Fisher Scientific, 10099158). HK-2 cells were transfected with 50 nm BNIP3 siRNA for 8 hours by Lipofectamine™ 3000 Transfection Reagent (Thermo Fisher Scientific, L3000150). Two hours before cisplatin administration, HK-2 cells were pretreated with ferrostatin-1 (10 μM, MCE, HY-100579) 62 followed by treatment with cisplatin (20 μM) for 24 hours, and cells were then harvested 63.
The following small interfering RNA (siRNA) sequence was used, which was confirmed in our previous work 18: BNIP3 siRNA 5′-CGUUCCAGCCUCGGUUUCUAUUUAU-3′. Experiments were performed in triplicate.
## Renal function, histopathology and immunohistochemical staining
Serum creatinine and BUN were used to evaluate the renal function of mice. The serum creatinine (Nanjing Jiancheng Bioengineering Institute, C011-2-1) and BUN (Nanjing Jiancheng Bioengineering Institute, C013-2-1) levels were measured according to the manufacturer's instructions. HE staining and PAS staining of kidneys were performed as previously described 64, 65. The tubular injury score was based on the percentage of damaged tubules as follows: grade 0, no damage; grade 1, injured tubules less than $25\%$; grade 2, $25\%$<injured tubules≤$50\%$; grade 3, $50\%$<injured tubules≤$75\%$; and grade 4, injured tubules more than $75\%$.
For immunohistochemical analyses, 4-μm thick paraffin-embedded kidney sections were deparaffinized using dimethylbenzene. After ethylenediaminetetraacetic acid retrieval, the kidney sections were incubated with anti-F$\frac{4}{80}$ (1:100, Cell Signal Technology, 70076, MA, USA), a marker for macrophage infiltration, and anti-GPX4 (1:100, Abcam ab125066, Cambridge, UK), a marker for ferroptosis, at 4 °C overnight. The images were captured using ZEISS and Axio Vert A1, and they were quantified by ImageJ (ImageJ bundled with 64-bit Java 8).
To label the mitophagosomes, we used MitoTracker Green (50 nM, ThermoFisher Scientific, M7514), LysoTracker Red (50 nM, Beyotime, C1046) and Hoechst (5 μg/mL) to label mitochondria, lysosomes and nuclei, respectively, following the manufacturer's instructions. The images were captured using a ZEISS Axio Vert A1 microscope.
## Transmission electron microscopy
The fresh kidney was cut into 1 mm3 pieces and prefixed in $2\%$ glutaraldehyde. The fixation, dehydration, embedding, polymerization, and lead citrate staining were performed by the Core Facility of Basic Medical Sciences, Shanghai Jiao Tong University of Medicine, as described previously 17. An H-7650 transmission electron microscope (Hitachi, H-7650) was used to detect 70-nm thick sections.
## Cell viability analysis
A CCK-8 kit (Dojindo, CK04) was used to analyze the viability of HK-2 cells. Briefly, after treatment with cisplatin, 10 μl of CCK8 solution was added to cell culture followed by incubation at 37 °C for 4 hours. BioTak CytationTM3 was used to measure the absorbance at 450 nm every 30 minutes.
## Immunoblot analysis
The proteins were subjected to 10-$12\%$ gel electrophoresis as described previously 66. The membranes were incubated with the following primary antibodies (1:1000 dilution) at 4 °C overnight: TFRC (Cell Signal Technology, 46222, MA, USA), HO1 (Cell Signal Technology, 43966, MA, USA), GPX4 (Abcam, ab125066, Cambridge, UK), BNIP3 (Santa Cruz Biotechnology, sc-56167, TX, USA), PINK1 (Novas Biologicals, BC100-494, CO, USA), PARK2 (Cell Signal Technology, 2132, MA, USA), VDAC (Abcam, ab14734, Cambridge, UK), COX IV (Cell Signal Technology, 4844, MA, USA), LC3B (Sigma‒Aldrich, L7543, MO, USA), KIM1 (R&D, AF1817, MN, USA) and TUBA (Beyotime, AF0001, Shanghai, China).
## ROS and ferroptosis analyses
ROS activity was detected by MDA (Beyotime, S0131S) and SOD (Nanjing Jiancheng Bioengineering Institute, A001-3-2) levels according to the manufacturer's instructions. The MDA and SOD levels were adjusted by protein concentration.
A GSH kit (Nanjing Jiancheng Bioengineering Institute, A006-2-1) was used to evaluate glutathione levels. The FerroOrange (1 μM, Dojindo, F374) probe was added to the cell culture to label Fe2+, and the BODIPY™ $\frac{581}{591}$ C11 (10 μM, ThermoFisher Scientific, M7514) probe distinguished oxidized and nonoxidized lipids 30.
## Statistical analysis
GraphPad Prism 8 (GraphPad) was used for all statistical analyses. The qualitative data are presented as the mean± standard error (SEM). Student's t test was used to compare the differences between two groups, and one-way analysis of variance and Tukey's post hoc test were used to compare differences between more than two groups. A P value less than 0.05 was considered to indicate a significant difference.
## Author contributions
Q.L. designed and performed most experiments and wrote the manuscript. H.J. and H.C. analyzed the data and edited the manuscript. X.Z. and Y.Y. helped design the animal experiments. J.W., S.X. and K.Z. helped design the cell experiments. C.Q., W.Z. and M.Z. analyzed the histology and histopathology. J.L. and J.C. helped interpret data. L.G. and S.M. helped edit the manuscript. S.L. and Z.N. supervised all experiments and edited the manuscript.
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|
---
title: Lipocalin-2 promotes acute lung inflammation and oxidative stress by enhancing
macrophage iron accumulation
authors:
- Hyeong Seok An
- Jung-Wan Yoo
- Jong Hwan Jeong
- Manbong Heo
- Si Hwan Hwang
- Hye Min Jang
- Eun Ae Jeong
- Jaewoong Lee
- Hyun Joo Shin
- Kyung Eun Kim
- Meong Cheol Shin
- Gu Seob Roh
journal: International Journal of Biological Sciences
year: 2023
pmcid: PMC10008694
doi: 10.7150/ijbs.79915
license: CC BY 4.0
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# Lipocalin-2 promotes acute lung inflammation and oxidative stress by enhancing macrophage iron accumulation
## Abstract
Lipocalin-2 (LCN2) is an acute-phase protein that regulates inflammatory responses to bacteria or lipopolysaccharide (LPS). Although the bacteriostatic role of LCN2 is well studied, the function of LCN2 in acute lung damage remains unclear. Here, LCN2 knockout (KO) mice were used to investigate the role of LCN2 in LPS-treated mice with or without recombinant LCN2 (rLCN2). In addition, we employed patients with pneumonia. RAW264.7 cells were given LCN2 inhibition or rLCN2 with or without iron chelator deferiprone. LCN2 KO mice had a higher survival rate than wild-type (WT) mice after LPS treatment. In addition to elevated LCN2 levels in serum and bronchoalveolar lavage fluid (BALF), LPS treatment also increased LCN2 protein in alveolar macrophage lysates of BALF. LCN2 deletion attenuated neutrophil and macrophage infiltration in the lungs of LPS-treated mice as well as serum and BALF interleukin-6 (IL-6). Circulating proinflammatory cytokines and LCN2-positive macrophages were prominently increased in the BALF of pneumonia patients. In addition to increase of iron-stained macrophages in pneumonia patients, increased iron-stained macrophages and oxidative stress in LPS-treated mice were inhibited by LCN2 deletion. In contrast, rLCN2 pretreatment aggravated lung inflammation and oxidative stress in LPS-treated WT mice and then resulted in higher mortality. In RAW264.7 cells, exogenous LCN2 treatment also increased inflammation and oxidative stress, whereas LCN2 knockdown markedly diminished these effects. Furthermore, deferiprone inhibited inflammation, oxidative stress, and phagocytosis in RAW264.7 cells with high LCN2 levels, as well as LPS-induced acute lung injury in WT and LCN2 KO mice. Thus, these findings suggest that LCN2 plays a key role in inflammation and oxidative stress following acute lung injury and that LCN2 is a potential therapeutic target for pneumonia or acute lung injury.
## Introduction
Acute lung injury usually causes acute respiretory distress syndrome (ARDS), which requires intensive care and has a high mortality 1, 2. Acute lung injury is characterized by neutrophil infiltration, epithelial damage, and increased alveolar permeability 3. Lung resident macrophages communicate with the immune microenvironment to suppress or exacerbate inflammatory responses. Alveolar macrophages and monocyte-derived macrophages polarize into classically lipopolysaccharide (LPS)-induced activated macrophage M1 phenotype or into alternatively M2 phenotype in the presence of interleukin (IL)-4 and IL-13 4, 5. In particular, the elevation of M1-like genes using genome-wide transcriptional profiling is associated with death on the first day in alveolar macrophages of the BALF taken from patients with ARDS 6, 7. Although activated macrophages play a role in inflammation and phagocytosis in response to acute lung damage, the molecular mechanisms responsible for engaging activated macrophages are still poorly understood.
Lipocalin-2 (LCN2) is a secreted acute-phase protein also referred to as neutrophil gelatinase-associated lipocalin 8. LCN2 functions in multiple biological processes, including transport of iron, immune defense against bacterial infections, and cell migration and differentiation 9. However, LCN2 plays both protective and detrimental roles in the inflammatory response. For example, LCN2 limits iron uptake in the host, which reduces bacterial growth during the innate immune response 10-12. In experimental models of pneumonia or sepsis, LCN2 deficiency increases bacterial proliferation and lowers host survival 13-17. In contrast, LCN2 deletion has been shown to markedly attenuate the production of proinflammatory cytokines in mice with ARDS 18. Based on these inconsistent evidences, the precise role of LCN2 in acute lung injury is not fully understood.
Iron accumulation is associated with several inflammatory respiratory diseases, including ARDS, and mediates an inflammatory response that produces reactive oxygen species through the Fenton reaction 19, 20. The iron-overloaded lung is vulnerable to oxidative stress since it is constantly exposed to the highest concentration of oxygen. Although the lung expresses high levels of several antioxidants, such as ascorbate, heme oxygenase-1 (HO-1), superoxide dismutase (SOD), and iron-binding proteins, an imbalance of iron and antioxidants leads to oxidative stress 21. Although antioxidants have been suggested as therapeutics for acute lung injury, to our knowledge no studies have been evaluated to yet examining at the efficacy of iron chelators in acute lung injury.
In this study, we investigated the role of LCN2 and its underlying mechanism of action in LPS-treated LCN2-deficient mice with recombinant LCN2 (rLCN2) as well as pneumonia patients. In particular, we found that rLCN2 pretreatment promotes inflammation and oxidative stress in LPS-treated RAW264.7 cells, but iron chelator deferiprone decreased inflammation and phagocytic function. Based on these findings, we hypothesize that LCN2 deletion could protect against pneumonia by inhibiting inflammation and oxidative stress.
## Human patients
From June to November 2020, we obtained blood and bronchoalveolar lavage fluid (BALF) samples from 26 patients with severe pneumonia who were admitted to the medical ICU due to acute respiratory failure. Patients with pneumonia received high-flow nasal cannula oxygen or invasive mechanical ventilation to treat their respiratory failure (Supplementary Table 1). Bronchoscopy with BALF collection was performed to evaluate bronchial lesions, obtain respiratory samples, and remove secretions. As controls, ten patients admitted to the general ward during the same period were selected. These control patients underwent bronchoscopy with BALF collection to evaluate stable chronic lung disease. Demographic data (age, sex, body mass index, comorbidities) and clinical characteristics (septic shock, ARDS, types of oxygen therapy) were recorded (Supplementary Table 1). Various laboratory parameters (white cell count, hemoglobin, platelet, C-reactive protein, albumin, and procalcitonin) were collected and analyzed retrospectively (Supplementary Table 1). The human study protocol was approved by the institutional review board of Gyeongsang National University Hospital (GNUH) [2020-11-009], and written informed consent was waived due to the retrospective nature of the analysis of clinical data and the use of blood and BALF samples provided by the Biobank of GNUH.
## Experimental mouse model
Female and male LCN2 KO mice were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). LCN2 (+/+) and LCN2 (-/-) mice on a C57BL/6J background were back-crossed to produce homozygous and heterozygous mice free of background effects for 8 to 10 generations. The absence of LCN2 was confirmed by PCR analysis of genomic DNA. According to the National Institutes of Health Guidelines on the Use of Laboratory Animals, the animal research was carried out at Gyeongsang National University (GNU). The animal study protocol (GNU-190701-M0033) was authorized by the Animal Care Committee of the GNU. After genotyping at 5 weeks of age, male LCN2 KO and WT mice were randomly divided into four groups ($$n = 6$$-8). Six-month-old mice were intratracheally administered LPS (1 mg/kg, Sigma-Aldrich, St. Louis, MO, USA) or $0.9\%$ normal saline as control. After 24 hours, mice were sacrificed, and samples, including serum, BALF, and lung tissues, were obtained. To determine the percentage of survival, LPS (20 mg/kg) was intratracheally administered to WT and LCN2 KO mice ($$n = 10$$), and these mice were monitored for 8 days. In another study, recombinant LCN2 (rLCN2, 300 μg/kg) was intraperitoneally injected into WT and LCN2 KO mice ($$n = 6$$-8). After 24 hours, saline or LPS (1 mg/kg) was administered, and the mice were sacrificed 24 hours later. To investigate the mortality rate after rLCN2 pretreatment, LPS (20 mg/kg) was administrated to WT and LCN2 KO mice ($$n = 10$$), which were monitored for 8 days. In order to evaluate the protective effect of an iron chelator against acute lung injury, LPS (1 mg/kg) was intratracheally administered to WT and LCN2 KO mice ($$n = 3$$-4) and then iron chelator deferiprone (100 mg/kg, DFP, Sigma-Aldrich) was intraperitoneally injected after 3 hours of LPS instillation. Mice were sacrificed at 24 hours and only BALF was obtained.
## Recombinant LCN2 production
The pET28a-SUMO-LCN2-ABD vector was prepared by insertion of the mouse lcn2 gene (545 bp, GenScript, Piscataway, NJ, USA) into a linearized pET28a-SUMO-ABD vector. The vector was transformed to a BL21 (DE3) E. coli strain for the protein expression. For every production, a starter culture was prepared by inoculating 50 mL of Lura-Bertani (LB) medium (80 µg/mL kanamycin) with a colony of the transformed E coli. After incubation of the culture at 37ºC overnight, the starter culture was added to 1 L of fresh LB medium (80 µg/mL of kanamycin). The culture was maintained at the same incubation conditions, and, when the UV absorbance at 600 nm reached about 1, isopropylthio-D-galactoside (inducer) was added (final concentration: 0.5 mM). After further incubation for 4 h, the E. coli cells were pelleted by centrifugation (4000 rpm for 20 min). The cells were then resuspended in phosphate buffer saline (20 mM phosphate, 300 mM NaCl, $1\%$ soybean protease inhibitor, $1\%$ leupeptin, pH 7), and lysed by sonication (4 cycles with 30 sec run at $50\%$ output on ice). The lysed sample was centrifuged (8000 rpm for 10 min) and the supernatant was loaded to the Talon® metal affinity resins (Clontech, Mountain View, CA, USA) for purification. The expression and purification of the SUMO-LCN2-ABD (recombinant LCN2, rLCN2) was verified by SDS-PAGE.
## Sample preparation
All mice were anesthetized with Zoletil (20 mg/kg, Virbac Laboratories, Carros, France) and Rompun (5 mg/kg, Bayer, Bayer Korea, Republic of Korea). Blood samples were extracted from the left ventricle and centrifuged. For protein extraction, the right lung was isolated, frozen in liquid nitrogen, and stored at -80℃. For histological analysis, the left lung was slowly perfused with $4\%$ paraformaldehyde and immersed in a fixative solution overnight. Lung tissues were embedded in paraffin and cut into 5-µm sections.
## BALF analysis
After the removal of blood, the BALF was collected by instillation and suction of $0.9\%$ normal saline (3 × 0.8 mL) in the lungs with 24-G intravenous catheter. BALF samples were centrifuged for 5 minutes at 400 xg at 4°C, and supernatants were collected and stored at -80℃ until use. The remaining cell pellets were used to prepare cytospin slides to identify cells present in the pulmonary air spaces. Using cytospin (Shandon Cytospin 3 Cyto-centrifuge, Thermo Fisher Scientific, Waltham, MA, USA), slides were prepared by centrifuging pellets at 1,165 xg for 10 minutes. After air-drying, cytospin slides were stained using H&E (Abcam, Cambridge, MA, USA) and Differential Quik stain (Diff-Quik, Polysciences, Inc., Warrington, PA, USA). Cytospin slides were visualized under a BX51 light microscope (Olympus, Tokyo, Japan).
## Isolation of mature macrophages in the BALF
Cells from the BALF were cultured in RPMI 1640 medium (RPMI; Gibco, Life Technologies, MD, USA) supplemented with $10\%$ fetal bovine serum (Gibco), $1\%$ penicillin/streptomycin (Gibco), L-Glutamine 200 mM (Gibco). Mature macrophages were encouraged to adhere to cell culture plates for 2 hours at 37 °C, at which time the media was discarded, and the cells underwent two RPMI medium washes. Collected macrophages were lysed in lysis buffer for western blot analysis.
## Fluorescence-Activated Cell Sorting (FACS) analysis
The cells collected from the BALF were used for flow cytometry. The fluorescent antibodies were purchased from BioLegend (San Diego, CA, USA); Alexa Fluor® 647 anti-mouse CD170 (Siglec-F), 155519; Brilliant Violet 785™ anti-mouse CD11c; 117335; PE/Cyanine5 anti-mouse CD86, 105015; Brilliant Violet 711™ anti-mouse CD206 (MMR), 141727. Dead/Live staining (eBioscience™ Fixable Viability Dye eFluor™ 780) from Invitrogen (Carlsbad, CA, USA) was used to separate dead and live cells. The fluorescence wavelength of the stained cells was measured by BD FACSymphony™ A3 Cell Analyzer (San jose, CA, USA). The FlowJo_v10.8.1 were utilized to set the gates.
## Enzyme-Linked Immunosorbent Assay (ELISA) analysis
Serum and BALF cytokines were measured using mouse IL-6 (ab100713, Abcam), IL-1β (ab197742, Abcam), IL-10 (ab100697, Abcam), LCN2 (R&D Systems, Minneapolis, MN, USA), human IL-6 (ab46042, Abcam), IL-1β (ab214025, Abcam), TNF-α (ab181421, Abcam), IL-10 (ab46034, Abcam), and LCN2 (R&D Systems) ELISA kits according to the manufacturers' protocols.
## Western blot analysis
Frozen lung tissues or cells were homogenized in T-PER (Tissue Protein Extraction Reagent, Pierce, Rockford, IL, USA). Homogenized samples were centrifuged for 30 minutes at 13,475 xg at 4°C. Lysates were probed with primary antibodies (Supplementary Table 2). Using an enhanced chemiluminescence substrate, membranes were seen (Pierce). All band densitometry were measured using the Multi-Gauge image analysis program (Fujifilm, Tokyo, Japan, v3.0). β-actin was used as an internal control to normalize protein levels.
## Immunohistochemistry
Deparaffinized lung sections and BALF cytospin slides were placed in a $0.3\%$ H2O2 solution for 10 minutes. After washing, slides were treated with primary antibodies (Supplementary Table 2) diluted in blocking serum for overnight at 4°C in a humidified chamber. After incubation with a secondary biotinylated antibody (1:200), section slides were incubated in avidin-biotin-peroxidase complex solution (ABC solution, Vector Laboratories, Burlingame, CA, USA). Slides were developed with 3,3-diaminobenzidine (DAB) peroxidase substrate kit (Vector Laboratories), dehydrated in varying alcohol concentrations, cleared in xylene, and cover-slipped with Permount mounting (Sigma-Aldrich). The immunostaining sections were visualized under BX51 light microscopy (Olympus).
## Immunofluorescence
Deparaffinized sections and BALF cytospin slides were incubated overnight at 4°C with primary antibodies (Supplementary Table 2). After washing, slides were incubated with Alexa Fluor donkey anti-secondary antibody (1:1000, Invitrogen). 4′, 6-diamidino-2-phenylindole (DAPI, 1:10,000; Invitrogen) was used to stain the nuclei. Slides were examined under under a BX51-DSU microscope (Olympus) and images were captured.
## Iron staining and quantification
Deparaffinized lung sections and BALF cytospin slides were stained with Perls Prussian blue (Iron Stain Kit, Abcam). The iron stained slides were visualized under a BX51 light microscope (Olympus). Total iron levels from frozen lungs were measured using an iron assay kit (MAK025, Sigma-Aldrich) according to the manufacturer's instructions.
## Cell culture
The macrophage cell line, RAW264.7 cells, was cultured in Dulbecco's modified *Eagle medium* (DMEM; Gibco) supplemented with $1\%$ penicillin/streptomycin (Gibco) and $10\%$ fetal bovine serum (Gibco) at 37 ℃ in a $5\%$ CO2 humidified incubator. RAW264.7 cells were plated at a density of 0.5x106 cells per 60-mm dish. RAW264.7 cells were stimulated with LPS (100 ng/mL, Sigma-Aldrich) at the specified time points to secrete LCN2. Following LPS treatment for 24 hours, their medium containing LCN2 (LTM) was incubated with RAW264.7 cells for the indicated times.
## Transfection of small interfering RNA
Small interfering RNA (siRNA) targeting mouse LCN2 (Santa Cruz Biotech., Santa Cruz, CA, USA) were purchased. RAW264.7 cells were transfected with LCN2 siRNA or control scrambled siRNA using lipofectamine RNAiMAX (Invitrogen) according to the manufacturer's instructions.
## Treatment of recombinant LCN2 (rLCN2) and iron chelator deferiprone (DFP)
rLCN2 was incubated with 0.5, 1, 2, or 4 μg/mL mouse rLCN2 for 24 hours. RAW264.7 cells treated with DFP (Sigma-Aldrich) were pre-treated with 50 µM DFP for 1 hour before treatment with LTM or 2 µg/mL mouse LCN2 for 12 hours.
## Mitochondrial superoxide (MitoSOX) and Phagocytosis assay
For measurement of LCN2 mediated-mitochondrial superoxide, RAW264.7 cells were treated with 5 μM MitoSOX (Invitrogen) at 37 °C for 30 min. To determine phagocytic function of DFP or siLCN2, RAW 264.7 cells were incubated with 20 μg/mL Zymosan (Invitrogen) at 37 °C for 30 min. Nuclei were stained with DAPI (1:10,000, Invitrogen) following fixation with $4\%$ paraformaldehyde for 10 min. Fluorescent slides were visualized using a BX51-DSU microscope (Olympus).
## Statistical analysis
For statistical analyses, we used PRISM 7.0 (GraphPad Software Inc., San Diego, CA, USA). Group differences were determined using unpaired Student t-tests and two-way ANOVA followed by Tukey's post-hoc tests. All results were presented as mean ± standard error of the mean (SEM). A P-value of less than 0.05 was used to indicate statistical significance.
## LCN2 deletion attenuates acute lung inflammation in LPS-treated mice
To first determine the effect of LCN2 deletion on survival, mice were given an intratracheal injection of LPS (20 mg/kg). Eight days after LPS injection, LCN2 KO mice had a higher percent survival than LPS-treated WT mice (Figure 1A). Next, to evaluate whether LCN2 deletion affects acute lung inflammation, mice were treated with intratracheal LPS (1 mg/kg) injection. As well as increased serum LCN2 levels (Figure 1B), LCN2 proteins from supernatant and cell lysates of the BALF were significantly increased in LPS-treated WT mice compared to saline-treated WT mice (Figure 1C). As expected, lung LCN2 and its receptor 24p3R protein expressions were also elevated in LPS-treated WT mice (Figure 1D). Immunofluorescence study showed that LCN2-positive cells were significantly increased in LPS-treated WT mice (Figure 1E). Furthermore, Diff-Quik staining and immunofluorescence revealed that many Ly6G-positive neutrophils and F$\frac{4}{80}$-positive macrophages in the BALF of LPS-treated WT mice were attenuated by LCN2 deletion (Supplementary Figure 1). Using ELISA analysis, LCN2 deletion significantly inhibited the upregulation of serum and BALF IL-6 levels in LPS-treated mice (Supplementary Figure 2). To further determine whether LCN2 deletion affects the polarization of alveolar macrophages in the BALF, we performed FACS (Supplementary Figure 3). BAL cells were analyzed for Siglec F+ CD11c+ (alveolar macrophages), Siglec F+ CD86+ (M1 polarization), and Siglec F+ CD206+ (M2 polarization) (Supplementary Figure 3A). Similar to human BALF (Supplementary Table 1), the proportion of macrophages tended to be decreased in LPS-treated WT mice compared to saline-treated mice. However, the proportion of alveolar macrophage in LCN2KO mice was not changed by LPS. In particular, there was no difference between the M1 macrophage and M2 macrophage portions due to the small population of alveolar macrophages in BALF (Supplementary Figure 3B-D). Taken together, these findings indicate that LCN2-positive neutrophils and alveolar macrophages play an important role in the resolution of acute lung inflammation.
## Circulating LCN2 level is elevated in patients with pneumonia
These biologically significant findings in LPS-induced acute lung injury were validated in human patients with pneumonia. Notably, pneumonia patients had significantly higher serum LCN2 levels than control subjects (Supplementary Table 1). In addition to H&E staining, many LCN2-positive cells were significantly increased in BALF slides from pneumonia patients (Supplementary Figure 4A). As shown in supplementary table 1, pneumonia patients had considerably greater white blood cell counts, C-reactive protein levels, and procalcitonin levels than control subjects. A high rate of ($65.4\%$, $\frac{17}{26}$) of pneumonia patients progressed to ARDS due to severe inflammation. In particular, differential cell analysis in the BALF revealed a significantly higher proportion of neutrophils in patients with pneumonia compared to control subjects (Supplementary Table 1). Consistent with LPS-treated mice, serum and BALF IL-1β and IL-6 levels in pneumonia patients were higher than in controls (Supplementary Figure 4B, C). In particular, there was a significant increase of tumor necrosis factor (TNF)-α in the BALF of pneumonia patients. These results suggest that LCN2 may play a role in the regulation of acute lung inflammation in pneumonia patients.
## LCN2 deletion attenuates oxidative stress and iron accumulation in the lung of LPS-treated mice
LPS treatment produces proinflammatory cytokines and recruits neutrophils to the alveolar spaces to initiate inflammatory responses 22. In particular, persistent neutrophil accumulation produces reactive oxygen radicals and activates alveolar macrophages 23. Increased protein levels of heme oxygenase-1 (HO-1) and superoxide dismutase-2 (SOD-2) in the lung of LPS-treated WT mice were significantly reduced by LCN2 deletion (Figure 2A). In addition, many 4-hydroxynonenal (4-HNE)-positive macrophages (CD11b-positive cells) in LPS-treated WT mice were also significantly reduced by LCN2 deletion (Figure 2B, C). Similarly, in human BALF, immunofluorescence showed that HO-1- or 4-HNE-positive cells were increased in pneumonia patients compared to control subjects (Supplementary Figure 5A, B).
LCN2 not only transports secreted iron but also regulates intracellular iron concentrations 24. Inflammation and oxidative stress were induced by iron accumulation 25. So, to determine whether LCN2 affects iron overload in LPS-induced acute lung injury, we performed Perls Prussian blue iron staining (Figure 2D). Notably, we found that the induction of iron-stained alveolar macrophages in the lung section and BALF of LPS-treated WT mice was significantly reduced by LCN2 deletion (Figure 2E). Many iron-stained macrophages were also observed in the BALF of pneumonia patients (Supplementary Figure 5C).
To further determine whether LCN2-positive cells affect iron uptake in alveolar macrophages of LPS-treated mice, we conducted triple immunofluorescence with F$\frac{4}{80}$, LCN2, and ferritin (iron storage protein) antibodies. Many F$\frac{4}{80}$ and LCN2-positive macrophages were co-localized with ferritin-positive cells in LPS-treated WT mice. However, these co-localized macrophages were significantly reduced by LCN2 deletion (Figure 2F, G). Taken together, these findings suggest that LCN2 may play a critical role in the regulation of iron-mediated oxidative stress in acute lung injury.
## Recombinant LCN2 pretreatment promotes acute lung inflammation in LPS-treated mice
As expected, after rLCN2 pretreatment, LPS (20 mg/mL)-treated LCN2 KO mice exhibited a higher survival rate than LPS-treated WT mice (Figure 3A). To further test the hypothesis that LCN2 promotes acute lung inflammation, LPS (1 mg/mL)-treated mice were intraperitoneally pretreated with rLCN2 (Figure 3B). Although both serum and BALF LCN2 proteins were measured in LCN2 deficient mice, they were prominently reduced in rLCN2+LPS-treated LCN2 KO mice compared to rLCN2+LPS-treated WT mice (Figure 3C, D). Immunohistochemical study revealed that rLCN2 pretreatment increased Ly6G-positive neutrophils and F$\frac{4}{80}$-positive macrophages in lung sections of LPS-treated WT mice, but many Ly6G- and F$\frac{4}{80}$-positive cells in rLCN2+LPS-treated WT mice were significantly reduced by LCN2 deletion (Figure 3E, F). Furthermore, rLCN2 increased lung F$\frac{4}{80}$ and IL-6 protein levels in LPS-treated WT mice, whereas LCN2 deletion significantly attenuated theirs protein levels in rLCN2+LPS-treated mice (Figure 3G). These findings indicate that exogenous LCN2 can promote acute inflammation in LPS-induced acute lung injury.
## Recombinant LCN2 pretreatment promotes lung oxidative stress and iron overload in LPS-treated mice
Given that LCN2 increased iron uptake in alveolar macrophages, we evaluated whether exogenous LCN2 pretreatment affects oxidative stress in LPS-treated mice. Western blot analysis showed that rLCN2 increased lung HO-1, 4-HNE, and SOD-2 protein levels in LPS-treated WT mice, whereas LCN2 deletion significantly reduced these protein levels in rLCN2+LPS-treated mice (Figure 4A). Double immunofluorescence revealed that many co-localized HO-1 (or 4-HNE) and F/480 (or CD11b)-positive macrophages in rLCN2+LPS-treated WT mice were significantly reduced by LCN2 deletion (Supplementary Figure 6 and 7). Furthermore, rLCN2 increased the number of iron-stained AMs in lung sections and BALF of LPS-treated WT mice, whereas LCN2 deletion prominently attenuated the increased number of iron-stained macrophages in the lung section and BALF of rLCN2+LPS-treated mice (Figure 4B, C). Notably, we found that total lung iron levels were higher in rLCN2+LPS-treated WT mice than LPS-treated WT mice, and were lower in rLCN2+LPS-treated LCN2 KO mice compared to rLPS+LPS-treated WT mice (Figure 4D). Taken together, these results suggest that exogenous LCN2 may promote acute lung injury by iron accumulation-induced oxidative stress.
## Exogenous LCN2 increases proinflammatory cytokines in RAW264.7 cells
We next verified whether LPS or exogenous LCN2 affects LCN2 expression in RAW264.7 cells. As shown in figure 5A, LCN2, 24p3R, IL-6, TNF-α, iNOS, and secreted LCN2 were increased in a time-dependent manner. Consistent with LPS treatment, these proteins were elevated in RAW264.7 cells following LPS-treated medium (LTM) treatment (Figure 5B, C). Furthermore, we also found that rLCN2 increases these proteins in a dose-dependent manner (Figure 5D). To determine whether LCN2 silencing inhibits proinflammatory cytokines in LPS-treated RAW264.7 cells, we used siLCN2. siLCN2 treatment significantly reduced LPS-induced LCN2, IL-6, TNF-α, iNOS, HO-1, and secreted LCN2 protein levels in LPS-treated RAW264.7 cells (Figure 5E, F). These results indicate that LCN2 promotes the secretion of proinflammatory cytokines in activated macrophages.
## Iron chelator inhibits LCN2-induced inflammation and oxidative stress in LTM or rLCN2-treated RAW264.7 cells
To assess the protective role of iron chelator on LPS-induced acute lung injury, iron chelator DFP was intraperitoneally given to WT and LCN2 KO mice (Supplementary Figure 8A). BALF-stained analysis showed that many neutrophils and iron-stained alveolar macrophages in LPS-treated WT mice were prominently reduced by LCN2 deletion (Supplementary Figure 8B). In particular, the numbers of neutrophils and iron-stained macrophages were lower in the BALF of LPS+DFP-treated WT and LCN2 KO mice than LPS-treated WT mice. To further assess the role of iron chelator on inflammation and oxidative stress, we depleted cellular iron in RAW264.7 cells using iron chelator DFP. DFP significantly decreased LTM or rLCN2-induced LCN2, 24p3R, IL-6, TNF-α, iNOS, and medium LCN2 proteins in RAW264.7 cells (Figure 6A-D). As expected, LTM- or rLCN2 -induced HO-1, SOD-2, and 4-HNE protein levels were also prominently reduced by DFP. In particular, increased iron responsive element binding protein 1 (IRP1) and iron-storage protein ferritin levels were significantly reduced by DFP (Figure 6A-D). Furthermore, increased intensities of MitoSOX in LTM or rLCN2-treated RAW264.7 cells were prominently inhibited by DFP (Figure 6E, F). Thus, these results suggest that ion chelator inhibits inflammation and oxidative stress in activated macrophages under elevated LCN2 levels.
## Iron chelator inhibits phagocytosis in LTM or rLCN2-treated RAW264.7 cells
Zymosan has been used as a model for the recognition of microbes by immune responses 26. After inflammatory stimuli in macrophages, phagocytic receptors on macrophages bind zymosan and stimulate particle engulfment 27. So, we measured phagocytosis using zymosan assay kit. As shown in supplementary figure 9, LCN2 knockdown significantly reduced the increased number of zymosan particles in LPS-treated RAW264.7 cells. To further determine the role of iron chelator on phagocytosis, we depleted cellular iron in LTM or rLCN2-treated RAW264.7 cells using DFP. DFP significantly reduced the increased number of zymosan particles in LTM or rLCN2-treated RAW264.7 cells (Figure 7). These findings provide evidence that iron chelator can inhibit LCN2-induced iron accumulation and may play an essential role in the regulation of inflammation and oxidative stress against acute lung injury.
## Discussion
In this study, we investigated that LCN2 is an inflammatory mediator that is elevated in LPS-treated mice and in patients with pneumonia. LCN2-deleted mice with a higher survival rate after LPS treatment had reduced lung inflammation and oxidative stress compared to LPS-treated WT mice. In contrast, rLCN2 pretreatment exacerbated acute lung inflammation and oxidative stress. In particular, we found that increased iron-stained alveolar macrophages can be associated with acute lung injury. Furthermore, DFP inhibited inflammation, oxidative stress, and phagocytosis in RAW264.7 cells with high LCN2 levels. Thus, our findings suggest that LCN2 may promote acute lung injury by enhancing macrophage iron accumulation.
The role of LCN2 in inflammatory conditions remains controversial, and experimental results vary depending on the selected agent (bacteria or LPS) and administration route (intraperitoneal or intratracheal). Classically, LCN2 bound to bacterial siderophores inhibits bacterial growth by preventing the utilization of host iron 12. It has been known that LCN2 is protective against intraperitoneal infection by E. coli or *Klebsiella pneumoniae* 12, 13. Intratracheal instillation of E. coli upregulates LCN2 in the lungs, and LCN2 loss results in increased mortality of these infected mice 28. Furthermore, LCN2 deficiency alters neutrophil homeostasis, impairing neutrophil migration and function in acute lung injury model using E. coli 29. In contrast, LCN2 impairs immune responses and bacterial clearance of *Streptococcus pneumonia* and increases mortality 30. Consistent with a recent study that LCN2 silencing inhibits lung inflammation in neonatal mice 18, we showed that after intratracheal injection of LPS treatment, LCN2 deficient mice had higher survival rates rather than LPS-treated WT mice. Additionally, rLCN2 pretreatment reduced mortality in LPS-treated LCN2 KO mice. Thus, our findings indicate that LCN2 may have a detrimental role in acute inflammatory conditions following intratracheal LPS administration.
It has been reported that LCN2 is increased in a variety of cell types, including bronchial epithelial cells, type II pneumocytes, neutrophils, and macrophages 31, 32. Consistent with an increased percentage of neutrophils in the BALF from pneumonia patients (Supplementary Table 1), LPS-treated mice had an increased number of neutrophils compared to macrophages (Supplementary Figure 1 and 3). Our findings may be due to the infiltration of neutrophils through blood-air barrier breakdown by LPS. Notably, we found that LCN2-positive cells are more prominently observed in alveolar macrophages than neutrophils in the BALF of pneumonia patients. Furthermore, as shown in figure 1C, LCN2 was remarkably increased in the mature macrophage isolated from the BALF of LPS-treated WT mice. Although pro-inflammatory cytokines and LCN2-positive macrophages were increased in BALF from patients with pneumonia, we saw no significant difference in LCN2 levels in BALF samples between pneumonia and controls. Maybe, we suggest that control subjects in this study consisted of patients with stable chronic lung disease, which may affect LCN2 levels. Thus, our data suggest that LCN2-mediated activated macrophages could play an important role in the regulation of acute lung inflammation.
By scavenging iron and storing it into ferritin, alveolar macrophages, which are resident lung macrophages, play a critical role in iron sequestration 33, 34. In fact, LCN2 transports free iron into macrophages, and these irons causes oxidative stress in infectious conditions 19, 20, 35. Consistent with evidence that iron overload-induced M1 polarization increases the secretion of proinflammatory cytokines 25, we also found that LPS-treated mice had MI polarization including BALF (IL-1ß and IL-6) and iron-stained macrophages. In particular, many iron-storing ferritin-positive macrophages were observed in LCN2-positive cells in the lungs of LPS-treated WT mice. However, these findings were reversed by LCN2 deletion. Furthermore, LCN2 KO mice had low levels of total iron concentrations compared to rLCN2+LPS-treated WT mice. Notably, DFP significantly the increased numbers of neutrophils and iron-stained alveolar macrophages in LPS-treated mice. The present study supports that exogenous LCN2 including LPS, LTM, and rLCN2 upregulates proinflammatory cytokines, but DFP inhibits these increased IL-6, TNF-α, iNOS proteins in exogenous-treated RAW264.7 cells. Taken together, our results indicate that LCN2-mediated iron sequestration may be associated with acute lung inflammation.
In addition to inhibiting siderophore-dependent bacterial iron uptake, LCN2 was found to have an impact on iron homeostasis and inflammatory response. However, it is still unclear to why siderophore-independent bacteria like *Streptococcus pneumoniae* cause macrophages to secrete LCN2. Warszawska et al. postulated that increased LCN2-induced IL-10 production could deactivate macrophages, prevent efficient bacterial clearance, and exacerbate the disease in a signal transducer and activator of transcription 3 (STAT3)-dependent pathway 30. So, we suggest that LCN2 may play an important critical role in macrophage polarization in different manners in response to different pathogens such as bacteria stains or diverse conditions.
Iron overload causes oxidative stress damage in the lung from coal dust and diesel particles containing iron-containing pollutants 36. Free radicals like nitric oxide and the superoxide anion are produced by these activated alveolar macrophages, which have exacerbated acute lung damage 37, 38. In this study, LPS-induced HO-1 and SOD-2 proteins in the mouse lungs were reduced by LCN2 deletion. There were increased numbers of HO-1 and 4-HNE-positive cells in pneumonia patients. Additionally, our data support that rLCN2-induced HO-1, SOD-2, and 4-HNE in LPS-treated WT mice are significantly reduced by LCN2 deficiency. Consistent with our results, 4-HNE-induced mitochondrial ROS exacerbates M1 polarization in obese mice 39, 40. LPS-induced activation of RAW264.7 cells also increased the 4-fold activity of IRP1, which has been known as a cytoplasmic RNA-binding protein that controls iron metabolism 41. They reported that NO acts as an intercellular stimulus to increase IRP1 activity in RAW264.7 cells. So we postulated that iron chelator could inhibit oxidative stress-related proteins and ferritin in exogenous LCN2-treated RAW264.7 cells. In accordance with previous study 38, DFP pretreatment resulted in reduced HO-1, SOD-2, 4-HNE, IRP1, ferritin, and MitoSOX and inhibited phagocytosis in RAW264.7 cells with high LCN2 level conditions.
On the other hand, alveolar macrophages have phagocytic functions for the removal of damaged cells, dust, and microorganisms. Macrophage phagocytosis function is essential for the resolution phage of acute lung injury 42. Apart from pathogens, the elimination of apoptotic neutrophils is significant to the resolution of acute lung injury. Using FACS analysis, we found that LPS treatment increases apoptotic cells in the BALF of LPS-treated WT mice compared to LPS-treated LCN2 KO mice. However, our findings are inconsistent with a recent study showing that LCN2 impairs phagocytic bacterial clearance of macrophages 29, 42. Although phagocytosis is beneficial during the acute phase of bacterial infection, excessive macrophage activation during acute lung injury can exacerbate lung damage. Here, our data show that after intratracheal LPS injection, LCN2 secreted by infiltrating neutrophils reciprocally activates macrophage-mediated inflammation and oxidative stress. Zymosan assay showed that increased zymosan particles in LTM or rLCN2-treated RAW264.7 cells are significantly attenuated by DFP. These findings further indicate that enhanced phagocytic function by acute inflammation could be regulated by iron chelator. Taken together, these results suggest that inhibiting LCN2-mediated iron uptake protects against acute lung inflammation by sequestrating iron or by inhibiting iron-mediated oxidative stress in activated macrophages.
There are several limitations of our study. First, the small sample sizes of patients and controls with stable chronic lung disease may have obscured significance differences of LCN2 in BALF. Second, we used LPS as the ALI insult, so the role of LCN2 during direct pathogen infection was not evaluated, which may limit the generalizability of our findings. Third, serum and BALF samples were obtained at patient admission, so evolution over time and with treatment was not evaluated. Fourth, we only investigated alveolar macrophages, although LCN2 also plays roles in neutrophils, another important innate immune cell subset that protects against infection.
In conclusion, we demonstrated that alveolar macrophages are responsible for inflammation, oxidative stress, and phagocytosis in acute lung injury via LCN2-mediated iron overload. Our findings showed that excessive iron accumulation within alveolar macrophages contributes to M1 polarization and causes oxidative stress. In vitro study supported that iron chelator inhibits inflammation, oxidative stress, and macrophage phagocytosis under elevated LCN2 condition levels. So, the present study strongly suggests that LCN2-targeting the polarization of macrophages has potential advantages and therapeutic targets for acute lung injury or pneumonia.
## Funding
This work was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea (No. 2015R1A5A2008833 and 2021R1A2C2093913).
## Author Contributions
HSA conducted animal experiments, acquired and analyzed data, and wrote the manuscript. JWY acquired human samples, analyzed data, and wrote the manuscript. JHJ and MH acquired human samples and analyzed data. SHH, HMJ, and EAJ performed histological analyses and assisted with mouse genotyping and experiments. JL, HJS, and KEK acquired and analyzed data. MCS provided r LCN2 and critically reviewed the manuscript. GSR provided materials and reagents, designed research studies, wrote the manuscript, and procured funding.
## Data Availability Statement
Data will be made available on reasonable request.
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|
---
title: Low-intensity pulsed ultrasound promotes skeletal muscle regeneration via modulating
the inflammatory immune microenvironment
authors:
- Haocheng Qin
- Zhiwen Luo
- Yaying Sun
- Zhong He
- Beijie Qi
- Yisheng Chen
- Junlong Wang
- Ce Li
- Weiwei Lin
- Zhihua Han
- Yulian Zhu
journal: International Journal of Biological Sciences
year: 2023
pmcid: PMC10008697
doi: 10.7150/ijbs.79685
license: CC BY 4.0
---
# Low-intensity pulsed ultrasound promotes skeletal muscle regeneration via modulating the inflammatory immune microenvironment
## Abstract
Background: Low-intensity pulsed ultrasound (LIPUS, a form of mechanical stimulation) can promote skeletal muscle functional repair, but a lack of mechanistic understanding of its relationship and tissue regeneration limits progress in this field. We investigated the hypothesis that specific energy levels of LIPUS mediates skeletal muscle regeneration by modulating the inflammatory microenvironment.
Methods: To address these gaps, LIPUS irritation was applied in vivo for 5 min at two different intensities (30mW/cm2 and 60mW/cm2) in next 7 consecutive days, and the treatment begun at 24h after air drop-induced contusion injury. In vitro experiments, LIPUS irritation was applied at three different intensities (30mW/cm2, 45mW/cm2, and 60mW/cm2) for 2 times 24h after introduction of LPS in RAW264.7. Then, we comprehensively assessed the functional and histological parameters of skeletal muscle injury in mice and the phenotype shifting in macrophages through molecular biological methods and immunofluorescence analysis both in vivo and in vitro.
Results: We reported that LIPUS therapy at intensity of 60mW/cm2 exhibited the most significant differences in functional recovery of contusion-injured muscle in mice. The comprehensive functional tests and histological analysis in vivo indirectly and directly proved the effectiveness of LIPUS for muscle recovery. Through biological methods and immunofluorescence analysis both in vivo and in vitro, we found that this improvement was attributable in part to the clearance of M1 macrophages populations and the increase in M2 subtypes with the change of macrophage-mediated factors. Depletion of macrophages in vivo eliminated the therapeutic effects of LIPUS, indicating that improvement in muscle function was the result of M2-shifted macrophage polarization. Moreover, the M2-inducing effects of LIPUS were proved partially through the WNT pathway by upregulating FZD5 expression and enhancing β-catenin nuclear translocation in macrophages both in vitro and in vivo. The inhibition and augment of WNT pathway in vitro further verified our results.
Conclusion: LIPUS at intensity of 60mW/cm2 could significantly promoted skeletal muscle regeneration through shifting macrophage phenotype from M1 to M2. The ability of LIPUS to direct macrophage polarization may be a beneficial target in the clinical treatment of many injuries and inflammatory diseases.
## Introduction
Skeletal muscle injury caused by traumatic accidents can impair posture and functional movement, limiting daily activities and affecting life quality 1,2. With over $20\%$ muscle mass loss, extensive deficits need therapeutic management to support normal muscle regeneration 3. The formation and maturation of regenerating muscle fibers are critical for functional recovery, which depends on the activity of myogenic progenitor cells (MPC) or satellite cells 4. A series of immune cell infiltration and activation processes will proceed during muscle regeneration 5-7. Immune cells with specific cytokines and growth factors are involved in the clearance of damaged muscle tissue, angiogenesis, and extracellular matrix (ECM) remodeling 4,8. Therefore, it has been suggested that the emerging therapeutic approaches for muscle healing that focus on immunomodulation and immunomodulatory agents are promising in several preclinical studies. They delivered the immunomodulation medicine into injured site through direct injection 9-11. Certain materials used to fill muscle defects have been reported to modulate the immune response, thus enhancing healing 12,13. Despite the high morbidity of the donor muscle site, the surgery using autologous muscle flaps is still the standard method for patients suffering from severe muscle injuries 1,14. Therefore, an adjunctive treatment strategy without injection and invasiveness is imperative. The biological mechanisms of simple and noninvasive approaches such as the ultrasound-based method to treat skeletal muscle injuries are poorly understood, thereby limiting their clinical application 9.
Low-intensity pulsed ultrasound (LIPUS), a form of mechanical stimulation delivered through designed transducer, is widely used clinically to treat musculoskeletal soft tissue injury as an alternative and complementary medicine 15,16. Over the past few decades, there were increasing evidence showing that LIPUS therapy can reduce the expression of pro-inflammatory cytokines, limit the infiltration of inflammatory cells, and modulate the phenotype of inflammatory cells. This beneficial alteration my relate to increased blood flow, activated mitochondrial biogenesis, and anti-oxidative stress effect, thereby promoting muscle healing 17-19. Both clinical trials and preclinical research have indicated that LIPUS can reduce the inflammatory response and accelerate muscle damage recovery 20,21. However, the direct link among specific cellular and molecular components, the reduction of inflammation, and functional recovery has yet been elucidated.
In recent years, macrophage polarization has been widely studied in mediating inflammatory immune microenvironment 22-25. *In* general, acute muscle injury initially recruits "typically activated" macrophages (M1) into the pathological loci to perform phagocytosis of necrosis tissue and initiate myogenesis by producing nitric oxide (NO) and pro-inflammatory cytokines 26,27. Subsequently, "alternatively activated" M2 macrophages replace the M1 phenotype to promote muscle regeneration and differentiation 28,29,29. M2 subtype secretes anti-inflammatory factors, including IL-10 and TGF-β1, which shifts the inflammatory microenvironment to the inflammation-suppressed one 30. If the balance between M1 and M2 is broken, the inflammatory microenvironment will beyond control, leading to impaired tissue healing. According to our recent studies, the macrophage polarization balance between M1and M2 plays an essential role in muscle healing 31. The previous study has primarily proved that LIPUS affects the polarization state of macrophages in vitro, which suggested LIPUS may influence the inflammatory microenvironment through a macrophage-mediating mechanism 32,33, while it has not been clearly investigated the underlying mechanism in treating skeletal muscle injury. WNT/β-catenin signaling is essential in mediating immune microenvironment, tissue repair and regeneration 34,35. Dysregulation of β-catenin signaling is involved in persistent inflammation and organ fibrosis 36. Studies have found that WNT/β-catenin signaling promotes M2 macrophage polarization, which could further promote the resolution of inflammation 37. It was also reported that WNT/β-catenin signaling play a pivotal role in myogenesis 38. However, the role of WNT/β-catenin signaling in mediating the effect of LIPUS on inflammatory immune microenvironment in injured skeletal muscle warrants further exploration.
Several lines of evidence illustrated that LIPUS might benefit the muscle healing process 17,19,21,39. So far, the changed immune microenvironment and cellular mechanisms by which LIPUS promotes muscle healing are, however, not well understood. In the study described here, effects of LIPUS on the immune microenvironment in mouse skeletal muscle were detected and the molecular modification of macrophage polarization in an induced M1 cell model was also investigated. It was hypothesized that LIPUS enhances muscle healing by shifting the inflammatory microenvironment in a macrophage-dependent manner.
## Establishment of Mice Contusion Model
All animal experiments were conducted under the standard of the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The Fudan University Animal Care Committee authorized the experimental protocols. ( Approval no.20171248A703, KY-2018-0390) and every attempt was introduced to keep animal suffering to a minimum.
In this study, a total of 104 C57BL/6 male mice (age: 10-11 weeks; weight:25 ± 3 g) obtained from Shanghai Experimental Animal Center were kept in the animal shelter facility under controlled temperature, humidity, and light, with free access to rodent food and water, and a 12-hour light/dark cycle at Department of Laboratory Animal Science, Fudan University. 72 mice were used in the normal contusion injury model, 24 mice were used in the macrophage depletion model and 8 mice were used in the negative control group for behavioral tests. A right gastrocnemius muscle contusion was induced as the muscle injury model via the dropped-weight technique. After being anesthetized and not responding to a toe pinch, the animals' hind limbs were positioned, dorsiflexing the ankle to 90°. A 16.8 g (diameter 15.9 mm) stainless steel ball was dropped from the height of 100 cm through a tube (interior diameter of tube:16 mm) onto an impactor resting with a surface of 28.26 mm2 on the middle of the gastrocnemius muscle of the mouse. Middle of the gastrocnemius was defined as 15 mm proximal to the calcaneus when the leg was stretched. While the left gastrocnemius (Control) was neither injured nor LIPUS treated. The instantaneous force delivered by a falling object with these characteristics was calculated to equal 0.58 N·m/cm2, where 1 N·m is equal to the force of an object weighing 100 g falling over 1 m. The muscle contusion model was a high-energy blunt injury that created a large hematoma and was followed by massive muscle regeneration healing processes that are very similar to those seen in humans 31. The Creatine Kinase concentrations in the serum at 2h and 24h was assessed to reflect the muscle damage in mice. No drugs (e.g., buprenorphine, NSAIDs) were given to the mice before or after the contusion injury.
After one week of acclimatization, the mice that needed to be modeled were subjected to acute muscle contusion on the right gastrocnemius muscles, with the left gastrocnemius muscles regarded as the Control muscle sample. Then, all the injured mice were randomly divided into 3 groups with 24 mice in each, named as Contusion group ($$n = 24$$), Contusion+UltrasoundL($$n = 24$$), and Contusion+UltrasoundH ($$n = 24$$) respectively. We harvested muscle samples from mice on Days 3, 7, and 14. 8 mice were randomly selected from each group at each time point.
## Macrophage depletion
We established macrophage depletion mice according to the published procedure 31. In short, the mice were injected with 2 mg clodronate-containing liposomes (purchased at www. clodronateliposomes.com) intraperitoneally two days before the contusion. Then, on Day 0, 3, and 6 after muscle contusion, 0.5g of clodronate-containing liposomes were re-injected to keep the macrophages at a low level. The spleen of mice was dissected for tissue flow cytometry to identify the proportion of CD11b+F$\frac{4}{80}$+ cells. Many studies have proven that macrophages percentage in the spleen can verify the success of macrophages depletionas spleen can presented the station of immune system 40. Research have also demonstrated the fact that systemic depletion of macrophages by clodronate liposomes can deplete skeletal muscle macrophages 41,42. On Day 14, all mice with macrophage depletion had their muscle functional recovery and fibrosis assessed.
## Ultrasound treatment in mice
Because macrophage polarization occurs primarily in the first 7 days after injury 43, mice sacrificed on Day 3 had daily treatment until Day 3, and mice sacrificed on Days 7 and 14 had daily treatment until Day 7. We applied a commercially available ultrasound gel (Jinya, China) as a coupling agent. During therapy, mice were stabilized using a restraint cage while their hind limbs were manually fixed, and the gastrocnemius area of all animals was depilated before the treatment. Before we start the research, the ultrasound equipment (Chattanooga 2776, USA) was calibrated by the Bioengineering department of Fudan University to ensure the stability and accuracy of output parameters. The treatment point was same as the drop point and the treatment begun at 24h after contusion injury. Experimental parameters are as followed. LIPUS was administered at 1 MHz frequency with a $20\%$ duty cycle for 5 minutes using a transducer with a 2 cm2 effective radiating area. The intensity of the Contusion+UltrasoundL and Contusion+UltrasoundH groups were 0.3 W/cm2 and 0.6 W/cm2 SATP (spatial average temporal peak), respectively, while the mice in the Contusion group receive the same time treatment but with the power turned off. Notably, the $20\%$ duty cycle (2 ms on, 8 ms off) and 2 cm2 effective radiating area corresponded to 30mW/cm2 and 60mW/cm2 SATA (spatial average temporal average). The procedure of the in vivo treatment was shown in Figure 1B, Figure S2A, and Sup Movie 1.
## Cell culture and cell intervention
RAW264.7 (mouse leukemia cells of monocyte-macrophage) were purchased from the American Type Culture Collection and constantly maintained in high glucose Dulbecco's modified eagle medium (DMEM) (HyClone) with $10\%$ fetal bovine serum (FBS) and 0.5 ml of penicillin/streptomycin solution (#0503; ScienCell Research Laboratories). All cells were kept in an incubator at 37℃ and $5\%$ CO2. The third passage of the RAW264.7 cells were used for the in vitro experiments. The cells were grown for 3 days with periodic medium changes and then seeded in 6‐well tissue culture plates. Then, 500 ng/ml lipopolysaccharides (LPS) were added to every plate to simulate the inflammatory environment (promoting M1 polarization) according to our previous study 31. Besides, the concentration we used depended on drug titration curve with cell viability shown in Fig. S7C. Six hours later, LPS was discarded and washed with PBS.
The cells were divided into four groups: LPS group, LPS+UltrasoundL group, LPS+ULtrasoundM, and LPS+UltrasoundH group. Cells were LIPUS treated 24 and 48 hours after LPS was added to the culture media. Cell culture supernatant from the treated cells was collected 24 hours after the second LIPUS treatment (i.e., 72 hours after the addition of LPS) for further analysis (The details of the cell experiment are shown in Fig. 1A). The treatment parameters were the same as in mice experiments and the medium dose was set to 45mW/cm2. Through a coupling gel applied between the ultrasonic transducer and the plate, sound energy was passed through the bottom of the plate. The acreage of the ultrasonic transducer is the same as that of a single hole in the six-well plate. When the silence of Wnt/β-catenin signaling was required, the RAW264.7 cells were treated with XAV-939 (Wnt/β-catenin signaling inhibitor) 44 at the final concentration of 10 μM for 24h. XAV-939 stimulate β-catenin degradation through stabilizing axin 45. If the activation of WNT/signaling was required, the RAW 264.7 was treated with QS11 (Wnt/β-catenin signaling agonist) 46 at the final concentration of 10 μM for 24h treatment. The concentration we used depended on drug titration (XAV-939 and QS11) curve with cell viability shown in Fig. S10.
## Animal Sample Harvest and Analysis
Bilateral gastrocnemius muscles were dissected after the mice were euthanized. Following the isolated bilateral muscles photographed, immediately the wet weight of the muscles on both sides was measured and the ratio of the injured side to the uninjured side was calculated.
Then, strain gauge transducers coupled with a TBM4 strain gauge amplifier (World Precision Instruments Inc., Sarasota, USA) were used to test the passive force of isolated muscles from Day 14. First, we separated the lower limbs of the mice. Then, the calf bone was cut down the middle. Next, the Two bones attached to the gastrocnemius tendon were used as anchors for the test machine. A data program was used to analyze the strength (Windaq; DATAQ Instruments Inc., Akron, USA). The data was further normalized with the wet weight.
Next, the bilateral muscles were washed immediately with saline and then fixed in the $10\%$ buffered neutral formalin solution. After 24 h, muscles were successively dehydrated in $70\%$, $80\%$ (2×), $95\%$ (2×), and $100\%$ (3×) ethanol for 30min per step. Subsequently, they were processed with xylene (3×) for 20min per step and embedded in paraffin. Next, the injured areas of embedded muscles were cut with a paraffin slicing machine (LEICA RM2235) into 5 µm thick sections (Perpendicular to the direction of muscle fibers) for routine Hematoxylin and eosin (HE) stain, Masson stain (for fibrosis analysis), and immunofluorescence stain (for fibrosis protein).
## HE staining
Firstly, the paraffin sections from Day 3 were deparaffinized by the oven (65℃ 30min), xylene (2× 10min), and gradient alcohol (10 min per time) successively. Hematoxylin and eosin-stained were performed on the tissue separately for 10 min and 1 min. Finally, the tissue was sealed with neutral resin and observed under the microscope directly (ECHO Revolve, American). The same HE staining was performed on muscle obtained from macrophage-depleted mice on Day14. The Cross-Sectional areas (CSA) and Cellularity of muscle fibers were calculated using ImageJ software. Besides, Centronucleated cells were considered to be regenerating myofibers. Therefore, manual recording of newly produced muscle fibers was also calculated 17. The source of the samples was masked from the observer conducting the counting. All histological statistics were calculated with 4 samples included in each group. 4 unbiased (*200 magnification) images were selected from 3 section in each muscle sample.
## Masson staining
Paraffin sections from time points Day 7 and Day 14 were subjected to Masson staining. The processes for deparaffinization and rehydration were the same as for HE stains. The entire process was carried out according to the commercial kit for Masson staining (Solarbio, China) 47. The same Masson staining was performed on muscle obtained from macrophage-depleted mice. The fibrosis condition was observed and recorded by the microscope (ECHO Revolve, American). The fibrotic areas and CSA were calculated with 3 sections from 4 samples in each group. 4 unbiased (*200 magnification) images were randomly selected for targeted section. The source of the samples was masked from the observer conducting the counting.
## Immunofluorescence analysis
Paraffin sections from Day 3, and Day 14 were used for Immunofluorescence analysis. As described above, the processes for deparaffinization and rehydration were the same as for HE staining. Then, the water surrounding the tissues was cleaned up, and a fluorescent pen was used to draw circles around the tissues on each slide. Then, $5\%$ BSA and $0.5\%$ Triton-X-100 (Solarbio, Beijing, China) were used to block the tissue at RT for 1h and diluted primary antibodies were used to incubate with muscle overnight at 4°C. On the second day, 1× PBST washing for 5 min three times was performed before and after incubation with Alexa Fluor 488 anti-mouse (H+L) secondary antibodies (1:500; Life Technologies, USA) for 1 hour at RT. DAPI was used to locate nuclei. As for cell immunofluorescence, the DMEM was removed in the first step. Then after being rinsed with PBS, cells were fixed in $4\%$ PFA for 10 min and were counterstained cytoskeleton with Phalloidin-594 for 30 min. In the procedures of permeabilization and blockage, $5\%$ BSA with $0.5\%$ Triton-X-100 was used for 70 min at RT. Next, immunofluorescence was incubated with primary antibodies incubation at 4°C overnight (1:100 dilution for β-catenin [ab32572; Abcam]. 1:200 dilution for iNOS [AF0199; Affinity], CD86 [ab64693; Abcam], CD206 [ab64693; Abcam], CD68[ab955; Abcam]. 1:500 dilution for FZD5 [ab7523; Abcam], F$\frac{4}{80}$[ab90247; Abcam].) and then with the corresponding secondary antibody for 1 hour at RT. Finally, DAPI counterstained the nuclei. Images were observed by a fluorescence microscope (ECHO Revolve, America). All images taken used the same microscope settings (e.g., exposure time, laser intensity, and gain).
## Flow cytometry for M1/M2 in vitro and macrophages in vivo
The Cytomics™ FC 500 (Beckman Coulter) was used to calculate the targeted cells by the surface markers of macrophages, including anti-CD86-PE, anti-CD163-APC, anti-CD11b-FITC, anti-F$\frac{4}{80}$ APC, and anti-F$\frac{4}{80}$-FITC (Thermo/eBio) 28,48. Firstly, macrophages were collected with flow cytometry staining buffer (eBioscience). After adding 2ul of antibody to every 100ul of cell suspension and incubating for 60 min at 4°C in the dark, 5 mL staining buffer was added into each tube and the cell suspension was centrifuged for 5 min (500 × g, 4 ℃). Followed by three times of repeated washing procedures, the cell was re-suspended in 200 μL PBS for final flow cytometry analysis. In experiment of validation for macrophages depletion in mice, the spleens from the mice treated with clodronate-containing liposomes were surgically removed on Days 1, 3, and 7 after injection. Spleen from control was also collected. Dispase, Collagenase, and trypsin were used to digest the tissue matrix and isolate the cells. The cell suspension was used for final flow cytometry analysis 31.
## Water maze
The water maze was utilized to acquire the swim speed of mice, which was regarded as an indicator of recovery of locomotion ability 49. In brief, when the mice were put into the water, the timing was started, and the distance the mice swam was recorded for the next 60 seconds to obtain the average speed of movement (Fig. 2D). The swimming patterns were recorded by the Video Tracking System (ActualTrack™).
## Treadmill test
Exercise capacity was also determined using a treadmill (Anhui Zhenghua Biologic Apparatus Facilities, China) running tests 50. The mice were acclimated to the treadmill for 10 min with a slope of $5\%$ and speed of 10m/min for 2 days before the test. 6 shocks in 30 seconds were defined as fatigue and the intensity of the shock is set to 0.6mA 51. The initial speed of the platform is set at 10 m/min. Before the experiment, all mice were allowed to jog at this speed for 2 min. After that, the platform accelerated at 1 (m/min)/s until it reached 25m/min (Fig. 2I). After each test, the mice were given a 30-min rest period to minimize the effect of different test running time of each mouse, and the three tests were completed in the same day. Four mice from each group were randomly selected in this experiment.
## Catwalk test
CatWalk XT™ system (Noldus Information Technology, Netherlands), a fully automated and highly sensitive instrument for assessing voluntary movement and gait, was applied for gait analysis of mice (Fig. 2K). 4 mice in each group were randomly selected for the experiment on Day 14. Similar to a human clinical gait test, the system allows rodents to move autonomously within a restricted detection channel. A green LED light was emitted into the glass board, and high-speed digital cameras 30 cm away recorded the paw prints in real-time refracted by every contact with the glass. The intensity of the reflected light is proportional to the pressure placed on the glass. Before each testing session, mice were habituated to the testing room for 30 min and then mice were acclimated to the tunnel for 5 entire passes. The system was cleaned between each mouse tested. For each animal, three successful runs were acquired and recorded. We defined successful runs as spending longer than 2.0 s, but shorter than 10.0 s (5 gait cycles on average), with a maximum allowed speed variation ≤ $40\%$. Runs were rejected if the animal turned around.
## Rotarod test
Animals were briefly pre-trained on an automated 5-lane rotarod unit (Rotarod for mice; Ugo Basile, Italy, 3 cm diameter drums which are suitably machined to provide grip. Five flanges divide the five 5.7cm lanes, enabling five mice to be simultaneously on test.) that could be set on fixed speed or accelerating speed. The mice were trained for two days with five attempts each day with a rest of 20 min. 4 mice in each group were randomly selected from each group and then placed on a rod that accelerated smoothly from 5 to 40 rpm over a period of 5 min. Each mouse was tested three times with a rest of 20 min between each trial. The length of time that each animal was able to stay on the rod was recorded as the latency to fall, registered automatically by a trip switch under the floor of each rotating drum (Fig. 2G). If the mice were out of control and made three passive turns, the timing was also stopped.
## Real-time quantitative PCR (qPCR)
RNA expression of both in vivo and in vitro experiments was analyzed as previously reported 48. Total RNA was isolated by the Trizol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. RNA sample quantities were verified using a Nanodrop 2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, United States) and the excessive concentration of RNA was diluted in appropriate proportions to achieve a final concentration of 200ng/μl. RNA was determined to be of good quality based on A260/A280 values (>1.8). RNA was then reversely transcribed by the PrimeScript RT reagent kit (Takara Bio). The operation was performed on the ABI7900 Real-Time PCR System (Applied Biosystems) with gene-specific primers listed in the table (Table 1). All samples were run in triplicate. The 2-ΔΔCT approach was used to determine the relative changes in gene expression of CD86, iNOS, CD206, and Arg1, which were finally normalized against GAPDH and Control samples.
The primers for PCR were as follows:CD86 forward, 5'-TGACCGTTGTGTGTGTTCTGGA-3', CD86 reverse, 5'-TCTCTGTCAGCGTTACTATCCCG3', CD206 forward, 5'GCTGGCGAGCATCAAGAGTA-3', CD206 reverse, 5'-AGGAAACGGGAGAACCATCAC-3', Arg1 forward, 5'-CATATCTGCCAAAGACATCGTG-3', Arg1 reverse, 5'-GACATCAAAGCTCAGGTGAATC-3' iNOSforward, 5'-GGGCTGTCACGGAGATCAATG-3'iNOSreverse, 5'-GCCCGGTACTCATTCTGCATG-3', GAPDH forward, 5'-CCTCGTCCCGTAGACAAAATG-3', GAPDH reverse, 5'-TGAGGTCAATGAAGGGGTCGT-3'.
## Enzyme-linked immunosorbent assay (ELISA)
The ELISA kits purchased from Laizee (LEM060-2, LEM100-2, LEM822-2, LEM810-2) were used to analyze the changes in inflammatory cytokines of cell experiments. After culture-supernatants were collected and concentrations of inflammatory cytokines IL-10, IL-6, IL-1α, and TNF-α were measured according to the manufacturer's instructions, respectively.
## Protein preparation and western blotting analysis
Among the mice sacrificed on Day 3 and Day 7, we randomly selected 4 mice from each group for protein analysis. The gastrocnemius muscle on the injured side of each mouse was dissected and collected. Tissue samples were lysed with protein extracts containing RIPA lysis buffer (Beyotime, China) and protease inhibitors (Beyotime, China). Protein was extracted from RAW264.7 also using RIPA lysis buffer (Beyotime, China). Protein from both sources was quantified using the Bicinchoninic Acid (BCA) Protein Assay Kit (Pierce, Appleton, WI, USA). Final protein loading concentration was controlled to 2.5µg/µL. When protein analysis of nuclear translocation was needed, Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, China) was used to separate the nuclear protein and cytoplasmic protein in cells of tissues and macrophages, and the whole operation was conducted according to the manufacturer's instruction. The next procedures for the protein analysis are the same. 10 % sodium dodecyl sulfate-polyacrylamide gel electrophoresis (10 % SDS-PAGE) was used to separate an equal quantity of protein, which was subsequently deposited onto PVDF membranes (Millipore, Billerica, MA, USA) at 400mA for 1 h with a cold pack. After that, the membranes were incubated for 1 hour at room temperature (RT) in a 5 percent bovine serum albumin blocking solution. Next, the primary antibody was incubated overnight at 4℃ with an appropriate primary antibody (1:1000 dilution for iNOS [AF0199; Affinity], Arg1[DF6657; Affinity], CD86 [ab64693; Abcam], β-catenin [ab32572; Abcam], FZD5 [ab75234; Abcam]. 1:2000 dilution for LaminB1[ab16048; Abcam], CD206 [ab64693; Abcam]. 1:5000 dilution for GAPDH [ab8245; Abcam], β-Tubulin [ab52623; Abcam]). The next day, the secondary antibody was incubated at RT for 1h after 3 required washing procedures. Finally, the ECL luminescence solution was used to expose the target protein (Biosharp, China). Anti-CD86, anti-CD206, anti-Arginase 1, anti-iNOS, anti-β-Tubulin, anti-FZD5 anti-β-catenin, anti-GAPDH, and anti-laminB1 were used as primary antibodies (Table 2). Each group at different points contained 4 protein samples for calculation ($$n = 4$$/group).
## Laser speckle contrast analysis (LASCA)
Real-time blood perfusion to the gastrocnemius muscle was measured with a blood perfusion imager (PeriCam PSI System, Perimed AB, Stockholm, Sweden) based on Laser Speckle Contrast Analysis (LASCA) technology. Firstly, to facilitate measurement and remove the effect of fur, the mice were anesthetized and shaved at the aimed area. Then, mice were placed in a supine position with the camera aimed at the medial side of the gastrocnemius muscle. A constant-temperature plate was used to keep the mice warm. When the body temperature reached 37±0.5 ℃, we recorded the average distribution of blood perfusion for 2 min in real-time by PSI scanning. We used a 40 mm2 circle to determine the exact location of the muscle in each picture so that accuracy of the results can be improved. The software helped us automatically output the average perfusion amount.
## Mouse Inflammation Antibody Array
Mouse Inflammation Antibody Array (ab133999, Abcam) with 40 inflammatory targets was used to evaluate the comprehensive effect of ultrasound on the inflammatory microenvironment after muscle injury in mice. The entire experiment operation process strictly follows the manufacturer's instructions. Briefly, Antibody array membranes were blocked for 1 hour in 2 ml of blocking solution, then incubated overnight at 4 °C with 2 mL of samples and antibody mixtures. After discarding the samples, three washing procedures were performed at RT. Following the washing, the membranes were then incubated in 1:1000 diluted streptavidin-horseradish peroxidase at RT for 1h. Before the Chemiluminescent detection, membranes were washed thoroughly. Finally, the membranes were imaged.
## Cell counting kit-8 (CCK-8)
Cell counting kit-8 assay (CCK-8, Beyotime Biotechnology, Shanghai, China) was performed to evaluate the viabilities of RAW macrophages after different interventions 28. After treatment of ultrasound on a 6-well plate, the RAW 264.7 cells (1×103 cells/well) were seeded in a 96-well culture plate. 4 replicate wells were set in each group. 2 μl of CCK8 reagent (Bio-Rad, Hercules, CA, USA) was added to each well, incubated at 37° C for 2 h. The absorbance was measured at 450 nm.
## Statistical analysis
All experiments were performed three technical replicates. Data were analyzed with GraphPad Prism 9.0 (GraphPad Software, La Jolla, USA) and were presented by mean ± SD. Significance was typically analyzed by Student's t-test, one-way ANOVA followed by post hoc LSD test, and two-way ANOVA followed by multiple t-tests. $P \leq 0.05$ was regarded as significant.
## Experimental model for mouse contusion injury and LIPUS setting for mouse/macrophage
After modeling, the skin of the targeted tissue was intact, and no fracture was found on the tibia and fibula bones. The introduction of a repeatable contusion-induced device ensured the standardization of the mouse gastrocnemius contusion model. Creatine kinase (CK) expression measurement was randomly administrated in 4 mice before, 2h after, and 24h after operation respectively and the serum index showed that it raised 8 to 12-fold at 2h post-injury and restore 24h later, which indicated the success of our model (Fig. S1).
In the preliminary experiment, we mainly explored the LIPUS stimulus dose. With other parameters unchanged ($20\%$ duty cycle, 1MHz, 5min), 30 and 60 (mW/cm2) output energy could significantly reduce the fibrosis of muscle tissue compared to 10, 20, and 90 (mW/cm2) (Fig. S7C-D). Combined with the other articles, these two output energies were included as the only variables. After conversion, the intensity was from $\frac{1}{3}$ to three-folds of the clinical dose.
As for LIPUS intensity for cell experiments, the CCK8 assays were utilized to evaluate the effect of LIPUS on cell activity. The result suggested the intensity above 75 (mW/cm2) attenuated the activity of RAW 264.7 cells (Fig. S7A). Referring to parameters in vivo, the treatment protocols in vitro were finally decided to perform with gradient intensity of 30, 45, 60 (mW/cm2).
The reason why we chose Day 3 as our first observation time point is that the polarization of macrophages reached its peak 72h after injury 52. So, the first 72h is the critical period for intervention strategy to regulate macrophage polarization. Accordingly, the inflammatory activity basically ended around the Day 7, so the Day 7 was chosen to observe the final inflammation status of the injury site 43. Besides, according to our experience in previous experiments, by the Day 14, the fibrosis of muscle injury has been basically formed. Therefore, we chose day 14 as our final observation point.
## LIPUS improves functional performance in injured skeletal muscle
We first analyzed the hematoma of injured gastrocnemius muscle collected from Day 3 in different groups. In an intuitive aspect, the serious hematoma in the Contusion group was observed, while the condition was mitigated in the LIPUS-intervened groups. At the following time points, the muscle samples collected in the Contusion group exhibited atrophy, while LIPUS prevented this pathological transformation, rendering muscles fuller (Fig. 2A). To further explore the potentially affected properties of skeletal muscle brought by ultrasound, wet-weight measurement, which is one of the indications of edema, was performed. Results showed an increasing trend in the Contusion group on Day 3, when normalized to the contralateral side and compared to other groups, which indicated ultrasound could relieve early edema. In addition, on day 14, a significant increase in muscle wet was observed in the high-level ultrasound treatment group compared with the contusion group which suggested that ultrasound can prevent atrophy caused by traumatic injury (Fig. 2B). The benign effect of LIPUS in the acute stage and weight returned in the subacute stage suggested its modulation in inflammation retreating and muscle regeneration. In addition, isolated muscles from the Contusion+UltrasoundH groups possessed better passive mechanical properties (Fig. 2C).
In order to dig and comprehend the effect of LIPUS on recovery of muscle function deeply, we conducted several commonly used behavioral experiments on day 14 (Fig D, G, I, K display the behavioral test included). In the water maze test, the treatment session considerably improved the swimming patterns and speed in Contusion+UltrasoundH groups (Fig. 2E-F and Sup Movie 2), which revealed a fact that the recovery of locomotion ability was improved by LIPUS. The two tests-the Rotarod test and the Treadmill test represented coordination and motor persistence in mice respectively. Experimental results also indicated the injury-amelioration following LIPUS, which was more pronounced in the Contusion+UltrasoundH group (Figure G-H and I-J, presented in Sup Movies 4 and 5). Last, gait analyses of voluntary locomotion of mice were recorded via the CatWalk XT system. The 3D pressure distribution images and green intensity paw print exported from the system showed a visible improvement in paw print intensity in the Contusion+UltrasoundH group (Fig. 2 L-M), which was consistent with subsequent paw print mean intensity analysis (Fig. 2N). Besides, Contusion+UltrasoundH group exhibited significantly improved performance on three other gait parameters including stand duration, paw print area, and swing duration, while paw print area and swing duration was also significantly improved in Contusion+UltrasoundL group, when compared to the Contusion group. ( Fig. 2N, shown in Sup Movie 3, detailed gait patterns were shown in Fig. S2B). These findings suggested that a specific range of therapeutic ultrasound (Contusion+UltrasoundH) applied on early-stage comprehensively promoted functional recovery of severely impaired muscle, including increased wet weight, passive muscle mechanics, and functional assessments.
## LIPUS improved skeletal muscle recovery in the histological aspect
The subsequent experiments were utilized to further explore the improvement result from ultrasound in the histological aspect. By comparing the blood perfusion in the gastrocnemius region among three groups, LIPUS was found to lead to more physiological blood perfusion on Day 14 (Fig. 3A-B, the representative parameters of the Contusion group were shown in Fig. S4A-B), which, to some extent, hints association between LIPUS and angiogenesis. By analyzing HE staining of muscles harvested on Day 3, we found that the cell infiltration was increased in the Contusion group, accompanied by a large area of necrotic muscle fibers, while the situation was alleviated in the Contusion+UltrasoundL and Contusion+UltrasoundH group (Fig. 3C-D). By analyzing Masson staining of muscles harvested on Day 7 and Day 14, we observed the massive interstitial fibrosis in the Contusion group, which, however, turned out to be more mitigatory in Contusion+UltrasoundL and Contusion+UltrasoundH group (Fig. 3E, 3F, 3H, 3I, and S3). The CSA in Contusion+UltrasoundL and Contusion+UltrasoundH group was also significantly improved on Day 7 (Fig. 3G). However Only CSA in Contusion+UltrasoundH group was significantly improved. These conclusions versatilely confirmed the therapeutic effect of LIPUS on impaired muscle in different periods and Contusion+UltrasoundH exhibited better effect in blood perfusion recovery, fibrosis reversal, decreased cellularity infiltration, and muscle regeneration.
## LIPUS modulates the inflammatory immune microenvironment through shifting macrophage polarization
To determine the specific microenvironment modulation evoked by LIPUS, an inflammation array was applied to investigate its impact on the acute phase of muscle injury, and a total of 44 pro-inflammation-associated factors were examined (Fig. 4A). The array test assisted us to seek out 7 significantly down-regulated factors (Fig. 4B).
Factors did not decrease significantly shown in Fig. S5. These down-regulated 7 factors consisted of 5 inflammatory chemotactic factors, including MIP1γ, LIX, TCA-3, IL-13, and GM-CSF, as well as an inflammatory receptor, sTNFRI, and a macrophage M1 polarization-promoting factor-IL-1α. Therefore, we hypothesized that LIPUS-mediated regulation of the immune microenvironment may be associated with macrophage status. Then, macrophage polarization-related parameters of two types of macrophages were confirmed in injured muscle collected on Day 3 and Day 7 after daily LIPUS intervention. After analyzing the polarization-associated proteins on Day 3, the data revealed that the M2-related proteins (Arg1 and CD206) in Contusion+UltrasoundL and Contusion+UltrasoundH group were significantly higher than those in the Contusion group, while only the Contusion+UltrasoundH group significantly down-regulated M1-related proteins (CD86 and iNOS) (Fig. 4C-D). The proteins from Day 7 revealed that M2-related proteins (Arg1 and CD206) were significantly increased in Contusion+UltrasoundH group, while the proteins expression of iNOS was significantly decreased in both Contusion+UltrasoundL and Contusion+UltrasoundH group, and the proteins expression of CD86 was only decresed in Contusion+UltrasoundH group. *The* genes expression analysis on Day 3 collected muscle revealed that Contusion+UltrasoundH group not only inhibited the genes expression of CD86 and iNOS but also promoted the genes expression of Arg1 and CD206. Contusion+UltrasoundL group suppressed the genes expression iNOS and activated the genes expression of Arg1(Fig. 4E). In addition, immunofluorescence staining of muscle tissue harvested on Day 3 presented the same results which are that Contusion+UltrasoundH group reduced the recruitment of macrophages in tissue and promoted the arrived macrophages to an anti-inflammatory state (more CD206 positive cells were presented in the ultrasound-treated group) (Fig. 4F-J).
To further confirm the pivotal role of macrophages in LIPUS-mediated skeletal muscle repair, a macrophages depletion model was built. Flow cytometry results of tissue (CD11b+F$\frac{4}{80}$+ cells were significantly reduced) demonstrated that the content of macrophages in spleen was significantly reduced for the first 3 days with clodronate liposomes injection, and the effects lasted for 7 days long (Fig. 5A). After the effectiveness of the depletion was determined and the contusion model was established, LIPUS was performed for 7 consecutive days. The LIPUS-mediated positive effect on muscle recovery disappeared when analyzed the mice on Day 14. From the behavioral data measured on Day 14, gait and blood perfusion remained unchanged when groups compared with each other (Fig. 5B-E). Besides, the muscle fibers were still as disorganized as the Contusion group, no obvious restoration of CSA and new fibers was observed (Fig. 5F-H). Similarly, the fibrotic area between the Contusion and Contusion+UltrasoundH groups was not considerably different (Fig. 5I-J and Fig. S6). Thus, the macrophages depletion experiments suggested that delaying the macrophage response impairs muscle recovery as shown by Figure 5C, 5E, 5G, 5H, and 5J.
*In* general, we concluded that the inflammatory microenvironment in injured skeletal muscle can be shifted by LIPUS treatment, and the retreat of inflammation was achieved by the transformation of macrophages from the pro-inflammatory type to the anti-inflammatory type. *In* general, during the acute inflammatory phase after contusion injury, macrophages are required for LIPUS treatment to beneficially impact muscle regenerative capability by polarizing them toward an anti-inflammatory (M2) phenotype.
## LIPUS promotes M2 polarization via the WNT signaling pathway in vitro
To further figure out the connection between specific cellular and molecular components of reduced inflammation by LIPUS, LPS was utilized to induce polarization of macrophages towards M1 in vitro, which could simulate the early inflammatory microenvironment caused by contusion injury in vivo (The operation process of ultrasound for cells is shown in Supplementary Movie 6). Flow cytometry analysis showed that LPS polarized at least ~$30\%$ of macrophages toward M1, and the cell morphology directly reflected the induction success (Fig. 6A-B, Fig. S8). Additionally, Increased expression of pro-inflammatory genes, proteins, and cytokines proved LPS-induced activation of macrophage-associated pro-inflammatory pathways at three levels, including iNOS, CD86, TNF-α, IL-1α, and IL-6. The above results verified that LPS-induced inflammation conforms to the early inflammatory microenvironment after skeletal muscle injury. LPS as a classic method simulating the inflammatory environment in vitro has been widely reported in research. Through three different intensifications of standardized intervention, the amount of M1 macrophage presented a downward trend, and the decline was most apparent when a high dose was conducted (about $15\%$). Meanwhile, the proportion of M2 macrophages was significantly up-regulated after reaching a certain range of stimulation intensity (approximately $20\%$) (Fig. 6A-B). The PCR, WB, and immunofluorescence analysis also verified these LIPUS-regulated cytological effects. That is, genes and proteins of M2 (CD206 and Arg1) increased, while those of M1 (CD86 and iNOS) decreased (Fig. 6C, E, F). The same is compliant for secreted cytokines (TNF-α, IL-6, IL-1α, and IL-10), with a decrease in the expression and distribution of pro-inflammatory factors (iNOS) (Fig. 6D, G, H). However, it can be observed that the regulation effect of LPS+UltrasoundL group and LPS+UltrasoundM group on LPS-induced inflammatory microenvironment is not ideal compared with LPS+UltrasoundH group.
The potential molecular mechanism of macrophages alteration raised by LIPUS is still unclear. After exploration and screening, we found that the WNT pathway, a classical pathway regulating the polarization of macrophages, figured prominently during this process. Before exploring the effect of LIPUS on this pathway, it is reported that both FZD1 and FZD5 receptors may be involved in the process of macrophage polarization in Frizzled (FZD) family. After analyzing the effect of LIPUS on the expression of both receptors in vitro, we found that FZD5 was significantly increased, while the expression of FZD1 was not significantly altered (Fig. S9). Then, we investigated the expression of FZD5 and nuclear β-catenin through WB, which are the receptor and downstream mediator of WNT signaling respectively. In the treatment groups, both proteins were significantly up-regulated (Fig. 7A-B). From the immunofluorescence analysis, with the expression of FZD5 increased after intervention, the number of β-catenin nuclear entries was spatially increased, histologically proving that LIPUS activated the WNT pathway (Fig. 7C-D). The conclusion can be established through the above results that LIPUS promoted M2 polarization but reduced M1 polarization via activating the WNT signaling pathway, subsequently reducing the secretion of inflammatory factors, and modulating the whole inflammatory microenvironment.
In order to ascertain the participation of the WNT pathway in LIPUS-mediated M2 polarization, the respective combination of LIPUS with XAV-939 (WNT/β-Catenin signaling inhibitors) 53 and LIPUS with QS11 (WNT/β-Catenin signaling agonists) 46 was used to treat the LPS-induced macrophages. The results showed that β-catenin entering the nucleus and the therapeutic effect of ultrasound on RAW 264.7 were significantly impeded when the XAV-939 existed (Fig. 7E-F). A synergistic increase was observed when both QS11 and LIPUS were involved. The result of the WNT pathway-related gene analysis (Arg1, iNOS, CD206, and CD86) was consistent with immunofluorescence, which was that XAV-939 blocked the LIPUS-medicated effect while QS11 promoted the effect (Fig. 7G).
## LIPUS promotes M2 polarization via WNT signaling pathway in vivo
In order to confirm whether the WNT pathway also activated after LIPUS treatment in vivo, we further detected the protein expression of FZD5 and β-catenin in LIPUS-treated mice. The WB results from mice on Day 3 with LIPUS treatment showed an increase in expression of FZD5 and nucleated β-catenin, especially in the Contusion+ultrasoundH group (Fig. 8A-B). In addition, immunofluorescence was used to observe the spatial expression of FZD5 and nucleated β-catenin in macrophages on Day 3 and the images showed that the WNT signaling pathway in macrophages was indeed activated after high-dose LIPUS treatment, which was consistent with the results of WB and above cell experiments (Fig. 8C-D).
The results above indicated that LIPUS modulated M1 and M2 macrophages polarization by activating WNT signaling pathway, subsequently changing the expression of inflammation-related mRNA, proteins, and cytokines from in vitro., respectively. These molecular changes ultimately promoted the reversion of the inflammatory microenvironment. Excessive inflammatory microenvironment remission eventually results in angiogenesis, inhibits fibrosis, improves myogenesis, and promotes functional recovery (Fig. 9).
## Discussion
In this study, it has been thoroughly revealed that LIPUS treatment could promote muscle healing elicited by contusion via modulating the inflammatory immune microenvironment. The multiple results of all-inclusive functional tests indirectly proved the functional recovery of the injured muscle after LIPUS treatment. Meanwhile, Muscle mechanical testing directly proved therapeutic effects of LIPUS. According to in vivo and in vitro explorations, we proposed that the beneficial effects of LIPUS treatment relied on the regulation of macrophage polarization via mechanical activation of WNT signaling.
The spatio-temporal balance between M1 and M2 macrophages is of great significance for orchestrating the inflammatory immune microenvironment after severe muscle injury 51,54. Disturbing macrophages polarization not only extends inflammation but also leads to the formation of fibrosis, which has considerable impacts on skeletal muscle function and can increase the risk of secondary injury 54-56. Therefore, an adjuvant treatment necessary is required to reach an appropriate muscle healing. The therapeutic ultrasound technique was first proposed by Corradi et al. 57 and its effectivity in tissues repairs e.g. bone fractures, stroke, chronic prostatitis, tendon healing, ligament healing, inter-vertebral disc resorption, and cartilage recovery has been proved 58,59. Although LIPUS was first used by David Lindsay in skeletal muscle injuries in 1990 for athletic muscle injury 60, its biological mechanisms have not yet been fully elucidated, with known biomechanical mechanism such as stimulation of mechanically sensitive membrane surface receptors 6162, modulation of conformation state of ion channels 61,63, and changes of membrane capacitance 64. To date, many studies have attempted to elucidate the underlying mechanisms involved in the therapeutic effects of LIPUS on skeletal muscle injury in basic animal models. For example, Chongsatientam and Yimlamai found that LIPUS could hastened muscle recovery by upregulating angiogenesis in a rat model of gastrocnemius contusion injury 65. Negata et al. discovered that LIPUS modulated the inflammatory response, increased activated satellite cells expressing Pax7, and up-regulated the myogenic regulatory factor in a mice model of Cardiotoxin-induced muscle injury 66. Silveira et al. detected that LIPUS can alleviated the oxidative stress to improve muscle healing in a rat model of gastrocnemius contusion injury 67. Without exception they failed to assess the functional recovery of skeletal muscle-related indicators comprehensively enough, nor had they explored the mechanisms in depth.
In recent studies regarding the therapeutic mechanism of LIPUS in other clinical models, deeper biological mechanisms of LIPUS have been explored. Li et al proved that ultrasound could control anti-inflammatory polarization of microglia for targeted ischemic stroke therapy. Meanwhile, ultrasound combined with microglial therapy may be a novel strategy for stroke treatment via creating anti-inflammatory microenvironment 68. Based on Zhang et al., 69 LIPUS promoted spinal fusion and stimulated the transition of M1 to M2 macrophages. In the heart system, Zhao et al. demonstrated that LIPUS prevents hypoxia-induced cardiac fibrosis through HIF-1α/DNMT3a pathway via a TRAAK-dependent manner 61. Their team also discovered that LIPUS could ameliorate angiotensin II-induced cardiac fibrosis by alleviating inflammation via a caveolin-1-dependent pathway 62. In our study, various histological and behavioral functional indicators have been evaluated in detail and specific mechanisms have been elucidated by which LIPUS regulates the immune microenvironment of skeletal muscle from an immunological perspective.
As for LIPUS parameters set in vivo, human's exposure area and energy are 30mW/cm2 for human mouthpieces 70, whereas Montalti et al. 19 reported that rats are 30mW/cm2 for tibialis anterior. In addition, Chan's settings are 30mW/cm2 for gastrocnemius muscles 17. Because some studies are inconsistent 71,72, we performed pre-experiments and it was determined that 30 mW/cm2 and 60mW/cm2 are the lowest and highest energy to promote proper muscle healing, which is equivalent to 1 or 2 times the energy used in humans 70. For the cell experiments, Zhao et al. 73 applied the settings of 200 mW/cm2 and 1.5 MHz on macrophages, while other researchers 74,75 applied 50 mW/cm2 on PC12 cell and 30 mW/cm2 on C2C12 cells. Furthermore, it was determined that the dose range macrophages can tolerate is from 10 to 90 mW/cm2 through pre-experiments. The above parameters provide strong support for future studies on LIPUS treatment on skeletal muscle or macrophages.
Our study proved that ultrasound reduced early cell infiltration and hematomas histologically, which is in accordance to the previous results conducted by Signori and Junior et al. 39,76. In recent study by Sabbagh et al., they reported that LIPUS could opened the brain blood barrier and modulate liberation of chemokine, and the rapid exit of inflammation and hematoma in our study may be elicited by LIPUS-mediated vascular permeability and chemokines changes 77. In addition, new muscle fibers number and CSA elevated after LIPUS treatment in our study suggested that LIPUS can promote muscle healing and recover muscle strength. Stress is a more accurate representation of the muscle specific mechanical (material) properties. Fibrosis was significantly reduced in the LIPUS group, which also demonstrated that skeletal muscle achieved suitable tissue remodeling. Those results were also similar to the previous study 9. Moreover, LASCA is now one of the gold standards for assessing vascular status 78 and the LASCA was innovatively applied to assess vascular regeneration in LIPUS treatment of muscle injury for the first time, which further demonstrated that muscle healing is significantly enhanced by LIPUS. Wang et al. 79 found a significant recovery of blood flow after LIPUS treatment in the abusive head trauma model, which is consistent with our results. What's more, behaviorally and functionally, performed gait analysis, force test, rotard rod test, treadmills test have been comprehensively performed on injured mice and it was found that LIPUS treatment does significantly improv muscle function, including a range of indicators such as force, physical co-ordination, persistence, and posture, which is consistent with the recovery force reported by Yimlamai and Chan et al. 17,80, further providing a solid basis for the application of LIPUS in clinical practice.
Macrophage polarization has received extensive attention in many animal models of injurious disease 27,81-84. For instance, Martins et al. 51 found that increasing the M2 subtype at the early stage reduced fibrosis formation in the muscle contusion model, which was consistent with our results that LIPUS reduced fibrosis formation through mediating macrophage polarization. Zhou et al. 85 demonstrated M2 derived exosomal miR-501 could promote myotube formation after muscle injury. These studies proved the importance of macrophage polarization regulation in the process of tissue repair 27,86,87. Based on our previous studies, BMSC-derived exosomes can promote M2 polarization in skeletal muscle, which remarkably improved skeletal muscle healing 31, and inflammatory C2C12-Exos can induce M1 polarization to prolong the inflammatory response 28. In the present study, it was found that early overall macrophage infiltration was reduced, and it appeared that macrophage was a negative factor. But after we knocked out macrophages using clodronate liposomes, we found that LIPUS-mediated promotion of skeletal muscle healing was greatly reduced, both histologically and behaviorally, and the LIPUS+contusion groups showed no significant differences compared with contusion-only group, which suggested that macrophages played an indispensable role in LIPUS-mediated therapeutic effects and that the absence of M2 macrophages would lead to dysregulated tissue remodeling.27,88 This similar finding was also demonstrated in the study of Xiao et al. After macrophage depletion, there were significantly more fibrosis areas in the injured muscle 89.
Afterward, how LIPUS regulates macrophages was explored, and it was identified that LIPUS promoted M2 and reduced M1macrophages based on both in vitro and in vivo models, which was in line with the findings of Junior's study 39. We further clarified the related underlying mechanisms in the cell model and demonstrated that the WNT/FZD5/β-catenin axis was the key pathway in regulating LIPUS promoting M2 polarization through positive and negative validation in vitro. WNT/β-catenin is an evolutionarily highly conserved fundamental signaling system and orchestra the proliferation, differentiation, apoptosis, motility, and polarization of cells through the WNT ligands binding to FZD receptors and β-catenin nuclear translocation 90. 19 WNT homologs and 10 receptors of the FZD family as well as several co- and alternative receptors are conserved in mice and men. After a certain literature research, it was discovered that WNT3a and WNT5a can regulate the polarization of macrophages via FZD1 and FZD5 receptors respectively 91-93. However, with ultrasonic stimulation, we found that FZD1 protein expression was not significantly altered whereas FZD5 protein expression was dramatically elevated in vitro. Therefore, it can be concluded that FZD5 was involved in modulating macrophage polarization.
Although there is still no research directly demonstrating that FZD family receptors belong to mechanosensitive membrane surface receptors, some articles indicated the connection between FZD and other mechanosensitive receptors, such as integrin and caveolin 94. Whether the mechanical energy generated by ultrasound acts directly on FZD or modulates FZD through other known mechanosensitive ion channels or membrane surface receptors still requires further investigation.
Most importantly, this mechanism was evaluated in vivo, which was consistent with in vitro experiments. The WNT signaling pathway was reported as a classical pathway that regulates macrophage polarization 95-98. In term of the study by Cosin-Roger et al. 99, it was found that the promotion of the WNT pathway could enhance M2 polarization, which results in the promotion of mucosal repair in TNBS-Treated mice. However, LIPUS has been found to inhibit the WNT pathway in the synoviocyte in Liao's study 100. Nevertheless, in Ren's study 16, it was identified to promote the WNT pathway. Besides, Li et al. demonstrated that LIPUS could promote the differentiation of HGF-induced BMSCs into hepatocytes through the Wnt/β-catenin signaling pathway 101. Therefore, the effects of LIPUS on WNT signaling are different depending on the tissue and cell types 16,100. Since the WNT pathway plays a role in muscle, it is also worthwhile to investigated weather LIPUS promote the muscle regeneration through mediating WNT pathway. In this study, we found that ultrasound promoted M2 polarization via WNT pathway. When there were more M2 polarized macrophages, range of anti-inflammatory factors which relieved the excessively inflammatory microenvironment will be produced. The mannered inflammatory microenvironment further benefited tissue remodeling, vascular regeneration and other responses 27,102,103, which also exhibited from histological and behavioral aspects. In conclusion, our research has found that LIPUS promoted muscle healing and functional recovery by promoting M2 polarization through the WNT pathway and alleviating the inflammatory microenvironment.
Although we tried to elucidate the effects of LIPUS on skeletal muscle healing from various perspectives, we still have some shortcomings. First, we did not construct macrophage-specific FZD5 knockout mice to further validate the experimental results. Second, we cannot directly predict the optimal dose of ultrasound for clinical use in humans from this article, and more clinical cohort studies are required to investigate 104. Third, our in vitro cell experiments do not fully reflect in vivo conditions. Although some studies have reported that LIPUS can promote myoblast growth in vitro 17, it is not possible to fully model all cell types within skeletal muscle. The inflammatory microenvironment model induced by LPS is classically and widely applied to investigate macrophage-mediated immune regulation 105,106.
## Conclusion
In this study, we demonstrated for the first time that application of LIPUS in acute stage after muscle injury, especially high-dose strategy (60mW/cm2), suppressed the inflammatory immune microenvironment and improved muscle healing, where promoting M2 polarization via regulation of WNT signaling pathways was the key mechanism involved. Besides, the multiple results of comprehensive functional tests indirectly proved the functional recovery of the injured muscle after LIPUS treatment. Our work presents new insight and convincing evidence for LIPUS-based noninvasive treatments of severe muscle injury and other acute inflammatory diseases.
## Funding
Supported by the project of the National Natural Science Foundation of China (grant number 82102634, 81871843).
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---
title: Improving family health climate, effect of role modeling and maternal support
in female students
authors:
- Jeyran Ostovarfar
- Mohammad Hossein Kaveh
- Hossein Molavi Vardanjani
- Leila Ghahramani
- Masoud Karimi
- Abdolrahim Asadollahi
- Razie Zare
journal: BMC Primary Care
year: 2023
pmcid: PMC10008707
doi: 10.1186/s12875-023-02015-7
license: CC BY 4.0
---
# Improving family health climate, effect of role modeling and maternal support in female students
## Abstract
### Introduction
Girls can use their mother’s emotional, informational and behavioral support to perform healthy behaviors due to their constant access to their mothers. This study aimed to evaluate the effect of role modeling and maternal support in the family to improve healthy behaviors and perceived Family Health Climate (FHC) in female students.
### Methods
In this educational quasi-experimental study, 261 female students (133 in the intervention group and 128 in the control group) and 223 mothers (109 intervention and 114 control) were selected using the cluster multi-stages sampling method and entered the study. Participants (intervention and control groups) completed the FHC scale at three stages (before intervention, immediately after the intervention, and 2 months after intervention). A training program that comprised 12 sessions for students and six sessions for their mothers using collaborative learning techniques and printed materials was conducted with the experimental group. Also after completing the questionnaire in the follow-up phase, pamphlets and educational videos were given to the control group. Data were analyzed using SPSS20 via a chi-square test, independent t-test, and Repeated Measures ANOVA at a significance level of 0.05.
### Results
Before the intervention, there was no significant difference between demographic variables and the score of the FHC scale in both groups ($p \leq 0.05$). Immediately and 2 months after the intervention, the experimental group (female students and their mothers) showed a significant increase in dimensions of FHC, including FHC-NU (Family Health Climate-Nutrition) and FHC-PA (Family Health Climate-Physical Activity), compared to the control group ($p \leq 0.05$).
### Conclusions
Educating and informing mothers about the impact of their role modeling on their children, especially girls, can make them more aware of health-oriented behaviors towards their children. Such findings reinforced the importance of focusing on actions to encourage a healthy lifestyle (healthy diet and physical activity) in students with a focus on role modeling and parental support, especially mothers.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12875-023-02015-7.
## Background
Well-being and positive social functioning are rooted in childhood. Perhaps the strongest predictor of these paths is the family quality of life and the parental environment in which the child is placed [1, 2]. Adolescents’ health behaviors are influenced by social contexts, such as peer groups, the school environment, and the family. Because of its continual effect on children and adolescents’ physical activity and health behaviors, the family is critical [3].
The family is the core of the socialization of members, a place where values are passed on to individuals, ideas are learned and adopted, and beliefs and norms of behavior are acquired. Its members are divided into separate subsystems through symbolic boundaries; each contributes to the family’s functioning by performing the necessary roles and responsibilities [3, 4]. According to the family system’s approach, the family is more than the sum of individuals. This theoretical framework requires interactions within and between individuals and the shared family environment. Therefore, the family environment’s characteristics should affect individual behavior and factors. One aspect of the family environment may be the family health climate [3, 5].
Family health climate (FHC): Family health climate is defined as shared perceptions and cognitions about a healthy lifestyle within the family. It reflects the individual experience of daily family life, evaluating health-related issues and expectations according to common values, routine behaviors, and family interaction patterns. The FHC acts as a framework for health-related behaviors, is the basis for regulating health-related behaviors, and provides a reference for evaluating and interpreting individual behaviors. Thus, the FHC is an aspect of the family environment that shapes the daily health behaviors of family members. FHC can be assessed for healthy eating using the FHC-Nutrition scale and physical activity using the FHC-Physical Activity scale. FHC represents a variable at the family level related to internal and interpersonal relationships within the family environment and individual factors [5].
Family health climate has been demonstrated to affect the weekly physical activity and the consumption of nutritious foods in adolescents and children [3, 6]. A positive family health climate is an environment where eating healthfully and being physically active are highly valued and an integral part of daily family life [5]. While the strong association between physical activity and nutrition with health has been proven [7, 8]. She family health climate model can be utilized to understand how an individual’s family can shape their health behaviors [5].
Parents and children are part of a family whose members influence each other’s behavior and this influence is mutual [9]. It has been reported that due to the ongoing communication between parents and children’s behaviors and attitudes, parents’ modeling impacts how children think and behave about food and physical activity [10]. Parents also play a crucial role in transmitting health information and supporting their children’s healthy behavior [11]. Meanwhile, Wunsch et al. ’s study shows that targeting all family members facilitates behavior change at the individual and family level, because the implemented strategies address changes in daily family life [12].
Mothers are primary caregivers who usually provide a framework for children’s meals with specific foods and show them how much they can eat [13–15]. On the other hand, numerous studies show that a mother’s role modeling healthy active behaviors has a more impact on children than the paternal model [16, 17]. Girl’s health behaviors align with their mothers for several reasons; girls eat the food that their mothers prepare for them [18], they at an early age are more likely to pattern from their parents’ eating behaviors [19], and children with active mothers are more likely to be active than children with non-active mothers [20]. Children’s preferences for certain foods usually reflect eating at home [19]. Accordingly, researchers consider mothers to be the leading cause of change in influencing their children’s diet and physical activity [21] and mothers who are trained in healthy eating and physical activity are more likely to engage in health-oriented behaviors for their children [19]. Also, compared to other parent-child relationships, interdependence and emotional dependence in the mother-daughter relationship is more substantial, and the nature of the relationship plays a vital role in girls’ social and psychological well-being. Therefore, girls can use their emotional, informational and behavioral support to perform healthy behaviors due to their constant access to their mothers [22].
This study aimed to evaluate the effect of maternal support as role modeling in the family to improve healthy behaviors in girls. Furthermore, was done in answer to this question, is the mother’s support intervention as role modeling effective in changing the family health climate perceived by female students and their mothers?
## Research design
The purpose of this study was to evaluate the impact of the educational program on students and their mothers, using role modeling and mothers’ support to promote a perceived family health climate in female students and their mothers. Based on this goal, a quasi-experimental plan consisting of an intervention group and a control group was approved in which pre-test, post-test, and 2-month follow-up were designed to evaluate the program’s effects. Ethics approval was obtained from research ethics board of the Shiraz University of Medical Sciences. The study was approved by the ethics committee on $\frac{07}{08}$/2019 (IR.SUMS.REC.1398.896).
## Participants
Participants in this study were fifth-grade female students who studied in Shiraz schools and their mothers. Sampling was done by the cluster sampling method among the four education districts of Shiraz, each of which has 43, 39, 58, and 55 schools, respectively. From the four districts, two districts were randomly selected (districts 2 and 4). Schools in each area (four schools) were randomly allocated to experimental and control groups. Two classes were randomly selected in each school, and 261 female students were eventually selected.
In the data collection process, the first step was to visit the selected schools, meet with school principals and inform them about the content and purpose of the study. After receiving the approval of the school principals, the pre-test questionnaire form was completed by 261 fifth-grade students who met the inclusion criteria. In contrast, the questionnaire forms for mothers in the distribution envelopes were sent to them by the children. After three working days, the researchers returned to the schools, and the questionnaire forms completed by the mothers were collected. Mothers who participated in the intervention program meant that they completed the questionnaire themselves. Based on the collected data, mothers’ questionnaires were considered for further analysis. Two hundred twenty-three mothers finally agreed to participate in the study by filling out questionnaires after calling and explaining the objectives and content of the research. Among the mothers who decided to participate in the program, (109 intervention) and (114 control) were divided into two groups based on the division of their children’s schools and classes. At the beginning of the intervention, the educational and occupational levels of the mothers were checked in terms of the homogeneity of the intervention and control groups, because it was thought that these factors affect the results of the intervention.
The demographic characteristics of the students in the intervention and control groups are presented in Table 1.Table 1Demographic characteristics of students in the two groups of intervention and controlDemographic variablesNumber of people in the intervention groupNumber of people in the control group p-value * Mother’s education Primary530.113High school93Diploma3336Bachelor6672Master degree and higher2014 Father’s education Primary830.235High school77Diploma3948Bachelor5456Master degree and higher2514 Mother’s occupation Employee50410.158housewife7282self-employment115Retired00 Father’s occupation Employee64520.480worker59self-employment5760Retired and unemployed77 Number of family members 333240.639470725–629307 and more12 The inclusion criteria for female students and their mothers were: to be studying in public schools in Shiraz, their grade of education should be fifth, to complete the written informed consent form by students and their mothers, and the exclusion criteria were: absenteeism in educational sessions for two sessions or more, refusing to continue participating in the project, leave the research environment (such as changing schools, etc.).
## Instruments
Data collection tools included a demographic questionnaire and a family health climate scale.
The demographic questionnaire included the student’s age, parental occupation, parental education, and the number of family members.
The Family Health Climate scale was developed by Niermann et al. in 2014 to assess family members’ health behaviors. The questions of the FHC includes two separate scales: FHC-NU consists of 17 questions with four subscales (value, for example, “a healthy diet plays an important role in our lives”; communication, for example, “we talk about which foods are healthy”; cohesion, for example, “we appreciate spending time together during meals”; and consensus, for example, “we rarely argue about food- or diet-related matters”). All questions were begun with “In our family …,” [5].
FHC-PA contains 14 questions with three subscales (value, for example, “it is normal in our family to bephysically active in our leisure time”; cohesion, for example, “… we have fun doing physical activitie stogether (e.g.,bike tours and hikes)”; and information, for example, “we collect information (e.g., on the internet) on physical activity and exercise”) [5].
Answers were given on a four-point rating scale (0 = “definitely false,” 1 = “rather false,” 2 = “rather true,” and 3 = “definitely true”). In a study by Niermann et al., mothers, fathers, and adolescents completed a questionnaire separately. The internal correlation was αFHC-PA = 0:92 and αFHC-NU = 0:86 for the mothers, αFHC-PA = 0:90 and αFHC-NU = 0:86 for the fathers, and αFHC-PA = 0:90 and αFHC-NU = 0:85 for the adolescents [7].
In the Persian version of the FHC-Scale, Cronbach’s alpha coefficient for FHC-PA in female students and their mothers was 0.88 and 0.86 for the whole scale. Cronbach’s alpha coefficient for FHC-NU in female students and their mothers was 0.83 and 0.92 for the whole scale [23].
The education program for students and their mothers was implemented with the permission of the school administration. The program included 12 sessions for students and six sessions for their mothers.
## Procedure and program
In the student sessions, there were six sessions related to healthy eating education and promoting eating behaviors. At the beginning of the sessions, students received healthy snacks such as fruit, milk, or healthy pre-prepared foods. During the sessions, various methods were used, such as; lectures on general topics, use of audio-visual presentation, questions and answers, problem-solving, sample cases, asking students to make a list of healthy meals with the help of mothers, as well as making list of meals that students usually prefer to use and then a discussion was held about it. Six sessions related to physical activity were performed by doing the students’ favorite sports with the cooperation and presence of a physical activity instructor. Also, during the sessions about the minimum physical activity required by adolescents and the definition of moderate to vigorous physical activities and types of sports such as endurance, stretching, etc. were educated through educational videos, lectures, and booklets. The research team also coordinated with a sports club to facilitate the intervention group’s enrollment in a sports class.
In the mothers’ group, due to the busy schedule of mothers, the number of sessions was less, so more nutritional information and physical activity were provided to mothers in the form of short texts through social media. Three nutrition education sessions were conducted to get acquainted with the nutrition groups as well as the nutritional needs of adolescents through lectures, questions, answers, audio-visual presentations, and brainstorming. Mothers were also encouraged to cooperate and advise on preparing their children’s meal lists and involving children in choosing food when shopping. Three physical activity sessions were held. In the first session, the researchers gave a speech to express the objectives of the physical activity sessions, obtain the mothers’ consent to participate in the classes, and attend a sports club that was previously coordinated. Also, they were encouraged to start sports that did not require special equipment to have regular physical activity. Also, physical activity training videos that were appropriate for mothers and their daughters were shown. They were asked to organize family walks in their free time, encourage children and other family members to exercise, act as role models, and provide their children with the necessary facilities for physical activity.
To reduce dropout rates in the intervention stage, a reminder SMS was sent to the mothers of the intervention group before the training sessions. Invitation letters were also sent to mothers through students. Because the students were in school, one of the researchers encouraged them to attend the training sessions.
In the control group, the questionnaires were completed in 3 stages. When completing the questionnaires, healthy meals were given to students and their mothers, and after completing the questionnaire, educational booklets and videos were given to them in the follow-up stage.
## Statistical analysis
The collected data were analyzed using SPSS version 20. A Chi-square test was used to check the homogeneity of demographic variables in the control and intervention groups and a t-test was used to check the mean of the student’s age in the two groups. After that, an independent t-test and Repeated Measurement Anova at the significance level of 0.05 to examine the impact of the intervention on FHC -NU and its variables (Communication, Value, Cohesion, Consensus), FHC -PA and its variables (Information, Cohesion, Value) in students and their mothers were done.
## Findings
A total of 261 students were included in the study, including 133 in the intervention group and 128 in the control group. As shown in Table 1, the Chi-square test did not show a significant difference between the intervention and control groups regarding demographic variables ($p \leq 0.05$). In other words, the two groups were identical in these characteristics. The results of the t-test showed no statistically significant difference between the mean of the student’s age in the intervention and control groups ($$p \leq 0.254$$). The means of students’ age in the intervention and control groups were 19.57 and 10.50 years, respectively.
The results showed that the dimensions of FHC, including FHC-NU and FHC-PA (their subscales) in the sample of students in the intervention group after the intervention, were significantly different from before the educational intervention. However, in the control group, this difference was not significant. At the beginning of the intervention, there was no significant difference in the mean scores of FHC dimensions and their subscales between the two groups, but immediately after the intervention and 2 months after the intervention (follow-up stage), there was a significant difference (Table 2).Table 2Comparison of the mean score of FHC in the two groups of intervention and control before the intervention, immediately after the intervention, and 2 months after the educational intervention in female studentsVariableGroupBefore intervention M ± SDImmediately after the interventionTwo month after intervention M ± SD P-Value٭٭ FHC-NU experimental27.85 ± 10.3839.14 ± 9.4732.58 ± 11.71> 0.001control28.40 ± 9.4529.50 ± 10.1928.27 ± 11.230.614 P-Value٭0.876> 0.0010.005 Communication experimental7.37 ± 4.3810.50 ± 4.318.73 ± 4.19> 0.001control7.34 ± 4.337.75 ± 4.237.50 ± 4.100.759 P-Value٭0.774> 0.0010.027 Value experimental6.61 ± 3.2310.56 ± 2.368.58 ± 2.96> 0.001control6.67 ± 3.607.43 ± 3.596.96 ± 3.600.190 P-Value٭0.841> 0.001> 0.001 Cohesion experimental10.57 ± 4.0212.99 ± 3.4310.69 ± 4.24> 0.001control10.86 ± 3.7110.90 ± 4.399.86 ± 4.140.122 P-Value٭0.881> 0.0010.133 Consensus experimental3.30 ± 2.815.18 ± 2.894.58 ± 2.46> 0.001control3.47 ± 2.973.49 ± 2.703.95 ± 2.300.151 P-Value٭0.734> 0.0010.046 FHC-PA experimental18.09 ± 11.1427.37 ± 11.0222.00 ± 9.65> 0.001control18.98 ± 11.6620.43 ± 11.1219.52 ± 9.580.802 P-Value٭0.527> 0.0010.052 Information experimental4.08 ± 4.186.88 ± 4.355.43 ± 3.54> 0.001control4.62 ± 4.295.27 ± 3.924.83 ± 3.080.378 P-Value٭0.4770.0080.176 Cohesion experimental7.38 ± 5.2411.79 ± 3.598.98 ± 4.44> 0.001control8.00 ± 4.798.36 ± 4.457.97 ± 4.520.736 P-Value٭0.227> 0.0010.053 Value experimental7.21 ± 5.1011.35 ± 3.759.57 ± 4.21> 0.001control7.07 ± 4.928.62 ± 4.498.31 ± 4.380.158 P-Value٭0.812> 0.0010.028٭Independent t-test ٭٭ Repeated Measurement.
## Result of intervention in mothers
The results of the t-test showed no statistically significant difference between the mean of the mother’s age in the intervention and control groups ($$p \leq 0.061$$). The means of mother’s age in the intervention and control groups was 38.94 and 37.47 years, respectively. Similarly, the mean age of students’ fathers in the intervention group was 42.95, and in the control group was 42.40 years. The independent t-test showed that the two groups were homogeneous($$p \leq 0.457$$). There was no significant difference between the two groups of intervention and control before the educational intervention, as shown in Table 1 in terms of the level of education of the mother and father, parent’s occupation status, and the number of family members, so the two groups were homogeneous in terms of these variables.
The results showed that the dimensions of FHC, including FHC-NU and FHC-PA (their subscales) in the sample of mothers in the intervention group, differed significantly from before the educational intervention. However, in the control group, this difference was not significant. At the beginning of the intervention, there was no significant difference in the mean scores of FHC dimensions and their subscales between the two groups of mothers, but immediately after the intervention and in the follow-up stage, there was a significant difference (Table 3).Table 3Comparison of the mean score of FHC in the two groups of intervention and control before the intervention, immediately after the intervention and 2 months after the educational intervention in mothersVariableGroupBefore intervention M ± SDImmediately after the interventionTwo month after intervention M ± SD P-Value٭٭ FHC-NU experimental25.72 ± 8.4341.02 ± 7.2734.69 ± 11.71> 0.001control25.78 ± 9.0428.91 ± 12.3226.44 ± 12.840.120 P-Value٭0.961> 0.001> 0.001 Communication experimental6.64 ± 3.7012.34 ± 2.569.92 ± 3.85> 0.001control6.88 ± 3.747.92 ± 4.257.13 ± 3.980.126 P-Value٭0.817> 0.001> 0.001 Value experimental6.62 ± 2.389.82 ± 2.338.54 ± 2.69> 0.001control6.79 ± 2.617.51 ± 3.606.80 ± 3.540.142 P-Value٭0.628> 0.001> 0.001 Cohesion experimental8.63 ± 3.5512.84 ± 2.6711.01 ± 3.50> 0.001control8.46 ± 3.779.29 ± 4.408.22 ± 4.730.213 P-Value٭0.724> 0.001> 0.001 Consensus experimental3.83 ± 2.526.02 ± 2.255.22 ± 2.33> 0.001control3.65 ± 2.434.19 ± 2.434.29 ± 2.500.153 P-Value٭0.884> 0.0010.029 FHC-PA experimental19.51 ± 8.9627.01 ± 6.6622.47 ± 8.50> 0.001control19.62 ± 8.8622.36 ± 8.6921.36 ± 8.950. 229 P-Value٭0.924> 0.0010.344 Information experimental4.31 ± 3.396.72 ± 2.824.68 ± 3.07> 0.001control4.86 ± 3.455.56 ± 3.345.58 ± 3.090.297 P-Value٭0.8120.0090.052 Cohesion experimental7.67 ± 3.9110.17 ± 4.458.22 ± 4.44> 0.001control7.47 ± 3.858.20 ± 4.757.47 ± 4.520.575 P-Value٭0.727> 0.0010.197 Value experimental7.53 ± 4.1210.11 ± 2.879.57 ± 4.21> 0.001control7.29 ± 4.058.60 ± 3.828.31 ± 4.380.060 P-Value٭0.6890.0020016٭Independent t-test٭٭ Repeated Measurement
## Discussion
We investigated the effect of role modeling program training and mothers’ support on the perceived family health climate level in female students and their mothers compared to the control group (without support program and role modeling). ( See attached Supplementary Fig. 1).
Because of the researcher’s relationship with the students, all students participated in the study, and 196 ($75\%$) mothers participated based on the inclusion criteria and interests.
Considering that the maximum possible score of FHC-NU is 51, the study found that the average scores before intervention for the children (approximately 27) and mothers (approximately 25) in the intervention and control groups were low. The low pre-intervention scores are thought to be since the study sample was selected from a general population that had not experienced nutrition and physical activity interventions.
In the current study, as the results section shows, the changes during the study in the dimensions of FHC-NU (Communication, Value, Cohesion, Consensus) and FHC-PA (Information, Cohesion, Value) in the group of students are in line with the changes in the group of mothers. It seems that this information confirms the study results of Herman et al. [ 20].
The process of the present study is similar to a study that showed that family participation, especially mothers, in a school-based intervention is an important component of the program [18]. However, this study contributes to a growing body of literature emphasizing the importance of family and maternal involvement in influencing their children’s nutritional status and physical activity [19–21]. This may be because mothers act as important role models and comprehensive advocates of their child’s eating and physical activity behaviors [24], so it is reasonable to ask them to participate in programs on these critical issues actively. Also, comparing the results of the data obtained from the amount of students physical activity and their mothers during the present study shows that with the increase in the amount of mothers physical activity in the intervention phase as a role model, the amount of students physical activity has also increased and with reduction in the amount of mothers physical activity in the follow-up stage, the students physical activity has also decreased.
According to Goodwin et al. ’s research, in many communities, women have become socialized as caregivers and maintainers of the family unit by preparing food, caring for family members, and talking to children to become productive adults [25]. Therefore, it seems that the results of the present study also confirm this assertion, and women can be considered promoters of family health, especially in developing countries where mothers’ lives are very closely related to their children’s lives [25]. Therefore, by improving the perceived family health climate in mothers, both in the field of nutrition and physical activity, positive results can be seen in the perceived health climate of other family members, especially girls who have a closer relationship with their mothers.
This is while Elizabeth et al. [ 26], in their study, argue that mothers try to achieve health goals (physical activity and healthy diet) for their family members, especially children, even if those goals are incompatible with the mothers’ living conditions. Of course, they also point out that when these cares and goals are considered as the usual duties of women, and all duties (the duties of mothers about improving the nutrition of family members and sports habits, buying food, cooking, taking children to sports training) is on the responsibility of a family member, i.e., mother, it will be harmful. Therefore, it seems that the long-term results of such interventions on mothers should be investigated, and more accurate results should be reported.
The follow-up phase of the study was associated with the Covid-19 prevalence epidemic, so it seems the decrease in physical activity in both groups can be related to the prevalence of the Covid-19 epidemic. As Shahidi et al., In their study, points out that the Covid-19 prevalence epidemic has limited the amount of physical activity in this period [27]. Robinson et al. Also reported negative changes in eating and physical activity during the prevalence of the Covid-19 epidemic among UK adults, which is consistent with the results of the present study [28].
However, in addition to paying attention to external factors (positive interferer factors such as training and encouragement to have a healthy diet and physical activity or negative interfering factors such as the prevalence of Covid-19), it seems better to pay attention to the psychosocial factors of individuals and interactions within their family [29]. In this regard, Naisseh et al. [ 30] also showed that the level of self-determination of parents about participation in physical activity is related to their support of their children’s physical activity, which can act as a positive or negative role model.
## Conclusion
The results of the present study showed that despite the decrease in the amount of mothers FHC-PA and its subscales in the follow-up and epidemic stage of COVID-19, it did not reduce the amount of female students FHC-PA and its subscales, which can be referred to as the supportive role of mothers. They have supported their children’s health-oriented behaviors by enduring the stress of the COVID-19 outbreak. Such findings reinforced the importance of focusing on actions to encourage a healthy lifestyle (healthy diet and physical activity) in students with a focus on parental role modeling and support, especially mothers. Also, educating and informing mothers about the impact of their role modeling on their children, especially girls, can make them more aware of health-oriented behaviors towards their children.
## Supplementary Information
Additional file 1: Supplementary figure 1. Changes in mean scores of FHC in the two groups of intervention and control in students and their mothers during the study.
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|
---
title: Re-analysis of ventilator-free days (VFD) in acute respiratory distress syndrome
(ARDS) studies
authors:
- Rejina Mariam Verghis
- Cliona McDowell
- Bronagh Blackwood
- Bohee Lee
- Daniel F. McAuley
- Mike Clarke
journal: Trials
year: 2023
pmcid: PMC10008713
doi: 10.1186/s13063-023-07190-7
license: CC BY 4.0
---
# Re-analysis of ventilator-free days (VFD) in acute respiratory distress syndrome (ARDS) studies
## Abstract
### Background
Over recent decades, improvements in healthcare have reduced mortality and morbidity rates in many conditions. This has resulted, in part, from the identification of effective interventions in randomised trials, and in conducting such trials, a composite outcome measure (COM) with multiple components will increase event rates, which allows study completion with a smaller sample size. In critical care research, the COM “ventilator-free days” (VFD) combines mortality and duration of mechanical ventilation (MV) into a single continuous measure, which can be analysed in a variety of ways. This study investigates the usefulness of Poisson and two-part Poisson models compared to t-distribution for the analysis of VFD.
### Methods
Data from four studies (ALbuterol for the Treatment of ALI (ALTA), Early vs. Delayed Enteral Nutrition (EDEN), Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute Lung Injury (ALI) to reduce pulmonary dysfunction (HARP-2), Statins for Acutely Injured Lungs from Sepsis (SAILS)) were used for analysis, with the VFD results summarised using mean, standard deviation (SD), median, interquartile range (25th and 75th percentiles) and minimum and maximum values. The statistical analyses that are compared used the t-test, Poisson, zero-inflated Poisson (ZIP) and two-part Logit-Poisson hurdle models. The analyses were exploratory in nature, and the significance level for differences in the estimates was set to 0.05.
### Results
In HARP-2, which compared simvastatin and placebo, the mean (SD) VFD for all patients was 12.0 (10.2), but this mean value did not represent the data distribution as it falls in a zone between two peaks, with the lowest frequency of occurrence. The mean (SD) VFD after excluding patients who died before day 28 and patients who did not achieve unassisted breathing were 15.9 (8.7) and 18.2 (6.6), respectively. The mean difference ($95\%$ CI) between the two groups was 1.1 ($95\%$ CI: 0.7 to 2.8; $$p \leq 0.20$$) based on an independent t-test. However, when the two-part hurdle model was used, the simvastatin arm had a significantly higher number of non-zero values compared to the placebo group, which indicated that more patients were alive and free of mechanical ventilation in the simvastatin group. Similarly, in ALTA, this model found that significantly more patients were alive and free of MV in the control group. In EDEN and SAILS, there was no significant difference between the control and intervention groups.
### Conclusion
Our analyses show that the t-test and Poisson model are not appropriate for bi-modal data (such as VFD) where there is a large number of zero events. The two-part hurdle model was the most promising approach. There is a need for future research to investigate other analysis techniques, such as two-part quantile regression and to determine the impact on sample size requirements for comparative effectiveness trials.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13063-023-07190-7.
## Introduction
Improvements in healthcare have resulted in people living longer, and patients today have a better prognosis than even a decade ago because of lower mortality and morbidity rates. This has arisen, in part from the identification of effective interventions in randomised trials, but the decline in event rates implies that smaller differences (effect sizes) should now be expected between the groups in comparative trials. To show statistically significant smaller effect sizes, larger sample sizes, more sites for recruitment, more research staff, more regulatory requirements and usually a longer recruitment period [1]. All these factors increase the costs of research [1]. A composite outcome measure (COM) that combines two or more outcome measures can result in higher event rates and improve statistical efficiency, allowing study completion with a smaller sample size.
COMs can be classified into three main types [2]: (i) an outcome derived from a variety of component variables, (ii) occurrence of any one of the component events within a specified period and (iii) time to first occurring event within a specified period. In healthcare research, the idea of “free day” was initially proposed in 1992 [3, 4], with “free days” being a composite of survival and being “free” from receiving a resource such as organ support or ICU admission within a specified period. ICU-free days, hospital-free days and organ failure-free days are a few examples of free days.
In critical care, ventilator-free day (VFD) is generally defined as the number of days the patient was alive and free of mechanical ventilation (MV). It combines mortality and duration of MV into a single continuous measure. In the case of 28-day VFD, a patient is given a value of 0 if they die before day 28 or are still receiving MV at day 28. If, for example, the patient achieves unassisted breathing and remains ventilator-free at day 10 and alive at day 28, they are given a value of 18. VFD penalises mortality by giving the worst value of 0 if the patient dies at any time in the 28 days, which makes VFD a better outcome compared to analysing the duration of MV or duration in MV only in survivors.
In studies of patients with acute respiratory distress syndrome (ARDS), interventions are often designed to optimise respiratory parameters with the goal of improving ventilation and thus reducing time on MV. Reducing the duration of MV lowers the risk of ventilator-associated harms, length of stay in ICU and hospital and ultimately death. Thus, a COM, such as VFD, can be patient-centred and economically meaningful.
Despite its relevance in ARDS studies, VFD poses several methodological challenges. The first issue is the relative importance of components in the VFD. Mortality is a critical event, and prolonged MV is not as critical as death. As noted above, a patient who dies between 0 and 28 days is given a VFD value of 0, and a patient requiring prolonged ventilation for more than 28 days is also given this value of 0, and this is comparable to considering death as a censoring event in survival analysis, and in this scenario, it can lead to misleading results [3]. The second issue is related to the distribution of the VFD. VFD combine three mutually exclusive patient groups: those who (i) die before day 28, (ii) require prolonged MV and (iii) achieve unassisted breathing before day 28. Patients in groups (i) and (ii) receive a zero value, and those in group (iii) receive a non-zero value resulting in multiple peaks in a frequency plot, with one peak at 0 days and another in the twenties. This presents methodologic challenges in analysing and interpreting the data. The third issue is the presence of excessive “zeros” which is a separate methodological issue. These “zeros” are generated by two mutually exclusive processes “prolonged ventilation” and “death”. Patients in the former “zero” group will have had no days free of MV in the 28 days, but those in the latter did not become ventilator-free because of the competing event death, i.e. not all “0” imply “zero MV free days”. In relation to resource use, if the patient dies on day 8, the duration of ventilation is 8 days, and associated costs are for those 8 days, and for the patients who were on the ventilator for 28 days, the associated costs are higher compared to patients who die by day 8. The fourth issue is related to the type of component variables. Mortality is a binary variable (dead/alive), and duration of MV is a continuous variable (ranging from 0 to 28). Both mortality and duration of ventilation can be expressed as 0 [3], which indicates that the estimate of VFD may not reveal the mortality rate or duration of MV at the trial level unless they are reported separately. VFD is an ordinal variable, and if deaths are excluded, the VFD becomes a discrete interval variable. Bodet-Contentin and colleagues [3] presented an iso-VFD curve which showed that similar VFD estimates can be achieved for different mortality rates and duration of MV among survivors. For example, a VFD of 10 can be obtained for (i) a mortality rate of $10\%$ and 14 days of median duration of MV among survivors and (ii) a $20\%$ mortality rate and 10 days of median MV duration among survivors who have a VFD of 10 [3]. Therefore, the VFD is influenced by both the mortality rate and the duration of MV, and if the VFD in two groups (e.g. those receiving the interventions in a randomised trial) differ, it would not be possible to know how each of these variables is influencing the VFD value unless the components are reported separately.
Different approaches are used to analyse the duration of MV in the presence of mortality, and a single test (e.g. t-test, Wilcoxon rank-sum test) is widely used to analyse VFD for hypothesis testing. However, the statistical properties of VFD differ based on the number of days used to calculate the value (e.g. 28-day VFD vs. 60-day VFD) [5]. If a t-test is used, the survival will have a higher weight in the case of a 60-day VFD compared to a 28-day VFD because the surviving patients would have a larger value. A Wilcoxon rank-sum test, the non-parametric alternative for t-test, is less dependent on the normality assumption. However, the substantial number of ties due to the zeros is an issue when using a Wilcoxon test. Survival analysis is another technique used to estimate the time to successful extubation. Conventional survival analysis considers achieving unassisted breathing as the event of interest, with death and prolonged ventilation as the censoring event [6]. This type of analysis assumes that all the patients eventually achieve unassisted breathing or the event of interest, and in the event of death that assumption is violated [7]. Yehya et al. [ 8] proposed the use of the Fine and Gray competing risk approach to evaluate VFD, assuming achieving unassisted breathing as the event of interest and mortality as a competing event. Competing risk model is a frequently used approach when there are two or more competing events which hinder the occurrence of the event. The cumulative incidence function (CIF) is the probability of experiencing the event of interest in each time interval conditional on the patient not experiencing the event of interest or the competing event before. For example, in heart disease studies, the probability of hospitalisation due to a significant cardiac event or death, a competing risk event, is often used and is meaningful [9]. In ARDS studies, achieving unassisted breathing is a positive event and death is a negative event. Therefore, estimating the probability of achieving unassisted breathing or mortality is not very meaningful [9, 10]. If mortality is the outcome of interest, critical care studies often have short-term endpoints like 28-day mortality or hospital mortality, and survival analysis focuses on when the patient died rather than did the patient die. Based on survival analysis, survival function could appear superior for the intervention arm even though the mortality rate is identical, which confuses longer survival with better mortality, which is misleading and should be avoided [11]. The authors have previously stated that ignoring mortality when interpreting VFD can lead to misleading conclusions, but VFD is often interpreted as days free of ventilation ignoring mortality.
Poisson models are often used for positively skewed variable, like the length of hospital or ICU stay [12]. However, the presence of zeros due to death and prolonged ventilation indicates that a two-part model is more appropriate for VFD. This article investigates if Poisson and two-part Poisson models are a better fit for VFD compared to t-distribution.
## Method
VFD is summarised using mean, standard deviation (SD), median, interquartile range (IQR—25th and 75th percentiles) and range (minimum, maximum). The analysis compares the results based on Poisson, zero-inflated Poisson and two-part logit-Poisson hurdle with a t-test. We used the chi-square goodness of fit test to assess whether the expected value was significantly different from the observed value. Analyses were exploratory, and the significance level was set at 0.05. Analyses were carried out using RStudio [13], and the forest plot was created using a SAS macro by Matange [14].
## Poisson model
Denis Poisson proposed the Poisson distribution. A variable X with a Poisson distribution is written as:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{cc}{\varvec{P}}\left({\varvec{X}}={\varvec{x}}\right)=\frac{{{\varvec{\lambda}}}^{{\varvec{x}}}{{\varvec{e}}}^{-{\varvec{\lambda}}}}{{\varvec{x}}!}\end{array}$$\end{document}PX=x=λxe-λx!where, in this case, x would be the VFD value, ranging from 0 to 28. The Poisson distribution has only parameter λ, which represents the mean and variance of the distribution.
## Zero-inflated Poisson (ZIP) model
The zero-inflation Poisson (ZIP) model was proposed by Lambert as an application to estimate the defects in manufacturing [15, 16]. Zero-inflated models are used when two kinds of zeros are thought to exist in the data, “true zeros” and “excess zeros”. Zero-inflated models have two parts, one for the count model and one for the excess zeros. In the case of VFD, zeros due to death are considered excess zeros and zeros due to prolonged mechanical ventilation is considered true zeros, “zero-free days”. The two-part ZIP model with parameters π and λ is written as:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{P}}\left({\varvec{X}}={\varvec{x}}\right)=\left\{\begin{array}{ccc} {\varvec{\pi}}+\left(1-{\varvec{\pi}}\right)\boldsymbol{*}{{\varvec{e}}}^{-{\varvec{\lambda}}} & \mathbf{i}\mathbf{f} & {\varvec{X}} = 0 \\ \left(1-{\varvec{\pi}}\right)\boldsymbol{*}\left(\frac{{{\varvec{\lambda}}}^{{\varvec{x}}}{{\varvec{e}}}^{-{\varvec{\lambda}}}}{{\varvec{x}}!}\right)& \mathbf{i}\mathbf{f}&{\varvec{X}} >0\end{array}\right.$$\end{document}PX=x=π+1-π∗e-λifX=01-π∗λxe-λx!ifX>0where x would be the VFD value, π is the proportion of excess zeros values due to mortality and λ is the mean and variance.
## Logit-poisson hurdle model
The two-part logit-Poisson hurdle model or otherwise known as zero-altered Poisson (ZAP) was introduced by Mullahy [17]. This model assumes two processes, one generating zero and another for non-zero values. The first part of the model involves a logit model for zeros vs non-zeros, and the second part is a Poisson model, with mean λ, for the non-zero observations. Patients crossing the “hurdle” are assigned a positive value. In the case of VFD, the hurdle is being alive and achieving unassisted breathing, and the proportion is represented by π. The main difference between the ZIP model and the hurdle model is that the latter does not distinguish between true zeros and excess zeros. The two-part logit-Poisson hurdle model with parameters π and λ is written as:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varvec{P}}\left({\varvec{X}}={\varvec{x}}\right)=\left\{\begin{array}{ccc} {\varvec{\pi}}& \mathbf{i}\mathbf{f}&{\varvec{X}}=0\\ \left(1-{\varvec{\pi}}\right)\boldsymbol{*}\left(\frac{{{\varvec{\lambda}}}^{{\varvec{x}}}{{\varvec{e}}}^{-{\varvec{\lambda}}}}{{\varvec{x}}!}\right)& \mathbf{i}\mathbf{f}&{\varvec{X}}>0\end{array}\right.$$\end{document}PX=x=πifX=01-π∗λxe-λx!ifX>0where x would be the VFD value, π is the proportion of non-zero values and λ is the mean and variance.
## Data
The National Institute of Health (NIH) and National Heart Lung and Blood Institute (NHLBI) established the Acute Respiratory Distress Syndrome Network (ARDSnet) to develop an effective intervention for ARDS. Data from the HARP2 (Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in acute lung injury to reduce pulmonary dysfunction) trial [6] and three ARDSnet studies, ALbuterol for the Treatment of ALI (ALTA) [18], Early vs Delayed Enteral Nutrition (EDEN) [19], Statins for Acutely Injured Lungs from Sepsis (SAILS) [20], which reported VFD as a primary or secondary outcome, were used in this analysis.
## Results
Figure 1 shows the distribution of the VFD for the HARP2 data. There are two peaks in the observed VFD data, one at 0 and another at 25. By day 28, there were 132 ($24.6\%$) deaths, and 55 ($10.2\%$) other patients had not achieved unassisted breathing [6]. The mean (SD) VFD for all patients was 12.0 (10.2). The mean (SD) VFD after excluding patients who died prior to day 28 was 15.9 (8.7). The mean (SD) after the exclusion of deceased patients and patients requiring prolonged MV was 18.2 (6.6). Table 1 shows the summary statistics for all patients, summary statistics after excluding zeros due to mortality and summary statistics for patients who achieved unassisted breathing (excludes all zeros). The change in summary statistics indicates that the average value is influenced by the zeros. Fig. 1Distribution of the VFD in the HARP2 studyTable 1Summary statistics for HARP-2, ALTA, EDEN and SAILSStudyVFD scoreVFD score for survivorsaVFD score for patients achieving UBbHARP-2AllPlaceboSimvastatinAllPlaceboSimvastatinAllPlaceboSimvastatin N537279258405204201353170180 Mean (SD)12.0 (10.2)11.5 (10.4)12.6 (9.9)15.9 (8.70)15.8 (9.1)16.1 (8.3)18.2 (6.6)18.7 (6.4)17.8 (6.8) Median (IQR)13 [0, 22]12 [0, 22]14 [0, 22]19 (10 to 23)19 [9, 23]19 [10, 23]20 [14, 23]20 [14, 24]20 [14, 23] Min to Max0 to 270 to 270 to 270 to 270 to 270 to 272 to 272 to 272 to 27ALTAAllPlaceboAlbuterolAllPlaceboAlbuterolAllPlaceboAlbuterol N282130152233112121205102103 Mean (SD)15.4 (10.6)16.6 (10.0)14.4 (11.1)18.6 (8.7)19.3 (8.1)18.0 (9.4)21.2 (5.8)21.1 (5.6)21.2 (6.0) Median (IQR)20 [0, 24]21 [7, 24]20 (0, 24.5)22 (15 to 25)22 [17, 25]22 [13, 25]23 [19, 26]23 [17, 25]23 [19, 25] Min to Max0 to 280 to 280 to 280 to 280 to 280 to 282 to 282 to 282 to 28EDENAllFullTropicAllFullTropicAllFullTropic N1000492508806397409696351345 Mean (SD)14.9 (10.8)15.0 (10.6)14.9 (10.9)18.5 (8.8)18.6 (8.5)18.5 (9.1)21.3 (5.5)21 (5.5)21.6 (5.4) Median (IQR)20 [0, 24]19.5 [0, 24]20 [0, 25]22 (15 to 25)22 (16 to 25)22 (15 to 25)23 [19, 25]23 [18, 25]23 [20, 25] Min to Max0 to 280 to 280 to 280 to 280 to 280 to 281 to 281 to 281 to 28SAILSAllPlaceboRosuvastatinAllPlaceboRosuvastatinAllPlaceboRosuvastatin N745366379573285288515251264 Mean (SD)15.1 (10.9)15.1 (11.0)15.1 (10.8)19.6 (8.1)19.4 (8.5)19.8 (7.7)21.8 (5.2)21.9 (5.2)21.6 (5.1) Median (IQR)20 [0, 25]20 [0, 25]20 [0, 25]23 [17, 25]23 [17, 25]23 [17, 25]23 [20, 26]23 [20, 26]24 (19.5, 25.5) Min to max0 to 280 to 280 to 280 to 280 to 280 to 281 to 282 to 281 to 28a0 s due to 28-day mortality are excludedb0 s due to 28-day mortality or prolonged ventilation are excluded Figure 2 shows the observed and expected frequencies based on all four analytical distributions for VFD in HARP2. The two-part hurdle model predicts the numbers of the zeros correctly because of the model construct: zeros versus non-zero. The non-zero values peaked around 18 as per the hurdle model, while the peak in the observed values was at 25. The expected counts of the VFD were predicted based on the parameters estimated from the data. There were 187 zeros in the data. A normal distribution estimated eleven zeros, Poisson model did not predict any zeros and the ZIP model estimated 132 zeros. The hurdle model predicted all 187 zeros because the model looks at zeros and non-zero values. In the ZIP model, the proportion of excess zeros due to mortality was predicted, and the ZIP model did not predict additional zeros. The other peak in the VFD distribution was observed at value 25. The chi-square values (Table 2) indicate that none of the models was a good fit for the VFD distribution ($p \leq 0.05$), but the logit-Poisson hurdle model was comparatively better. Fig. 2Observed and expected HARP2 VFD values for different modelsTable 2Chi-square goodness of fit statistic for HARP2, ALTA, EDEN and SAILSχ2 statisticHARP2ALTAEDENSAILSOLS292.8168.20618.65486.52Poisson618.8307.071125.11849.82Zero-inflated Poisson210.9113.53378.58204.05Logit-Poisson hurdle117.154.40153.71104.70 The ZIP model considers excess zeros due to mortality in the first part and the rest of the VFD values in the second part, while the hurdle model considers all the zeros versus non-zeros as a logistic model in the first part and the second part for non-zero values in a Poisson model.
The mortality in the placebo and simvastatin groups in HARP2 was $26.9\%$ and $22.1\%$, respectively, with a difference in the mortality rate of approximately $5\%$ favouring the simvastatin. Similarly, $12.2\%$ and $8.1\%$ in the placebo and simvastatin groups required MV more than 28 days, with approximately a $4\%$ difference favouring simvastatin. After the exclusion of the zeros, the mean (SD) VFD was similar in both groups: 18.7 (6.4) for placebo and 17.8 (6.8) for simvastatin. The mean (SD) VFD was 11.5 (10.4) in the placebo group and 12.6 (9.9) in the simvastatin group; the mean difference between the groups was 1.1 ($95\%$ CI: − 0.7 to 2.8, $$p \leq 0.20$$) [13]. In the ALTA study, the mean VFD in the albuterol and placebo groups was 14.4 (11.1) and 16.6 (10.0), respectively (mean difference: − 2.2, $95\%$ CI: − 4.7 to 0.3, $$p \leq 0.087$$). In the EDEN study, the mean VFD in the trophic-feeding group and full-feeding group was 14.9 (10.9) and 15.0 (10.6), respectively (mean difference: − 0.1, $95\%$ CI: − 1.4 to 1.2, $$p \leq 0.89$$). In SAILS, the mean (SD) VFD in the rosuvastatin and placebo groups were 15.1 (10.8) and 15.1 (11.0), respectively (mean difference: 0.04, $95\%$ CI: − 1.5 to 1.6, $$p \leq 0.96$$).
Additional file 1: Table S1 shows the hurdle model estimate based on data from HARP2. There are two parts to the models, the first part is the logit model for zero versus the non-zero, and the second part is the Poisson model for the non-zero values of the VFD. The logit model indicates a $48\%$ increase in the odds of having a non-zero value of VFD if the patient is in the simvastatin group compared to the placebo group, which was statistically significant ($$p \leq 0.032$$). The count model part indicates a $4.2\%$ decrease in VFD for patients in the simvastatin group compared to the placebo group, which was not statistically significant.
Table 3 and Fig. 3 show the odds ratio (OR) and rate ratio (RR) estimates for the logit sub-model and count data sub-model, respectively, for ALTA, EDEN, HARP2 and SAILS. This shows that, in ALTA, the number of patients achieving unassisted breathing is significantly higher in the placebo than in the albuterol group, and there was no statistically significant difference in non-zero VFD between the groups. There was no statistically significant difference between the logit sub-model and count data sub-model in EDEN and SAILS.Table 3Chi-square goodness of fit statistic for HARP2, ALTA, EDEN and SAILSStudyTreatment groupHARP-2PlaceboSimvastatinEstimate ($95\%$ CI)p-value N279258 Logit-Poisson hurdle model Logistic sub-model (n (%))a170 ($61.0\%$)180 ($69.8\%$)1.47 (1.03 to 2.12)0.03 Count sub-model (Mean ± SD)b18.7 ± 6.417.8 ± 6.80.96 (0.91 to 1.01)0.09ALTAPlaceboAlbuterolEstimate ($95\%$ CI)p-value N130152 Logit-Poisson hurdle model Logistic sub-model (n (%))102 ($78.5\%$)103 ($67.8\%$)0.55 (0.32 to 0.95)0.03 Count sub-model (mean ± SD)21.1 ± 5.621.2 ± 6.01.01 (0.95 to 1.07)0.70EDENFullTropicEstimate ($95\%$ CI)p-value N492508 Logit-Poisson hurdle model Logistic sub-model (n (%))351 ($71.3\%$)345 ($67.9\%$)0.89 (0.68 to 1.17)0.40 Count sub-model (mean ± SD)21 ± 5.521.6 ± 5.41.02 (0.99 to 1.06)0.11SAILSPlaceboRosuvastatinEstimate ($95\%$ CI)p-value N366379 Logit-Poisson hurdle model Logistic sub-model (n (%))251 ($68.6\%$)264 ($69.7\%$)1.03 (0.75 to 1.40)0.88 Count sub-model (mean ± SD)21.9 ± 5.221.6 ± 5.10.99 (0.95 to 1.03)0.60aLogistic sub-model reports the n (%) of patients with non-zero values and odds ratio ($95\%$ CI) and p-value for the Logit-Poisson hurdle modelbCount sub-model reports the mean ± SD, rate ratio ($95\%$ CI) and p-value for the logit-Poisson hurdle modelFig. 3OR estimates for the logit sub-model and RR estimate for count data sub-model for ALTA, EDEN, HARP2 and SAILS
## Discussion
This paper has reviewed the utility of VFD as a valid outcome measure for critical care studies and the use of t-test for hypothesis testing. VFD penalises the worst outcome, death, by giving death the worst value, with patients who died before day 28 having the same score [0] as those who have MV ≥ 28 days, which makes the VFD one of the few composite outcomes which efficiently captures the worst component in a composite. VFD has two peaks, one at 0 and at the twenties, because of three different patient populations: those who died before day 28, those who did not achieve unassisted breathing by day 28 and those who achieved unassisted breathing by day 28. The first two groups are patients with the worst outcomes and caused a peak at zero, while many of the third group will have been extubated during their first week of MV and provided a peak VFD in the twenties. The mean values are misleading because of bimodality, and excluding all zeros raises the mean VFD from 12.0 to 18.2 in HARP2. Similarly, if those excess zeros due to deaths are excluded, the mean VFD is 15.9. These show the importance of reporting each outcome component alongside the VFD summary to provide insight into every component in the composite outcome.
The mean (SD) VFD in all patients in the HARP2 study was 12.0 (10.2), which is in the zone between the two peaks where there is the lowest frequency of occurrence. This makes the usefulness of such mean estimates doubtful. Poisson models are frequently used to analyse the duration data which are usually positively skewed. In this paper, the Poisson model showed the worst fit for VFD and did not predict any of the zeros, which means *Poisson is* not appropriate for data with excess zeros. In the VFD distribution, the true zeros are for those patients who had no MV-free days or required ventilation for at least 28 days, and the excess zeros are those who died within 28 days without achieving unassisted breathing. The ZIP model was used to deal with excess zeros and uses two simultaneous equations: one for excess zeros and another for the other VFD values. The ZIP model, with treatment as the predictor value for excess zeros and other values, produced comparable results to the two-part hurdle model and its usefulness needs to be investigated further.
The two-part logit-Poisson hurdle model is like the ZIP model, with the difference being that the hurdle model does not differentiate between true zeros and excess zeros. The first part of the model involves a logit model for zeros vs non-zeros and a Poisson model, with mean λ, for the non-zero observations. The patients receive a non-zero value once they pass the hurdle, which in VFD is being alive and achieving unassisted breathing. In HARP2, the mean difference between the placebo and simvastatin groups was not statistically significant based on the t-test, but the two-part model showed statistical significance in the logistic sub-model with more patients achieving unassisted breathing in the simvastatin than the placebo group. Similarly, in the ALTA, the results based on the t-test were not statistically significant, but based on the two-part hurdle model, the logistic sub-model showed more patients achieving unassisted breathing in the placebo than in the albuterol group. Figure 3 shows that the count data sub-model was not significantly different across the studies and this forest plot also shows that the CI for the OR estimate is wider than the RR estimate. This study did not investigate the sample size requirement or the issue of multiple testing when a two-part logit-Poisson model is used.
The heterogeneity in the definition of VFD across trials has been reported by several authors. For example, Blackwood et al. looked at sixty-six MV trials, and twenty-five trials reported VFD as an outcome. In the 16 studies which reported a definition, start and endpoints varied [21]. Contentin and colleagues reviewed 128 reports of ICU studies that reported MV duration and/or VFD as outcomes [22]. VFD was reported in fifty-five studies of which thirty-four reported a definition, and thirteen different definitions were identified. These inconsistencies reflect a lack of standardised methods among trialists to report this outcome consistently, which can result in significant problems for systematic reviews and meta-analyses. Yehya’s paper makes the following recommendations on the definition of the VFD in randomised controlled trials [8]: (i) day of randomisation should be considered as day 0, (ii) the 28-day period for VFD calculation, (iii) extubations lasting more than 48 h should be considered successful, (iv) non-invasive ventilation and tracheotomies should not be counted in the VFD calculation and (v) all 28-day non-survivors should be given a VFD of 0 with patients censored after day 28. In contrast to this, the core outcomes of the COVENT study recommend a 60-day period for duration outcomes [23]. To assess the impact of these different durations, we are planning future research to compare two-part model results based on 28-day and 60-day VFD.
## Conclusion
VFD is a frequently reported composite outcome in critical care trials. For example, of 191 critical care COVID-19-related studies registered in ClinicalTrials.gov by July 2022, about 160 of them had VFD as an outcome. This article investigated the utility of VFD for comparing the effects of interventions in such studies and evaluated the fit of Poisson, ZIP and the logit-Poisson hurdle model compared to the t-test. It showed that “zeros” can cause challenges with the analyses and that a traditional mean and SD approach is not appropriate for the VFD, which implies that the t-test is not appropriate for hypothesis testing. The Poisson distribution had the worst fit for VFD, and the two-part logit-Poisson hurdle model was the most promising approach, which allows the analysis of zeros and non-zeros simultaneously. Future research should investigate the usefulness of other techniques, such as logit-negative binomial regression and logit-quantile regression.
## Supplementary Information
Additional file 1: Supplemental Table S1. HARP2 Study: Logit-Poisson Hurdle Model.
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|
---
title: 'Emergency endotracheal intubation in critically ill patients with COVID-19:
management and clinical characteristics'
authors:
- Fuquan Fang
- Jing Jin
- Yongmin Pi
- Shaohui Guo
- Yuhong Li
- Shengmei Zhu
- Xianhui Kang
journal: Anesthesiology and Perioperative Science
year: 2023
pmcid: PMC10008717
doi: 10.1007/s44254-023-00003-9
license: CC BY 4.0
---
# Emergency endotracheal intubation in critically ill patients with COVID-19: management and clinical characteristics
## Abstract
### Purposes
SARS-CoV-2 have become widespread worldwide since the outbreak. Respiratory function deteriorates rapidly in critically ill patients infected with SARS-CoV-2. Endotracheal intubation is an indispensable therapeutic measure during the development of the disease. This study was intended to describe the experience of endotracheal intubation from front-line anesthesiologists and clinical prognosis of patients infected with Coronavirus disease-19 (COVID-19).
### Methods
Fourteen critical patients infected with COVID-19 who underwent endotracheal intubation were included in this study. We collate and analyze the blood gas results before and after tracheal intubation of patients and clinical prognostic indicators such as length of stay and. mortality. The experience of anesthesiologists who intubated patients has also been recorded in detail.
### Results
Patients had a mean time of 10.6 days from initial symptoms to endotracheal intubation. Most intubated patients had one or more underlying conditions: hypertension (8, $57.14\%$), diabetes (5, $35.71\%$), and cardiovascular and cerebrovascular diseases (2, $14.29\%$). The oxygenation index increased significantly after intubation compared with before intubation (148.80 ± 42.25 vs 284.43 ± 60.17 $p \leq 0.001$). $85.72\%$ of patients required extra-corporeal membrane oxygenation (ECMO) due to inability to maintain oxygen saturation with standard therapeutic measures. Two patients underwent lung transplantation because their lungs were essentially nonfunctional, and they recovered well after surgery. As of this writing, all patients were discharged after satisfactory recovery.
### Conclusions
Reasonable selection of intubation timing is particularly important. It is crucial to increase the patient's oxygen supply and reduce oxygen consumption as much as possible during endotracheal intubation. In addition, the personal protective measures of medical personnel participating in treatment should be scientific and standardized.
## Introduction
COVID-19 due to severe acute respiratory syndrome coronavirus 2 infection broke out in late 2019 and spread rapidly throughout the world [1, 2]. As of the time of this writing, there had been more than 100 million cases of infection and more than 2 million deaths worldwide. Studies have shown that among the COVID-19 patients in China, $14\%$ of critically ill patients had severe dyspnea, manifested as respiratory rate ≥ 30 min−1, saturation ≤ $93\%$, oxygenation index < 300 mmHg, and/or pulmonary infiltration greater than $50\%$; $5\%$ were critically ill patients, who experienced respiratory failure, sepsis, and multiple organ failure [3].
High-flow nasal cannula oxygen therapy and non-invasive positive pressure ventilation are used for the initial treatment of patients with COVID-19-associated respiratory failure [4, 5]. The World Health Organization and other institutions recommend avoiding its use in patients with continuous worsening hypoxemia, hemodynamic instability, and multiple organ failure [6, 7]. A study of 310 patients with acute respiratory distress syndrome (ARDS) showed that high-flow nasal cannula oxygen therapy did not significantly reduce the rate of endotracheal intubation [8]. Studies have reported that non-invasive positive pressure ventilation increases mortality in critically ill patients with ARDS [9–11]. This increase in mortality may be caused by a delay in the timing of endotracheal intubation. Therefore, endotracheal intubation is an essential treatment measure after respiratory deterioration in patients with severe COVID-19. Accurate grasp of the timing and criterion for endotracheal intubation is a challenge for anesthesiologists. At the same time, endotracheal intubation is one of the clinical procedures with the highest risk of close contact infection, and during the SARS epidemic in 2003, studies reported that endotracheal intubation was an important risk factor for healthcare providers to be infected through patients (odds ratio, 6.6) [12].
This study was intended to elaborate on the detailed process and preventive measures of endotracheal intubation for COVID-19 critically ill patients from front-line anesthesiologists in the first affiliated hospital, Zhejiang University school of medicine. *The* general clinical characteristics, important clinical treatment measures and related clinical outcomes of patients undergoing endotracheal intubation are also summarized.
## Patient recruitment
This is an observational case series aiming to present COVID-19 patients that underwent intubation. Fourteen critically ill patients with COVID-19 who underwent endotracheal intubation were enrolled. Due to the small number of critically ill COVID-19 patients and the small sample size of this study, the conclusions of the paper should be treated with caution. Ethical approval was provided by the Clinical Research Ethical Committee of the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China (Chairperson Prof. Youming Li, Reference Number: IIT20200307A) on 19 July 2020. The trial was registered in Chinese Clinical Trial Registry (ChiCTR2000034852) and written informed consent was obtained. Fourteen patients diagnosed with COVID-19 and underwent endotracheal intubation during treatment in the hospital were enrolled.
## Data collection and study procedures
Demographic data and medical history were recorded. Saturation of pulse oximetry (SpO2) and blood pressure(BP) during endotracheal intubation were obtained from the anesthesiologist's operating records. Arterial blood gas detection results were obtained within 1 h before and 1 h after endotracheal intubation, and the difference of partial pressure of oxygen (PaO2) and partial pressure of carbon dioxide (PaCO2) before and after endotracheal intubation was compared. The time from the onset of symptoms to endotracheal intubation in COVID-19 patients was recorded. Other important treatments, such as Continuous Renal Replacement Therapy (CRRT), Extracorporeal Membrane Oxygenation(ECMO), and lung transplantation, were also recorded. The patients' survival was followed up by telephone 1 year later.
## Statistical analysis
Statistical analysis was performed using SPSS 23. Results for continuous data are expressed as means ± standard deviation (± SD). Group comparisons of numerical data were performed by Student’s t-test or Mann–Whitney test, as appropriate. $P \leq 0.05$ was considered statistically significant.
## Characteristics of COVID-19 patients treated with endotracheal intubation
A total of 105 patients diagnosed with COVID-19 were admitted to the hospital, of whom $80\%$ were severely ill and critically ill patients, the oldest was 96 years old and the youngest was 13 years old. 14 patients underwent endotracheal intubation during treatment in the hospital.
14 patients with endotracheal intubation during treatment were aged between 36 and 90, years with a mean age of 70.15 ± 15.63, including ten patients older than 65 years, as shown in Table 1. $71.43\%$ were males. Of note, only three patients had a clear history of contact in Wuhan epidemic area. The most common initial symptoms were fever (9, $64.28\%$), cough (4, $28.57\%$). One patient had no initial clinical symptoms and was diagnosed because of a positive SARS-CoV-2 test by viral nucleic acid testing. $64.29\%$ of patients had the history of one or more underlying medical diseases: hypertension (8, $57.14\%$), diabetes (5, $35.71\%$), and cardiovascular and cerebrovascular disease (2, $14.29\%$). One of these patients was on long-term oral immunosuppressant-tacrolimus after liver transplantation. All patients had severe bilateral pulmonary infection on CT before endotracheal intubation. Table 1Baseline characteristics, major therapeutic measures and prognosis of patients with COVID-19 infection Disease/PhenotypeStatistics of Intubated patients [14]Age(years)70.15 ± 15.63Sex (M/F)$\frac{10}{4}$Wuhan residence or contact history3Hypertension8Diabetes5Malignant tumor1Cardiovascular and cerebrovascular diseases2COPD0Respiratory failure1Chronic kidney disease3Chronic liver disease1HIV0Tuberculosis2Main initial symptoms Fever9 Cough4 Diarrhea0 Asymptomatic1Airway assessment: Mallampati class(≥ 3)2Chest CT before intubation Unilateral pneumonia0 Bilateral pneumonia14ECMO12Lung transplantation2CRRT2Clinical outcome Discharged14 Death0COPD Chronic obstructive pulmoriary disease, HIV Human immuno-deficiency virus, CT Computed tomography, ECMO Extracorporeal membrane oxygenation, CRRT Continuous renal replacement therapy
## Oxygen saturation and oxygenation index before, during, and after endotracheal intubation
The intubation process was smooth in 13 patients. The lowest SpO2 during intubation was $62.45\%$ ± 13.4, and the hemodynamics during intubation was stable. All patients underwent endotracheal intubation through visual laryngoscope, and there were no complications related to endotracheal intubation. However, a patient with Mallampati class III and an indwelling gastric tube had poor mask ventilation, unsatisfactory glottic exposure. The lowest SpO2 decreased to $30\%$ during intubation. After positive pressure ventilation therapy, SpO2 was still only about $60\%$. Fortunately, the second intubation was successful. In a review of 14 intubated patients, the mean arterial pressure during intubation was 82.49 mmHg ± 10.70.
Compared with before intubation, the PaO2 increased one day after intubation (59.52 mmHg ± 7.04 vs 113.77 mmHg ± 33.26 $p \leq 0.001$). Oxygenation index (PaO2·FiO2−1) and SpO2 also significantly increased compared with those before intubation (148.80 ± 42.25 vs 284.43 ± 60.17, $p \leq 0.001$; $88.44\%$ ± 5.00 vs $96.48\%$ ± 2.44, $p \leq 0.001$). PaCO2 was significantly improved after mechanical ventilation (33.60 mmHg ± 5.27 vs 43.91 mmHg ± 10.85 $$p \leq 0.011$$), as shown in Table 2.Table 2Comparison of oxygen partial pressure, oxygenation index, partial pressure of carbon dioxide and oxygen saturation before and after endotracheal intubationIndexBefore intubationAfter intubationpPaO259.52 ± 7.04113.77 ± 33.260.001PaO2/FiO2148.80 ± 42.25284.43 ± 60.170.001PaCO233.60 ± 5.2743.91 ± 10.850.011SPO288.44 ± 5.0096.48 ± 2.440.001
## Adjuvant therapy and prognosis of patients with endotracheal intubation
The mean time from onset of symptoms to endotracheal intubation was 10.6 days in the thirteen patients who completed endotracheal intubation (one patient had no initial symptoms), of whom two were re-intubated due to significant fluctuations in SpO2 after extubation, as shown in Fig.1. Twelve patients developed pulmonary infection rapidly, the oxygenation was not improved with standard lung ventilation strategies, so they were treated with ECMO, and the mean time from the onset of symptoms to ECMO was 24.2 days. Two patients received CRRT. Fourteen intubated patients had their endotracheal tube removed, with a mean time from onset of symptoms to extubation of 23.1 days, and were discharged uneventfully, with a mean time from onset to discharge of 101.4 days. No patients died during treatment. Fig. 1Timeline of treatment events after admission of COVID-19 patients. This study included 14 patients with tracheal intubation. Only the average time from symptom onset to tracheal intubation was counted in 13 patients. This is because one patient had no initial symptoms before being diagnosed with COVID-19. ECMO, Extra-Corporeal Membrane Oxygenation; CRRT, Continuous Renal Replacement Therapy Twelve intubated patients were treated with ECMO due to poor pulmonary function, and two of them underwent successful lung transplantation assisted by ECMO with good postoperative recovery [13]. All patients were discharged after satisfactory recovery. After 1 year of follow-up, all patients were alive and had no other respiratory and cardiovascular complications except the underlying disease.
## Discussion
Reasonable selection of intubation timing is particularly important, and no premature intervention or delay in intubation can reduce the oxygen debt of COVID-19 patients. In addition to the risk of hypoxia to the patient during intubation, there is also the risk of infection to the anesthesiologist directly exposed to SARS-CoV-2. Adequate and skilled intubation preparation and intubation are needed. The preparation of endotracheal intubation includes medical equipment, fasting and water-deprivation strategy, patient preparation (evaluating airway, etc.), self-protection of healthcare provider, etc. The intubation protocol is adjusted according to the status in actual operation based on the pre-prepared protocol. The mechanical ventilation should be implemented by sticking to the principles of optimizing oxygen and minimizing lung damage. The results of this retrospective study indicate that the critical COVID-19 patients who underwent endotracheal intubation at the hospital recovered satisfactorily. Based on the experience of COVID-19 diagnosis and treatment in the First Affiliated Hospital of Zhejiang University School and related research reports, we discussed the timing of tracheal intubation, intubation method, mechanical ventilation scheme and personal protection of medical staff.
Although some patients with COVID-19 have a low oxygenation index (< 100 mmHg), their clinical symptoms are mild. Whether such patients should be immediately intubated should be comprehensively considered in clinical setting. The most important is to pay attention to the progression of their underlying disease and comprehensively assess the patient's general status, compensatory ability and disease trend. Endotracheal intubation should be performed immediately when the patient develops hemodynamic instability and deteriorated state of consciousness. Endotracheal intubation criterion: when the patient shows evidence of persistent or progressively worsening respiratory failure and at least 2 of the following criteria are met: ① respiratory rate > 40 min−1; ② no signs of improvement in high respiratory load; ③ the presence of large amounts of airway secretions; ④ acidosis (pH < 7.35); ⑤ SpO2 < $90\%$ for at least 5 min.
During the endotracheal intubation procedures, at least three health care anesthesiologists with more than five years of work experience are needed, with one respiratory therapist and one nurse additionally needed. The endotracheal intubation operation is scheduled in the negative pressure isolation ward. Anesthesiologists are required to be protected according to the Level 3 standard, shown in Table 3. However, inevitable shortcomings also follow, and the use of protective equipment and concerns about cross-infection can make the otherwise simple operation clumsy. The medical equipment and drug preparation before endotracheal intubation were similar to Professor Lingzhong Meng's regimen for first-line treatment of COVID-1 patients in Wuhan, as shown in Table 4 [12].Table 3COVID-19 Related personal protection managementProtection LevelProtective EquipmentScope of applicationLevel I protectionDisposable surgical capDisposable surgical maskWork uniformDisposable latex gloves or/and disposable isolation clothing if necessaryPre-examination triage, general outpatient departmentLevel II protectionDisposable surgical capMedical protective mask (N95) · Work uniformDisposable medical protective uniformDisposable latex glovesGogglesFever outpatient department · Isolation ward area (including isolated intensive ICU)Non-respiratory specimen examination of suspected/confirmed patientsImaging examination of suspected/ confirmed patientsCleaning of surgical instruments used with suspected/confirmed patientsLevel III protectionDisposable surgical capMedical protective mask (N95)Work uniformDisposable medical protective uniformDisposable latex glovesFull-face respiratory protective devices or powered air-purifying respiratorWhen the staff performs operations such as tracheal intubation, tracheotomy, bronchofibroscope, gastroenterological endoscope, etc., during which, the suspected/confirmed patients may spray or splash respiratory secretions or body fluids/bloodWhen the staff performs surgery and autopsy for confirmed/suspected patientsWhen the staff carries out NAT for COVID-19Notes: 1. All staff at the healthcare facilities must wear medical surgical masks; 2. All staff working in the emergency department, outpatient department of infectious diseases, outpatient department of respiratory care, department of stomatology or endoscopic examination room (such as gastrointestinal endoscopy, bronchofibroscopy, laryngoscopy, etc.) must upgrade their surgical masks to medical protective masks (N95) based on Level I protection; 3. Staff must wear a protective face screen based on Level II protection while collecting respiratory specimens from suspected/confirmed patientsTable 4Endotracheal intubation medication and equipment preparationComponentsActionBackup PlanO:OxygenEnsure an adequate supply of oxygen is availableEnsure a separate, full oxygen tank is available in the roomH:HelpersIdentify and ensure helpers are readily availableClearly understand how to obtain the needed helpM:MonitorEnsure pulse oximetry, electrocardiography, and noninvasive blood pressure monitors are functionalEnsure backup monitors are readily available, at least outside of the roomS:SuctionEnsure suction is functional and readily availableEnsure a separate (may be portable) suction is availableM:MachineEnsure an anesthesia machine or an ICU ventilator is functional and ready to goEnsure a bag-mask system (e.g., Ambu bag) capable of positive-pressure ventilation is readily availableA:Airway suppliesEnsure the video laryngoscope (e.g., GlideScope) is functional and have a direct laryngoscope as a backupHave a difficult airway cart in the room if a difficult airway is anticipated; otherwise, it should be readily available but outside of the roomI:Intravenous accessFlush and ensure functional intravenous accessHave the supplies readily available in case a new access site is neededD:DrugsHave all drugs for sedation, anesthesia induction and muscle relaxation and different vasoactive drugs preparedHave a drug tray based on the same standards for OR and ICU settingsICU Intensive care unit, OR Operating room Compared with common laryngoscope, video laryngoscope can not only expose glottis in a better way, but also increase the distance between anesthesiologist and patient airway, thus reducing the risk of cross infection in anesthesiologists. Fiberoptic bronchoscopy is only used when laryngoscopic intubation fails. Because fiberoptic bronchoscopy has a long operation time, it will increase the time of no oxygen supply to the patient. To prevent cross-contamination between patients, devices such as disposable laryngoscope blade can be used.
To prevent additional pulmonary infection in COVID-19 patients caused by aspirating gastric contents, consulting with ICU physicians regarding the duration of fasting and water-deprivation was needed. In addition, before an endotracheal intubation, the airway should be effectively assessed.
The retrospective study found that COVID-19 patients had a very poor ability to tolerate hypoxia for a short time during intubation operation. In 14 patients with COVID-19, their lowest oxygen saturation during intubation was $62.45\%$ ± 13.4. Therefore, preoxygenation before intubation appears particularly important. Most COVID-19 patients treated in the ICU of the hospital were given high-flow nasal cannula oxygen therapy or non-invasive positive pressure ventilation. If a patient has previously received nasal cannula high-flow oxygen therapy, it can be continued during endotracheal intubation after 5 min of positive pressure ventilation with $100\%$ oxygen via mask. If the patient is treated with non-invasive positive pressure ventilation, it can be continued for 5 min before intubation, in combination with high-flow nasal cannula oxygen therapy during the intubation. Continuous use of nasal cannula during tracheal intubation can increase oxygen supply, but it also increases aerosol particles production.
Endotracheal intubation is performed after safe and effective induction of anesthesia. The choice of anesthetic drugs is based on the principle of reducing patient oxygen consumption and circulatory fluctuations. Oxygen consumption can also be reduced by reducing anxiety in patients using 1—2 mg of midazolam. For patients with stable hemodynamics, induction is performed using propofol (1—1.5 ml·kg−1). Otherwise, etomidate (0.15—0.3 ml·kg−1) is a better choice. However, the incidence of etomidate-induced muscle tremor is high, which will aggravate oxygen consumption in patients. Therefore, its use should be avoided as much as possible. For the aforementioned difficult airway, we do not recommend the use of muscle relaxants. For patients with a non-difficult airway, the use of muscle relaxants can inhibit the cough reflex, thereby preventing viral particles from splashing on healthcare providers. Vecuronium (0.1—0.13 ml·kg−1) is available. Opioids can reduce hemodynamic fluctuations in patients with endotracheal intubation, but opioids can cause choking reactions and increase aerosol particle production. Intravenous injection and topical airway spraying of lidocaine can reduce this airway responsiveness. Vasoactive and cardiotonic drugs require routine backup.
In patients with a difficult airway, rapid induction is recommended. If difficult mask ventilation after induction is predicted, intubation on the basis of retention of spontaneous breathing is recommended. Patients with COVID-19 not only have poor lung function but also have increased airway secretions. Hypoxia plus secretion greatly increases the risk of laryngospasm. Sputum suction before tracheal intubation and tracheal surface anesthesia can reduce the incidence of cough reflex and laryngospasm.
Level III protection with a positive pressure head cover is recommended, which could prevent vapor generated by the head cover and goggles from blocking the field of vision. Since the medical staff wear a head cover, it is not feasible to assess the position of the tracheal tube by auscultating the patient's breath sounds. Observing endotracheal tube vapor is still feasible. After connection to the ventilator, the thoracic fluctuation and carbon dioxide waveform are observed to determine the position of the endotracheal tube. The correct position and depth of the endotracheal tube should be ensured at all times to avoid pulling of the tube and to prevent unplanned extubation. Attempts should be made to ensure the patency of artificial airway, prevent sputum and gastric contents from being sucked into the lungs and promote the discharge of airway secretions. If an endotracheal tube with a subglottic suction feature is used, intermittent suction can be performed after endotracheal intubation. Disconnection of the ventilator from the endotracheal tube should be avoided at all times.
The risk of ventilator-associated lung injury should be minimized during mechanical ventilation. To avoid man–machine confrontation, patients should be given appropriate sedation and analgesia. If necessary, muscle relaxants should be used, especially in the early stages of ventilator use. Conservative oxygen therapy strategy and conservative fluid management strategy should be adopted. Health care providers should adjust the PEEP levels reasonably and perform titration of PEEP according to patient response. Patients with obesity, increased intra-abdominal pressure, and pleural effusion may require higher PEEP levels and plateau pressure. If conditions permit (with enough guardian) and under the premise of ensuring safety, prone position ventilation is implemented for the patients with oxygenation index of less than 100. It is recommended to maintain the prone position for 12 ~ 16 h·d−1.
When a doctor extubated for a COVID-19 patient in the (Intensive care unit) ICU of the hospital, his arm was bitten by the patient, as shown in Fig. 2. Fortunately, the patient’s viral nucleic acid detection was negative, and the doctor was in personal protective equipment, which was not broken after the biting. Wearing personal protective equipment is important at the time of extubation. Extubation can be dangerous if the patient is irritable with the delirium symptoms. The use of the sedative agents prior to extubation is recommended to prevent irritability and delirium. Dexmedetomidine (0.4 mcg·kg−1·h−1) or remifentanil (1 to 4 ng·ml−1 target blood concentration) have been recommended. Fig. 2A doctor bitten by a COVID-19 patient during extubation
## Conclusions
Reasonable selection of intubation timing is particularly important. It is crucial to increase the patient's oxygen supply and reduce oxygen consumption as much as possible during endotracheal intubation. In addition, the personal protective measures of medical personnel participating in treatment should be scientific and standardized.
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|
---
title: 'Unravelling the potential of social prescribing in individual-level type 2
diabetes prevention: a mixed-methods realist evaluation'
authors:
- Sara Calderón-Larrañaga
- Trish Greenhalgh
- Megan Clinch
- John Robson
- Isabel Dostal
- Fabiola Eto
- Sarah Finer
journal: BMC Medicine
year: 2023
pmcid: PMC10008720
doi: 10.1186/s12916-023-02796-9
license: CC BY 4.0
---
# Unravelling the potential of social prescribing in individual-level type 2 diabetes prevention: a mixed-methods realist evaluation
## Abstract
### Background
Social prescribing (SP) usually involves linking patients in primary care with services provided by the voluntary and community sector. Preliminary evidence suggests that SP may offer a means of connecting patients with community-based health promotion activities, potentially contributing to the prevention of long-term conditions, such as type 2 diabetes (T2D).
### Methods
Using mixed-methods realist evaluation, we explored the possible contribution of SP to individual-level prevention of T2D in a multi-ethnic, socio-economically deprived population in London, UK. We made comparisons with an existing prevention programme (NHS Diabetes Prevention Programme (NDPP)) where relevant and possible. Anonymised primary care electronic health record data of 447,360 people 18+ with an active GP registration between December 2016 and February 2022 were analysed using quantitative methods. Qualitative data (interviews with 11 primary care clinicians, 11 social prescribers, 13 community organisations and 8 SP users at high risk of T2D; 36 hours of ethnographic observations of SP and NDPP sessions; and relevant documents) were analysed thematically. Data were integrated using visual means and realist methods.
### Results
People at high risk of T2D were four times more likely to be referred into SP than the eligible general population (RR 4.31 ($95\%$ CI 4.17–4.46)), with adjustment for socio-demographic variables resulting in attenuation (RR 1.33 ($95\%$ CI 1.27–1.39)). More people at risk of T2D were referred to SP than to NDPP, which could be explained by the broad referral criteria for SP and highly supportive (proactive, welcoming) environments. Holistic and sustained SP allowed acknowledgement of patients’ wider socio-economic constraints and provision of long-term personalised care. The fact that SP was embedded within the local community and primary care infrastructure facilitated the timely exchange of information and cross-referrals across providers, resulting in enhanced service responsiveness.
### Conclusions
Our study suggests that SP may offer an opportunity for individual-level T2D prevention to shift away from standardised, targeted and short-term strategies to approaches that are increasingly personalised, inclusive and long-term. Primary care-based SP seems most ideally placed to deliver such approaches where practitioners, providers and commissioners work collectively to achieve holistic, accessible, sustained and integrated services.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02796-9.
## Background
Social prescribing (SP) usually involves linking patients in primary care with services provided by the voluntary and community sector (VCS) [1]. Activities are typically non-medicalised, provided locally and have a wide remit (from lifestyle programmes to welfare advice and/or community engagement initiatives, depending on patients’ needs and local availability) [2, 3]. In the UK, the link between primary care and the VCS is facilitated by a link worker (also referred to as a social prescriber), whose role ranges from signposting to patients’ needs assessment, ongoing support or the development of new VCS activities where gaps exist [4]. As part of the NHS Long-Term Plan, NHS England set to recruiting enough link workers to make the service available in every GP practice by $\frac{2023}{2024}$ [5, 6].
SP is expected to advance the prevention and management of long-term conditions, such as type 2 diabetes (T2D) [7, 8], by encouraging a healthier lifestyle, self-management and personalised care [9–11]. However, the current evidence base for SP and its role in specific areas of health need, such as T2D, is scarce. This study focused on T2D prevention and investigated the role of primary care-based SP in people at high risk of the condition based on the following considerations. First, T2D is a major public health concern; it is common (and increasingly so) and is associated with reduced quality of life, life expectancy and considerable socio-economic consequences [12–14]. Both individual behavioural risk factors and socio-economic determinants appear to be major driving forces behind escalating T2D epidemics and health inequalities [15, 16]. Second, many community-based activities accessed through SP focus on healthy lifestyle, including weight management, dietary recommendations and physical activity [1, 17–19], which also underpin existing T2D prevention behavioural programmes, such as the NHS Diabetes Prevention Programme (NDPP) [20]. However, by also acknowledging people’s underlying social constraints, SP may offer a means of providing contextually sensitive and holistic health promotion [21], in line with best practice recommendations for individual-level T2D prevention [22]. Third, NDPP focuses purely on the promotion of behaviour change in patients at high risk of developing diabetes. Delivered at scale through (mostly private) independent providers, NDPP has shown low uptake and high attrition rates, especially amongst socio-economically deprived and diverse ethnic groups [23–25]. There is thus a need for T2D prevention strategies to increase attention to patients’ wider social context and improve their reach to those in greatest need.
This study aimed to investigate the possible role of SP in T2D prevention and evaluate the extent to which it may complement and inform existing preventative approaches (NDPP). We hypothesised that the reach and equity of access of SP and NDPP across high-risk patients within a multi-ethnic, socio-economically deprived population could differ and sought to understand why (and how) possible differences could occur. Using a realist mixed-methods design, we investigated, first, whether (and the extent to which) SP might reach high-risk patients in greatest health and social need; second, what “good” practice in SP relevant to people at high risk of T2D might look like; and third, key ingredients of SP that might contribute (or not) to T2D prevention.
## Methods
Quantitative and qualitative data were collected and analysed concurrently between November 2020 and March 2022 (see Fig. 1 for an overview of data sources and analysis) [26]. We followed RAMESES reporting standards for realist evaluations [27].Fig. 1Overview of study data sources and analysis. Legend: SP Social Prescribing; NDPP NHS Diabetes Prevention Programme; T2D Type 2 Diabetes; VCS Voluntary and Community Sector
## Theoretical approach: realist evaluation
This study used mixed-methods realist evaluation [28], informed by a previous realist synthesis undertaken by this team [21]. Realist evaluation is a theory-driven methodology that seeks to facilitate a deep understanding of how complex interventions, such as SP, work and in what circumstances [29]. Causal narratives are central to realist evaluation but have a wider conception that goes beyond the criterion of observability to also account for the non-physical and unobservable—for example, the conscious or unconscious reasoning which drives individuals’ decisions and actions [30]. In order to explore these underlying causal explanations, realist methodology is designed to tease out what are known as context-mechanism-outcome configurations (CMOCs) [31]. A CMOC is a hypothesis that the programme works (or does not work) (O) because of the action of some underlying mechanisms (M), which come into operation only in particular contexts (C) [32]. These causal explanations (also referred to as programme theories [33]) are made explicit and tested, confronted and refined iteratively throughout the research using a range of empirical data [27].
Realist evaluations have been successfully used to illuminate the resources (mechanisms within specific contexts) which influence the nature and potential impact of complex interventions, such as SP [34, 35]. This study builds on existing published literature using realist methodology to provide a novel evaluation of the potential role of SP in a specific context of preventative health (relating to T2D). Realist evaluation also provides a useful framework for mixed-methods research, whereby quantitative data may help identify patterns of SP practice that are then explored further using rich qualitative insights from SP users and service providers (or vice versa) [36].
## Study setting and intervention
The study was based in Tower Hamlets, a multi-ethnic inner-city borough in east London, UK, with an estimated population of 310,300. Tower *Hamlets is* one of the most deprived boroughs in the UK, characterised by its great ethnic diversity. Overall and T2D-related health outcomes are significantly poorer than the national average despite high-quality primary and secondary care [37–39].
Tower Hamlets has been at the forefront of SP implementation and delivery and has a long-established local VCS. Since 2016, all patients registered with a Tower Hamlets GP practice, aged over 18 and expressing a “non-clinical” support need are eligible for the local SP programme [18]. In line with NHS requirements for SP roll-out and delivery, people can self-refer or be referred by any local primary care professional to their named link worker, who is usually located in the GP practice with full access to patients’ medical records [11]. Based on the identified needs, link workers may offer signposting or referral to relevant VCS resources, and/or further face-to-face or telephone follow-up appointments. Most VCS activities accessed through SP were related to lifestyle (such as exercise, healthy eating and weight management) or welfare advice (such as debt, benefits, housing issues), while the duration of activities and support varied across different services and organisations [18]. During the early months of the COVID-19 pandemic, the service had to adapt its working practices to factors such as remote-by-default policies for GP access, working from home and local agencies providing limited or restricted services. All link workers were provided with remote access to medical records and homeworking equipment to ensure service continuation and the provision of phone and/or video appointments to referred patients.
Tower Hamlets was also one of the 27 areas across the country chosen to be part of the first wave roll-out of NDPP in 2016. The programme targets individuals at high risk of T2D, demonstrated by a diagnosis of non-diabetic hyperglycaemia or previous gestational diabetes. It is delivered by a private provider and commissioned by NHS England, with referral volume used as the main metric of site activity. General practice involvement is limited to the identification and referral of patients, enhanced through various incentives and support strategies. The core NDPP intervention consists of group-based sessions offering behavioural change content intended to achieve improvements in diet, physical activity levels and weight. The course consists of a minimum of 16 h of contact time over at least 9 months [20]. During the pandemic, sessions were held online or over the phone, depending on patients’ preference.
## Quantitative data
The study population was all adults (18+ years) without diabetes, registered with a GP in any of 35 practices in Tower Hamlets, east London, between 1 December 2016 and 14 February 2022. Electronic health record data were accessed from these GP practices via the Clinical Effectiveness Group, Queen Mary University of London, and pseudo anonymised at source under NHS and local information governance and data security policies. Detailed inclusion and exclusion criteria and study variables are presented in Additional file 1: Table S1.
We undertook a cohort study [1] of people eligible for SP (18+ and with an active GP registration in Tower Hamlets) to investigate the independent association between being at high risk of T2D and being referred into SP. This population was selected using the latest date of GP registration or the study period start date. In a second cohort study [2], we investigated the clinical and socio-demographic features associated with being referred into SP in a population at high risk of T2D. In this study, high risk of T2D was determined as history of gestational diabetes, Q Diabetes risk calculator score (QDiabetes®-2018 [40]) equal or above to 20, fasting blood glucose 5.5–6.9 mmol/L, HBA1c 42–47 mmol/mol, diagnosis of non-diabetic hyperglycaemia and/or pre-diabetes or history of referral into NDPP. We selected this population as all those in a T2D high-risk state, using the latest date of diagnosis, date of GP registration or study period start date. We excluded patients with diabetes if they had been diagnosed prior to the end of follow-up. People in both cohort studies were censored at the earliest date of the coded referral into SP (primary outcome), death, deregistration with the practice or the study end date. Finally, we undertook a cross-sectional study to investigate the clinical and socio-demographic features associated with a coded referral into SP, NDPP, both or neither amongst people eligible for NDPP. The exposures and outcomes for each of these studies were described using percentages (categorical variables) or median and IQR (continuous variables). Poisson fixed effect models were used to calculate all rate and rate ratios (RR) ($95\%$ CI) in cohort studies 1 and 2. Fixed effect multinomial regression models were used to calculate all odds and odd ratios (OR) ($95\%$ CI) comparing referral into SP with referral into NDPP (baseline category) in the cross-sectional study [3]. Potential confounders were included in the multivariate models to calculate the independent association of the different risk factors with being referred to SP and/or NDPP. Models were constructed based on the hierarchical relationship of variables established in a conceptual framework [41], previous assumptions on causality (determined using a direct acyclic graph (DAG) [42]) and assessment of the correlation between independent variables (collinearity). All analyses were conducted using Stata (version 17).
## Qualitative data
We collected different types of qualitative data to evaluate the actual delivery of SP, including key ingredients that could explain its potential in reaching high-risk patients with greatest health and social need. Qualitative data sources and sample characteristics are shown in detail in Additional file 2: Tables S1-S5. Briefly, we drew on [1] semi-structured interviews with 8 SP users at high risk of T2D, 11 primary care clinicians (GPs, nurses, a physiotherapist and health care assistant), 11 link workers and 13 local VCS organisations accessed through SP; [2] 36 hours of ethnographic observations of community-based SP activities (including holistic weight management and physical activity programmes), privately delivered NDPP sessions and VCS meetings; and [3] documents about the local SP scheme, NDPP programme and VCS organisations (including descriptions of activities on offer, target population, eligibility criteria and evaluation reports). Interviews and observations were initially conducted remotely (online or over the phone) and later shifted to face to face in compliance with COVID-19 safety measures. The sample size was determined iteratively guided by data saturation. Interview transcripts, fieldnotes and documents were initially analysed thematically. We combined a broadly deductive analytic approach (to test and refine previous realist review findings) with a more inductive analysis (to explore new, unexpected findings related to the specific intervention and local contexts) [43]. Data were analysed in six iterative stages (repeated reading, development of initial codes, generation, review and naming of themes, and writing), guided by the framework proposed by Braun and Clarke [44]. We sought negative cases (including contradictory findings and inconsistencies across and within data) and triangulated within the research team to enhance validity and rigour [45]. Data management was supported by NVivo V.10 software.
## Integration of quantitative and qualitative findings
Data were integrated using visual means (also referred to as joint display integration [26]), which enabled drawing new insights beyond the information gained from the separate quantitative and qualitative results [46]. Referral patterns and practices were further contextualised in light of rich qualitative data to develop explanations of how and why they occurred and their meaning for people at high risk of T2D. At this point, a realist logic of analysis was applied, which involved making inferences about whether different components of the data were functioning primarily as context, mechanism or outcome (and the relationships between them) [32]. CMOCs were further reviewed, refined and tested against empirical data, our previous realist review [21] and through discussion with the research team and stakeholders.
## Results
The extent to which SP succeeded in reaching high-risk people (quantitative findings) and the mechanisms by (and contexts in) which this was achieved (qualitative findings) are explained below.
## Referral and non-referral to social prescribing in a population at high risk of type 2 diabetes: quantitative findings
A total of 447,360 people eligible for SP were enrolled between December 2016 and February 2022. Over a median follow-up period of 4.5 years, 15,450 referrals into SP were observed (1,604,194 person-years). As shown in Table 1, people referred into SP were more likely to be female (RR 1.74 ($95\%$ CI 1.68–1.80)), socio-economically deprived (RR 2.18 ($95\%$ CI 1.97–2.40)), black (RR 2.02 ($95\%$ CI 1.90–2.15)), South Asian (RR 2.27 ($95\%$ CI 2.18–2.36)) or Arab (RR 2.54 ($95\%$ CI 2.07–3.13)) than the general population eligible for the service. Those at high risk of T2D were four times more likely to be referred to SP (RR 4.31 ($95\%$ CI 4.17–4.46)), with adjustment for socio-demographic variables attenuating the association (RR 1.33 ($95\%$ CI 1.27–1.39)). Similarly, people living with long-term conditions (including cardiovascular diseases (RR 4.67 ($95\%$ CI 4.35–5.01)), obesity (RR 3.15 ($95\%$ CI 3.05–3.26)), mental health conditions (RR 4.70 ($95\%$ CI 4.54–4.85))) and multimorbidity (RR 5.53 ($95\%$ CI 5.35–5.71)) were at higher risk of SP referral, which also attenuated with adjustment for socio-demographic variables (see Fig. 2 and Additional file 1: Table S2).Table 1Distribution of socio-demographic characteristics within the total study population and their association with referral into SP, cohort study 1VariablesTotalN 447,360 (%)Events (SP)N 15,454 (%)Rates per 1000 P/YRR ($95\%$ CI)P valueRRa ($95\%$ CI)P valueGenderMale219,795 (49.1)5990 (38.8)7.5 (7.3–7.7)1<0.0011<0.001Female227,498 (50.9)9462 (61.2)11.8 (11.5–12.0)1.57 (1.52–1.62)1.74 (1.68–1.80)Missing67 (0.01)2 (0.01)AgeMedian (IQR)33 [28, 42]43 [33, 57]EthnicityWhite184,631 (41.3)5031 (32.6)7.5 (7.3–7.7)11South Asian93,722 (21.0)5513 (35.7)15.5 (15.1–15.9)2.07 (1.99–2.15)<0.0012.27 (2.18–2.36)<0.001Chinese16,191 (3.6)77 (0.5)1.4 (1.1–1.8)0.19 (0.15–0.24)<0.0010.25 (0.20–0.32)<0.001Black18,298 (4.1)1290 (8.4)19.1 (18.1–20.2)2.55 (2.40–2.72)<0.0012.02 (1.90–2.15)<0.001Arab1931 (0.4)91 (0.6)16.6 (13.6–20.4)2.22 (1.81–2.73)<0.0012.54 (2.07–3.13)<0.001Mixed/others23,491 (5.3)694 (4.5)9.1 (8.5–9.9)1.22 (1.23–1.32)<0.0011.37 (1.26–1.48)<0.001Missing109,096 (24.4)2758 (17.9)IMD quintiles1st and 2nd (most deprived)371,964 (83.2)14,167 (91.7)10.6 (10.4–10.8)2.13 (1.98–2.28)<0.0012.18 (1.97–2.40)4th and 5th (least deprived)31,064 (6.9)462 (3.0)4.1 (3.6–4.5)11Missing3372 (0.8)15 (0.1)IMD Index of Multiple Deprivation, IQR interquartile rangeaAdjusted by the remaining socio-demographic variables (gender, ethnicity, IMD) and time variables (age, year)Fig. 2Distribution of clinical features and their association with referral into SP among the total study population, cohort study 1. Legend: T2D Type 2 Diabetes. Cardiovascular disease includes Ischemic Heart Disease, Peripheral Arterial Disease, and/or Stroke and Transient Ischemic Attack. Respiratory condition includes Asthma and/or COPD. See Additional File 1: Table S2 for information on the variables included in each adjusted model Of the total population eligible for SP, $9.2\%$ [41,378] were identified as high risk of T2D and were, therefore, included in cohort study 2. The median follow-up period for these high T2D risk individuals was 5.2 years, which resulted in 5226 referrals into SP (164,614 person-years). The pattern of findings for high-risk patients was similar to the general population. As shown in Table 2, those referred into SP were more likely to be female (RR 1.54 ($95\%$ CI 1.46–1.64)), socio-economically deprived (RR 1.83 ($95\%$ CI 1.53–2.19)) and South Asian (RR 1.16 ($95\%$ CI 1.08–1.25)). Similarly, living with cardiovascular disease (RR 1.43 ($95\%$ CI 1.31–1.55)), obesity (RR 1.30 ($95\%$ CI 1.23–1.37)), mental health conditions (RR 2.31 ($95\%$ CI 2.18–2.45)) or multimorbidity (RR 1.98 ($95\%$ CI 1.88–2.10)) significantly increased the risk of being referred into SP. Adjustment for confounders, however, resulted in a lower attenuation, suggesting greater homogeneity in terms of socio-demographic features across this high-risk sample (see Fig. 3 and Additional file 1: Table S3). In all cases, the referral rate was higher at older ages and during the first year of service roll-out (December 2016–December 2017) (Additional file 1: Tables S4 and S5).Fig. 3Distribution of clinical features and their association with referral into SP among people at high risk of T2D, cohort study 2. Legend: See Additional File 1: Table S3 for information on the variables included in each adjusted modelTable 2Distribution of socio-demographic characteristics within the study population at high risk of T2D and their association with referral into SP, cohort study 2VariablesTotalN 41,378 (%)Events (SP)N 5226 (%)Rates per 1000 P/YRR ($95\%$ CI)P valueRRa ($95\%$ CI)P valueGenderMale20,419 (49.4)2127 (40.7)25.5 (24.5–26.7)1<0.0011<0.001Female20,957 (50.8)3099 (59.3)38.1 (36.8–39.5)1.49 (1.41–1.58)1.54 (1.46–1.64)Missing2 (0.0)0 (0.0)AgeMedian (IQR)52 [43, 64]55 [45, 66]EthnicityWhite11,253 (27.2)1494 (28.6)32.0 (30.4–33.6)11South Asian19,406 (46.9)2469 (47.2)32.7 (31.4–34.0)1.02 (0.96–1.09)0.4891.16 (1.08–1.25)<0.001Chinese533 (1.3)23 (0.4)10.6 (7.1–16.0)0.33 (0.22–0.50)<0.0010.36 (0.24–0.64)<0.001Black3275 (7.9)469 (9.0)35.1 (32.0–38.4)1.10 (0.99–1.22)0.0801.10 (0.99–1.23)0.069Arab142 (0.3)24 (0.5)43.8 (29.3–65.3)1.37 (0.92–2.05)0.1261.49 (0.99–2.24)0.053Mixed/others1352 (3.3)177 (3.4)33.9 (29.3–39.3)1.06 (0.91–1.24)0.4601.11 (0.95–1.30)0.178Missing5417 (13.1)570 (10.9)IMD quintiles1st and 2nd (most deprived)37,283 (90.1)4878 (93.3)32.9 (32.0–33.9)1.90 (1.59–2.27)<0.0011.83 (1.53–2.19)<0.0014th and 5th (least deprived)1813 (4.4)127 (2.4)17.4 (14.6–20.6)11Missing31 (0.1)3 (0.1)IMD Index of Multiple Deprivation, IQR interquartile rangeaAdjusted by the remaining socio-demographic variables (gender, ethnicity, IMD) and time variables (age, year) Sixty-seven per cent [27,928] of people at high risk of T2D met the eligibility criteria for NDPP and were, therefore, included in our cross-sectional study. Of these people, $11\%$ [3015] had been referred to SP, $10\%$ [2899] to NDPP and $2\%$ [518] to both services. As shown in Table 3, patients referred into SP were significantly more likely to be female (OR 1.99 ($95\%$ CI 1.78–2.24)) and socio-economically deprived (OR 2.12 ($95\%$ CI 1.56–2.88)) than those referred into NDPP, but less likely to be South Asian (OR 0.48 ($95\%$ CI 0.41–0.56)), black (OR 0.46 ($95\%$ CI 0.37–0.56)) or Chinese (OR 0.14 (0.07–0.27)). Supporting the findings from previous cohort studies, people diagnosed with mental health conditions (OR 3.25 ($95\%$ CI 2.86–3.69)) or multimorbidity (OR 1.80 ($95\%$ CI 1.63–2.00)) were also more likely to be referred into SP. Adjustments for relevant variables did not alter the results substantially (Fig. 4 and Additional file 1: Table S6).Fig. 4Distribution of clinical features and their association with referral into SP amongst people eligible for NDPP, cross-sectional study. Legend: See Additional File 1: Table S6 for information on the variables included in each adjusted modelTable 3Distribution of socio-demographic characteristics amongst patients eligible for NDPP and their association with referral into SP compared to NDPP, cross-sectional studyVariablesNo referral21,483 (%)Only SPN 3028 (%)Only NDPPN 2899 (%)BothN 518 (%)OR ($95\%$ CI)P valueORa ($95\%$ CI)P valueGenderMale10,006 (46.6)1182 (39.0)1587 (54.7)213 (41.1)11Female11,476 (53.4)1846 (61.0)1311 (45.2)305 (58.9)1.38 (1.28–1.50)<0.0011.99 (1.78–2.24)<0.001Missing1 (0.01)01 (0.1)0Age median (IQR)49 [39, 60]53 [43, 63]52 [44, 61]54 [46, 64]1.00 (0.99–1.01)0.1290.99 (0.99–1.01)0.590EthnicityWhite5689 (26.5)831 (27.4)490 (16.9)92 (17.8)11South Asian9873 (46.0)1465 (48.4)1542 (53.2)266 (51.4)0.56 (0.49–0.64)<0.0010.48 (0.41–0.56)<0.001Chinese342 (1.6)12 (0.4)46 (1.6)6 (1.2)0.15 (0.08–0.29)<0.0010.14 (0.07–0.27)<0.001Black1664 (7.8)259 (8.6)289 (10.0)58 (11.2)0.53 (0.43–0.65)<0.0010.46 (0.37–0.56)<0.001Arab76 (0.4)13 (0.4)7 (0.2)2 (0.4)1.10 (0.43–2.76)0.8481.03 (0.41–2.62)0.944Mixed/others775 (3.6)104 (3.4)81 (2.8)18 (3.5)0.76 (0.55–1.03)0.0800.68 (0.50–0.93)0.017Missing3064 (14.3)344 (11.4)444 (15.3)76 (14.7)IMD quintiles1st and 2nd (most deprived)19,092 (88.9)2838 (93.7)2631 (90.7)477 (92.1)1.78 (1.33–2.39)<0.0012.12 (1.56–2.88)<0.0014th and 5th (least deprived)1115 (5.2)74 (2.4)122 (4.2)17 (3.3)11Missing19 (0.1)3 (0.1)4 (0.1)0 (0.0)OR comparing referral into only SP with referral into only NDPP (baseline category)IMD Index of Multiple Deprivation, IQR interquartile range, NDPP NHS Diabetes Prevention ProgrammeaAdjusted by the remaining socio-demographic variables (gender, age, ethnicity, IMD)
## How did social prescribing contribute to meeting the complex health and social needs of people at high risk of type 2 diabetes? Qualitative findings
We identified the following four mechanisms through which SP operated to reach high-risk patients with greatest health and social need.
## Accessible social prescribing: type 2 diabetes prevention as an inclusive and proactive care process
Unlike NDPP, SP had broad eligibility criteria (any patient 18+ and registered with a local GP could be invited). Lack of requirements for tests or medical assessments prior to a referral made the service easier to consider by referrers during routine consultations: “there’s no restrictions. We don’t have like; your blood pressure has to be this. Your weight has to be this. We can just refer” [Nurse 01.8]. Although patients were not necessarily referred based on their T2D risk, they often ended up accessing services relevant to its prevention: “[…] he was pre-diabetic and he was very, very overweight, which wouldn’t surprise me because often these conditions go together. Through [name of physical activity programme] and the support he got, he lost a lot of weight. His sugar levels were much more normal, things like that. I think that’s a good example of where he wasn’t actually there for the diabetes side but actually it really helped him more generally” [VCS 03.9].
Our study also revealed that patients often lacked confidence and felt guilty or helpless to reach out and access the services they required. As expressed by a patient referred into SP: “I know how to contact her [the link worker] but you know, […] I don’t want to feel that you know – I feel a bit guilty really that I didn’t follow up on a lot of the information that she gave me” [SPU 04.1]. Additional support was often needed to help them navigate (and reach) SP. Strategies included scheduling regular follow-ups with link workers, filling in referral forms in primary care (instead of signposting or encouraging self-referrals) or creating friendly and welcoming environments in the VCS with the help of volunteers, buddy systems and/or information packages, amongst others. However, the relationship between the provision of support and patients’ access or engagement was non-linear and hence unpredictable. Patients often failed to respond, did not turn up to sessions or refused to carry on despite these supportive environments. Making services accessible in such situations often relied on providers’ capacity (and willingness) to be tenacious and attentive: “She persevered, even though there would have been times when I did not pick up that phone and I did not want to talk to [name of link worker] or anyone. Yet again she would try and she would always inform me that ‘I could not get hold of you today, I will try in another five or six days’ time’ and she always kept her promise” [SPU 04.3]. Accessibility was no longer a static service attribute, but rather a proactive (creative and ongoing) process through which providers tried to overcome existing barriers and find ways to bring services closer to the patient.
## Holistic social prescribing: type 2 diabetes prevention as a dynamic and personalised practice
Providers throughout the SP referral pathway proactively explored patients’ wider socio-economic circumstances in search of concerns influencing their wellbeing and clinical presentations. Open conversations led to diverse courses of action, depending on the identified priorities. This broadened the scope and understanding of T2D prevention beyond lifestyle recommendations to also include services related to employment, housing or welfare advice, amongst others: “It’s not just about ‘I want to change my diet’, it will be looking at the barriers to them changing the diet. We might refer for the diet and exercise classes, but we’ll also refer to English classes and things like that” [Nurse 01.8]. Providers tried to widen and diversify their service remit to better accommodate patients’ multiple, intertwined needs (e.g. by providing in-house legal advice alongside physical activity programmes, up-skilling link workers on relevant domains (such as health coaching, welfare advice) or even bringing welfare advisors and lifestyle programmes into GP practices): “for example on a Thursday it’d be the advisor here and the receptionist would say, come and see the advisor in the surgery at that time. They’d make an appointment with them in the same way you’d make an appointment with the nurse. That’s excellent service” [VCS 03.9].
Instead of adhering to established, pre-defined ways of working, we identified joint attempts to work around patients’ complex life circumstances and adapt services accordingly. This involved prioritising patients’ context and the provision of support over the specific content and consistency of lifestyle recommendations: “It’s not specifically saying, this is what you must do for cholesterol. This is what you must do for diabetes. Very often the advice is the same anyway. […] the issue is not about the actual specific condition, it’s more about getting that peer support to help that person manage whatever’s going on for them in their life” [VCS 03.9]. Some patients needed “taking” (“I took patients on walking groups [...], just so that they go. […] I’ve taken a patient to an ESOL class because she didn’t want to go alone. So, I just took her to the first one” [LW 02.5]), while others needed to be listened to (“Sometimes also we receive referrals where they do not want any help; all they want is someone to listen to them” [LW 02.8]) or somebody to whom they could feel accountable (“I just needed somebody in a way to whom I could be answerable, if that sounds strange” [SPU 04.4]). Some patients preferred medicalised preventative approaches (where information about disease risk and anthropometric measurements were considered), while others responded better to “subtle” (opportunistic) lifestyle recommendations or regular follow-ups by health care assistants in primary care (instead of being referred into community-based lifestyle programmes).
## Sustained social prescribing: type 2 diabetes prevention as an ongoing and unpredictable struggle
Patients reported that their capacity to follow a “healthy” lifestyle fluctuated over time, highly conditioned by the amount and consistency of support they received. Many patients had tried different weight management or physical activity programmes, managed to lose weight while being supported and then relapsed as the intensity of interventions decreased. Critically, the lack of a long-lasting response was interpreted by patients as a personal failure rather than a deficiency of the services in place or a consequence of underlying structural constraints (e.g. poverty, food insecurity, obesogenic environments, etc.): “ when I did the [name of weight management programme] I’d gone down to 97kgs and that’s the best I ever did in my entire life. In the entire 35 years of my life that was the best I’d done and I felt great and I was a good size 18 and I was so happy. Then within stopping that programme because it’s again, for me, I think I lack and I don’t think it’s the services, for me I start something but […] I never sustain that, I never maintain anything. So, […] within a year I put all that back on plus more. So, it’s been very difficult” [SPU 04.3].
Regular services, conversely, allowed for the provision of ongoing support. Patients could build on previous work and share the burden of (and, hence, better cope with) critical social and health constraints: “I just felt a little bit better that I’m not dealing with this on my own” [SPU 04.3]. Continuity of care allowed providers to monitor patients’ progress (or lack thereof) and adapt their approach and next steps accordingly. “ Testing and trying” involved making the erratic nature of prevention, as well as the limitations of existing interventions explicit beforehand. Knowing that interventions could fail, be insufficient or inappropriate prevented unrealistic expectations and shifted the responsibility of any potential failure from the individual to the intervention: “So, I think what the prescriber was saying was ‘my resources are limited and you could go to this walk-in therapy and hate it and that’s okay, you’d come back and tell me’. But I think so often people are referred to a social prescriber and they take up one offer and that doesn’t work and so they think nothing will work” [SPU 04.4].
Ongoing services allowed for the development of meaningful (“therapeutic”, “trustful”) relationships with patients, across sectors and amongst attendees in group sessions: “a lot of people have been coming for 10 plus years, so they’ve built up these really good bonds with each other. So, not only are we looking out for them but they’re looking out for each other” [R1 VCS 03.2]. Patients were made aware that “there [was] somebody there for them” [LW 02.5], which proved reassuring and had therapeutic effects by itself: “just knowing that there’s people available for us” [SPU 04.2]. It counteracted feelings of helplessness and mistrust towards a system that had previously let them down (“if [the service] stops it’s like, ‘oh they’re the same as everyone’.” [ VCS 03.8]) and provided some stability within a context of great service and staff turnover (both within the health sector and the VCS): “with many services in the community coming and going, […] even in the NHS as well, programmes with different names each year, I think there’s real benefit in having someone stable, by having a known person in a GP surgery that they can come to whenever there is a change in their life situation because that often provides that window of opportunity to really change something” [LW 02.2].
## Integrated social prescribing: type 2 diabetes prevention as a locally embedded and joint endeavour
Bidirectional communication across sectors and among professionals allowed providers to develop greater knowledge on patients’ needs, “build on other’s work” and deliver consistent care. Clinicians became increasingly aware of patients’ “underlying story” and able to adapt care practices accordingly because of the information that link workers had shared: “I quickly realised how much the GPs are really struggling to know what the story is behind the medical record and […] just by sharing a few lines, […] ‘well actually this person had an accident last year or lost their job’, just very simple things that make us able to humanise people and their choices” [LW 02.2]. Patients’ medical notes provided link workers with “a two-sided approach” and relevant “context” (“background information”) over which they could build their assessment and recommendations. Similarly, VCS organisations often received key information with or prior to a referral on how best to support a patient by avoiding specific “triggers” that had already been disclosed. Communication channels across and within sectors were also used to flag those patients requiring closer, urgent attention (“Usually, they give us one- or two-lines feedback […] There’s also something on the records […] and, for very specific people that I’m maybe a bit more worried about, I may just ask them to let me know what has happened” [GP 01.1]) and/or reach out if they had not been contacted. Service providers became effective advocates by ensuring patients were not left behind and received the support they required: “When I felt depressed about my weight and not getting any contact from the weight management programme, [name of link worker] was right on it sending emails and making telephone calls, whilst I was on the phone by the way, and getting in touch with them and saying ‘look, I’ve got a patient here named… she’s waited for that number of weeks, why has nothing come through?’ So, I felt like the support was first-hand” [SPU 04.1].
Integration (and embeddedness) within the local community and primary care system also enhanced the scope and responsiveness of services. Patients at risk of T2D ended up accessing physical activity programmes that would have been difficult to locate had it not been for the SP network (“any kind of exercise programmes that we have running or any health sessions we try and send out to the GPs and that’s where a lot of our referrals came from” [VCS 03.2]). Link workers informed the design of VCS activities (e.g. lifestyle programmes, physical activity sessions) by sharing relevant information on identified needs and facilitated their development in local GP practices: “we would often run services at GP surgeries to increase accessibility. For example, [name of GP practice] has a room, we’d meet in there” [VCS 03.9]. Clinicians and link workers often worked together (and learnt from each other) to support patients with complex needs: “if you’re having any difficult patients, challenging, you can bounce off or you can say, ‘How are you dealing with that patient?’ Maybe in a different way and you see whether you can make sense of it or not” [LW 02.4]. This not only led to greater service appropriateness but also strengthened the local community by creating new partnerships and opportunities: “there’s an opportunity here for us to develop physical and social activities together. So it’s about doing things together and one of the – we use local residents who’ve become qualified instructors, we try to keep everything local” [VCS 03.8].
## Synthesis: primary care-based social prescribing as a means for successful individual-level type 2 diabetes prevention
Figure 5 brings together quantitative and qualitative findings by illustrating why and how primary care-based SP contributed to individual-level T2D prevention approaches relevant to patients in greatest social and health need. The four dimensions (represented as four horizontal lines in Fig. 5) summarise our final CMOCs, which are described in more detail in Additional file 3: Fig. S1.Fig. 5Synthesis of study findings. How SP operates to deliver T2D prevention Highly supportive (proactive, welcoming) environments and broad (inclusive) referral criteria made SP easier to access than NDPP by high T2D risk individuals in greatest health and social need (CMOC1). Holistic practices involved gaining understanding of these underlying health and social constraints (including what they meant for people at high risk) and providing tailored care and services accordingly. This was often achieved by holding broad conversations with patients and widening the scope and remit of available services (CMOC2). Following a “healthy” lifestyle proved highly demanding for patients at high risk (especially insofar as underlying drivers persisted) and hence benefitted from sustained (ongoing and open-ended) support by known and trustful service providers (CMOC3). Our quantitative data also clearly showed that people at risk of T2D with multiple physical and mental health morbidities were more likely to be referred to SP than NDPP and our qualitative data suggests that this likely reflects the integrated delivery of SP including cross-referrals, connected and seamless care within primary care and across sectors (CMOC4).
The identified mechanisms were interconnected and mutually dependent, meaning that they only became possible (and led to significant outcomes) as the rest of the dimensions co-existed. Holistic practices, for instance, were more than a set of questions asked in one consultation prior to a referral. They came about only over time (through sustained practice) as providers got acquainted with (and gained understanding of) the local population, their community and the specific patient involved. Similarly, providers were able to deliver proactive care and make sure services were accessed only through regular follow-ups and timely feedback from other practitioners (“I know she started because the [name of VCS organisation] updated me, so she has started” [LW 02.11]). Integrated SP facilitated a greater understanding of patients’ circumstances, enhanced service responsiveness and allowed to address their needs holistically. Yet, far from representing a static structural dimension, it relied on trustful interpersonal relations across providers and hence required time (sustained encounters) to be developed: “I’ve built such a good relationship with [VCS organisations], and it’s because you’ve been doing it for so long, you get to know people on the team as well because you’re backwards and forwards with emails” [LW 02.5].
## Discussion
This mixed-method study, in a multi-ethnic, inner-city locality with high levels of deprivation, systematically illustrated how accessible, holistic, sustained and integrated SP practices in primary care appeared to support the delivery of individual-level T2D preventative approaches relevant (and available) to those in greatest health and social need.
The qualitative study showed that lack of disease (or diabetes)-specific eligibility criteria simplified the referral of patients to SP during routine, busy primary care consultations, which resulted in increased access of high T2D risk individuals to health promotion and wellbeing activities (all relevant to T2D prevention). This inclusive approach to T2D prevention seems particularly relevant given the low accuracy (and inconsistencies) of current pre-diabetes screening strategies (whereby patients may receive an incorrect diagnosis while others be falsely reassured and not offered any intervention [47]) and the generalised merit of healthier diet and exercise (which are likely to benefit most patients in multiple aspects of health, not just their risk of developing diabetes [48]). Our research suggests that accessibility in T2D prevention (and SP) can be best understood as a twin process of identifying patients’ needs and conditions (instead of risk levels) while finding ways to make services readily available to them, which often entailed ensuring highly supportive environments.
We showed how patients’ health-related behaviours (and consequent T2D risk) are contingent and socially patterned [49, 50], and therefore amenable to change insofar as interventions are within patients’ material reach, familiar to their existing social world and relevant to their life circumstances [51]. The literature has shown mixed responses to similar T2D preventative strategies. For example, while some studies found that regular feedback regarding risk level prompted attendance to lifestyle interventions and successful behavioural change [52], others reported the opposite reaction, whereby obtaining new knowledge specific to patients’ own risk elicited negative feeling and prevented further attendance [53, 54]. These inconsistencies confirm that different strategies work for different people and suggest (in line with our study findings) that a personalised (holistic) approach which takes into consideration patients’ specific (and changing) characteristics, priorities, expectations and circumstances might be better suited to deliver effective individual-level T2D prevention [22, 55].
The benefit of sustained approaches in T2D prevention has been widely acknowledged in the literature [22, 55]. Participating in interventions often acts as a relevant motivator for change and may provide “relief” (and sense of fulfilment) for being committed (and striving) towards betterment, regardless of the outcome [56]. Following healthy lifestyle recommendations may also be lived as an ongoing “struggle” (given underlying, persistent constraints) and hence benefit from continuous support [56]. Trustful relationships with service providers (enabled through and within ongoing interventions) have also been found to help patients make informed decisions about their health and contribute to healthy lifestyle maintenance. Critically, short-term interventions may lead to feelings of “frustration”, “failure” or “guilt” amongst participants for not achieving the intended (though arguably unrealistic) outcomes [48, 51].
Our study, in line with published literature, revealed that high-risk patients often suffered from co-morbidities and multimorbidity, which made them require (and access regularly) primary health care. Integrating SP and T2D prevention into routine primary care allowed for opportunistic health promotion advice to those who might have contacted the GP regarding a different (yet, coexistent) concern [55, 57]. It also encouraged to think of and practice the promotion of a healthy lifestyle as an ongoing and incremental process, instead of a one-off intervention [58]. Lastly, relying on community-based, local organisations (as opposed to external private providers, as was the case in NDPP) contributed to strengthening the local community [53, 59]. Services became “more than a place” in which patients were seen (or referred into) to be also conceived as “community spaces” where meaningful social connection and community action could engender (whose benefits may transcend T2D prevention) [60].
Our study suggests that SP may offer an opportunity for individual-level T2D prevention to become more inclusive, personalised and long-term. Interestingly, this was achieved through the very same interlinked mechanisms that define the strength and essence of primary care (namely, comprehensiveness, continuity of care, contact accessibility and coordination, as defined by Starfield [61]). This parallelism reminds us that SP relevant to T2D prevention both requires and results in a strengthened primary care system. Such practices, however, did not happen in a cultural or historical vacuum. They were enacted within (and shaped by) an environment characterised by a long (and proud) history of partnership between the health sector and local voluntary, community and faith groups. In adapting and accommodating practices together (over time), SP became recognisable and relevant to all concerned.
## Strength and limitations
To our knowledge, this is the first study exploring the potential of SP in the prevention of T2D. We identified key ingredients that contributed to explaining how (and why) SP succeeded in reaching people at high risk of T2D with greatest health and social vulnerability, while overcoming some of the limitations described in existing T2D prevention programmes. Another key strength of this study is the theoretically grounded, methodologically pluralistic design [62], derived from previous realist [21] and discourse analysis [9] reviews of primary care-based SP literature. The use of a realist approach and the combination of qualitative and quantitative methodologies allowed us to define rich, concrete, context-dependent exemplars of “good” practice in SP and T2D prevention and develop useful recommendations for policy and practice.
The study has limitations. It was confined to a single locality with a particular history and ethos of social engagement and provider innovation. Findings might not, therefore, be applicable elsewhere—in particular the close and productive working relationships between public and third-sector organisations in our study site cannot be expected to occur everywhere. Quantitative data were restricted to referrals into SP (and/or NDPP) and did not, therefore, capture the extent to which patients actually engaged with SP or NDPP, the type and duration of activities accessed, subsequent actions or clinical outcomes. Qualitative findings, however, helped to mitigate these constraints by providing rich context and in-depth explanations, including the key ingredients for potentially successful individual-level T2D prevention and detailed accounts of how (and why) programmes may (or may not) work. Our quantitative dataset also showed $24\%$ of missing ethnicity data, which although may have altered the real ethnic distribution of our cohort, is in line with the proportion of missingness described in published electronic health records studies [63]. Lastly, pandemic restrictions involved adaptations of both the intervention(s) (e.g. remote SP and NDPP sessions) and data collection strategies (e.g. holding some of the ethnographic observations and qualitative interviews remotely). This might have biased our sample towards less deprived and digitally literate participants. We sought to mitigate this by offering phone as well as video interviews, adapting interview schedules to meet individual circumstances and undertaking face-to-face interviews as soon as it was permissible and safe to do so.
## Implications for practice and policy
This realist evaluation generates a framework (Fig. 5) that could contribute to guiding the development, implementation and evaluation of SP programmes relevant to the prevention of T2D in communities at high risk. Building on rich, empirical data we defined “best practice(s)” in SP relevant to T2D prevention and identified the conditions (“key ingredients”) that need to be in place to facilitate this (and why).
Our study revealed the potential of accessible, holistic, sustained and integrated SP practices in T2D prevention. Critically, such approaches are not acknowledged by (and even seem to contradict) overarching SP health policy discourses and arrangements, which emphasise and prioritise the commissioning of short-term, motivational interventions [9]. Our study supports a revision of exiting SP health policy and commissioning strategies, so that they facilitate (and encourage) personalised, inclusive and long-term preventative approaches in primary care and the VCS. Study findings may also provide valuable insights into how to enhance the reach and equity of access of existing NHS T2D preventative strategies, such as NDPP (especially where a social gradient exists in those accessing and gaining benefit from the service) or develop adapted interventions that combine or integrate elements from SP and NDPP.
## Conclusions
Our research revealed the need (and merit) of an alternative framing of individual-level T2D prevention: as personalised, long-term and inclusive practices rather than standardised, short-term and targeted interventions (such as NDPP). SP proved ideally placed to deliver this where practitioners, providers and commissioners worked collectively to achieve holistic, sustained, accessible and integrated services. The wider contexts in which these practices developed proved, however, far from neutral. Existing organisational arrangements, priorities and routines shaped providers’ capacity (and willingness) to deliver SP relevant to patients at high risk. Additional research is being undertaken by the researchers involved in this study to investigate existing barriers and enablers for the delivery of accessible, holistic, sustained and/or integrated SP practices in primary care. Further research will also be critical to ascertain whether (and if so, to what extent) such practices contribute to reducing patients’ overall risk of developing T2D and to support appropriate delivery and roll-out in different settings.
## Supplementary Information
Additional file 1. Quantitative research design and results. Table S1. Overview of quantitative research design. Table S2. Distribution of clinical characteristics within the total study population and their association with referral into SP. Table S3. Distribution of clinical characteristics amongst people at high risk of T2D and their association with referral into SP. Table S4. Study year, age, and their association with referral to SP within the study population. Table S5. Study year, age, and their association with referral to SP amongst patients at high risk of T2D. Table S6. Association of clinical characteristics with referral into only SP compared to referral into only NDPP amongst patients eligible for NDPP.Additional file 2. Qualitative data sources and sample characteristics. Table S1. Qualitative data sources and their contribution to the study. Table S2. Characteristics of SP users interviewed. Table S3. Characteristics of link workers interviewed. Table S4. Characteristics of primary care clinicians interviewed. Table S5. Characteristics of VCS members interviewed. Additional file 3. COMCs developed in the realist mixed-methods evaluation. Figure S1. Realist evaluation COMCs.
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|
---
title: 'Stigma Mutation: Tracking Lineage, Variation and Strength in Emerging
COVID-19 Stigma'
authors:
- Hannah Farrimond
journal: Sociological Research Online
year: 2021
pmcid: PMC10008726
doi: 10.1177/13607804211031580
license: CC BY 4.0
---
# Stigma Mutation: Tracking Lineage, Variation and Strength in Emerging
COVID-19 Stigma
## Body
‘A major outbreak of novel, fatal epidemic disease can quickly be followed... by plagues of fear, panic, suspicion and stigma’. ( Strong, 1990: 249) Pandemics and stigma go hand in hand. It is not surprising, therefore, that the spectre of stigmatized health-care workers or sufferers, familiar from prior epidemics, is returning. However, celebrities and politicians’ openness about their ‘Covid status’ is striking. ‘ Hanx’ (Tom Hanks) and his friends do not appear afraid of gaining a ‘spoiled social identity’ (Goffman, 1963) as a result of being COVID-19 positive. Stigma is endemic alongside new contagious disease. The sudden emergence of a new threat throws social life into disarray. The chaos also makes tracking and analysing stigma difficult. In this article, I outline a novel theoretical framework for analysing the emergence of pandemic stigma using the metaphor of ‘mutation’. This metaphor draws attention to (a) stigma lineage, its emergence in relation to prior stigmas and origin stories; (b) stigma variance, its change over time, with cultural and temporal variation; and (c) stigma strength, its amplification, or weakening through de- and counter-stigmatization. I then use this theory to offer an analysis of the initial unfolding of COVID-19 stigma as it has emerged within complex, social media driven, globalized local worlds, highlighting the opportunities for intervention.
## Abstract
In this article, I propose a novel theoretical framework for conceptualizing pandemic stigma using the metaphor of ‘mutation’. This metaphor highlights that stigma is not a static or fixed state but is enacted through processes of continuity and change. The following three orienting concepts are identified: (a) lineage (i.e. origin narratives and initial manifestations are created in relation to existing stigmas, stereotypes, and outgroups), (b) variation (i.e. stigma changes over time in response to new content and contexts), and (c) strength (i.e. stigma can be amplified or weakened through counter- or de-stigmatizing forces). I go on to use this metaphor to offer an analysis of the emergence of COVID-19 stigma. The lineage of COVID-19 stigma includes a long history of contagious disease, resonant with fears of contamination and death. Origin narratives have stigmatized Asian/Chinese groups as virus carriers, leading to socio-political manifestations of discrimination. Newer ‘risky’ groups have emerged in relation to old age, race and ethnicity, poverty, and weight, whose designation as ‘vulnerable’ simultaneously identifies them as victims in need of protection but also as a risk to the social body. Counter-stigmatizing trends are also visible. Public disclosure of having COVID-19 by high-status individuals such as the actor Tom Hanks has, in some instances, converted ‘testing positive’ into shared rather than shamed behaviour in the West. As discourses concerning risk, controllability, and blame unfold, so COVID-19 stigma will further mutate. In conclusion, the metaphor of mutation, and its three concepts of lineage, variation, and strength, offers a vocabulary through which to articulate emergent and ongoing stigma processes. Furthermore, the concept of stigma mutation identifies a clear role for social scientists and public health in terms of process engagement; to disrupt stigma, remaking it in less deadly forms or even to prevent its emergence altogether.
## Stigma mutation: theory
‘Man’s yesterday may ne’er be like his morrow; Nought may endure but Mutability’. ( Shelley, 1885)
## Pandemic stigma literature
In this section, I review current thinking about the sociology of stigma, particularly the recent emphasis on the ideological and institutional creation of stigmatized identities beyond the interpersonal interactions first articulated by Goffman (e.g. Link and Phelan, 2001; Parker and Aggleton, 2003; Scambler, 2018; Tyler, 2020). Stigma is a state of social devaluation, being designated as ‘lesser’, due to a characteristic, difference, or membership of a group (Goffman, 1963). Stigmatization occurs when others behave differently towards the person, from overt discrimination to micro-aggressions. It is also experienced internally as ‘felt’ stigma, either as self-stigma (feeling shame towards oneself) or perceived stigma (anticipating others stigmatizing views or behaviours; Hammarlund et al., 2018). In other words, stigma consists of possessing a derogated social identity with external and internal consequences; however, these differ widely in how they manifest.
Recent sociological theories have emphasized that the creation of derogated social identities is not just a matter of interpersonal dislike or fear, but is embedded in wider socio-cultural representations of the ‘other’. We need to ask who, culturally and politically, is doing the stigmatizing, and why? For example, Scambler argues recent discourses about the responsibility of the sick and disabled for their own plight is directly linked to neo-liberal values underlying capitalist governance of Austerity Britain (Scambler, 2018). The ‘weaponizing’ of stigma, pairing the shame of state dependency with blame, diverts attention away from other misuses of power. Similarly, Tyler [2020] has drawn attention to the reproductive historical nature of power in racial inequalities. The relevance of this theorization is acute given COVID-19’s acceleration of the ‘othering’ of displaced, migrant, and ethnic minority groups (Roelen et al., 2020). The current focus on the operationalization of power within stigma theory reminds us that stigma emerges from complex socio-historical contexts and power relations; speaking about ‘COVID-19 stigma’ may overly simplify and reify what is occurring, and also hide questions about why.
In relation to disease, stigma has been identified across a large range of conditions; for example, mental illness (Hayward and Bright, 1997), chronic pain (Jackson, 2005), and other non-communicable diseases (Rose et al., 2017). However, the contours of stigma in relation to communicable or infectious diseases, such as viral epidemics, are particularly well-delineated. Orienting around a fear of contamination, an extensive literature has documented the stigma of people with Human Immunodeficiency Virus (HIV)/Acquired Immunodeficiency Syndrome (AIDS) (e.g. Parker and Aggleton, 2003) as well as for other epidemic or pandemic diseases, such as Tuberculosis (TB), Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and Ebola (e.g. Mak et al., 2006). For example, the stigma of Ebola resulted in the shunning of survivors, health-care workers, and occasionally, the entire village (Hewlett and Amolat, 2003). Pandemic stigma is highly problematic in disrupting efforts to contain transmission. Being stigmatized causes psychological distress, making groups the target of social rejection and exacerbates inequalities (Parker and Aggleton, 2003). Furthermore, provoking shame interferes with prevention and containment behaviours (Dolezal and Lyons, 2017). Affected individuals may avoid being tested, hide test results, or fail to adopt safer practices. Stigma prevention is consequently a core public health strategy for pandemic management. The COVID-19 virus has created an urgency to identify emergent stigma, given its potential to entrench socio-political inequalities and inhibit virus control.
## The metaphor of mutation
My aim in this article is to articulate the ‘epidemic of stigma’ that Strong has identified, paying particular attention to the way that stigma emerges, mutates, and changes in response to contexts. Although much work on stigma has measured individual and aggregate stigma, some research has started to articulate the social processes underlying pandemic stigma (e.g. Parker and Aggleton, 2003; Roelen et al., 2020). Anthropological work on prior epidemics has also highlighted the importance of cultural context through specific case studies (e.g. Brewis and Wutich, 2019; Hewlett and Amolat, 2003). This article takes a wide lens approach, offering a novel metaphor with which to think through stigma emergence in global and local contexts; and a borrowed (from biological science) vocabulary to articulate its contextual and temporal unfolding. Offering up a theoretical framework that considers stigma widely will allow a focus on particular elements of stigma, or a particular variant in one locality, but with the awareness of the interconnectedness of these enactments in 21st-century life. I suggest three aspects of the mutation metaphor are useful as orienting concepts: *In this* section, I have explained why stigma mutation may be a useful conceptual metaphor to consider stigma change over time. However, I have some reservations. Cultural studies have charted the use of the term ‘mutation’, from the 1950s radiation effects to current usage and this has progressively become more negative (Condit et al., 2002). Mutation as it appears in literature also carries a sense of monsters, ‘mutants’ which are neither human, nor anything else. Shelley’s poem was quoted in his wife’s study of transformative horror, Frankenstein. However, stigma IS a negative social phenomenon, not a positive one. Mutation is more recently and neutrally used in biology to describe changes in genes, the effects of which are often negative, but can also be advantageous (e.g. as part of genetic variation or evolutionary adaptation). With respect to viruses, there are already multiple genetic variants of COVID-19. The key reason, therefore, that I have stuck with the metaphor is not just the notion of mutation, but of mutability, the possibility and likelihood of change. The notion of stigma mutation avoids a reductionist sense of inevitability about COVID-19 stigma. It asks, what can we do differently? Where can we engage and disrupt stigma processes? As social scientists better understand the processes of stigma emergence and mutation, the opportunities to prevent stigma forming, embedding, or reoccurring are greater. Furthermore, understanding stigma in terms of lineage, variation, and strength has applicability beyond the immediate pandemic as a way to conceptualize stigma continuity and change. In the next section, I offer a worked through example of the utility of the mutation metaphor, at a particular time point (March 2020 to May 2021) from a Western (UK) perspective, to illustrate its portability to stigma theory in general.
## Analysis of emergence and mutation of COVID-19 stigma
Since my initial draft in summer 2020, there has been a plethora of literature published to indicate COVID-19 stigma emergence. Broadly, this literature is of two types. One is policy-driven work which highlights COVID-19 stigma as a core issue for public health (e.g. Logie and Turan, 2020; Sotgiu and Dobler, 2020; Van Daalen et al., 2021). The other offers early empirical evidence of COVID-19 stigma, for example, documenting hostility towards survivors in Kashmir and Latin America (Bagcchi, 2020; Dar et al., 2020); stigmatizing behaviour towards health-care workers (Dye et al., 2020; McKay et al., 2020) and contamination fears over death practices in Egypt (Abdelhafiz and Alorabi, 2020), Indonesia, and Iran (World Health Organization, 2020). Prejudice towards perceived ‘origin groups’ and social practices such as mask-wearing has also been identified (Ma and Zhan, 2020). Collectively, this work establishes that COVID-19 stigma has begun to emerge and that challenging it is important. However, often COVID-19 stigma is presented as a relatively fixed entity; something that now exists; ‘health-care workers are stigmatized’, even though there is huge variation and lack of universality about these experiences (although, see Bagcchi, 2020; Roelen et al., 2020). What I offer in the next sections, therefore, is a nuanced account of COVID-19 stigma emergence which pays attention to its lineage in past epidemics, and accounts for important culture-specific variance.
## Lineage of contagious diseases
COVID-19 is part of a lineage of highly feared contagious diseases: Ebola, SARS, MERS and HIV/AIDS and other viruses such as flu. Underlying this stigma is the ‘threat’ they pose (Jones et al., 1984). That said, although COVID-19’s mortality rate is considerable (current estimates can be found at: ArcGIS.com/apps/dashboard, accessed 04 June 2021), the individual risk of death for sufferers is not as high as in other epidemics such as Ebola (e.g. with $50\%$ of those infected dying), nor the toll currently as high as for HIV/AIDS. However, COVID-19, in all of its variants, is relatively contagious compared with SARS or MERS (Sanche et al., 2020). Contagiousness is an important dimension of disease stigma, as it threatens others, not just the self (Jones et al., 1984), invoking symbolic as well as physical fears of pollution (Douglas, 1966). In terms of threat and contagion, therefore, COVID-19 has strong lineage with other epidemics.
Another core dimension of disease stigma is controllability; the extent to which the condition is deemed the bearer’s responsibility (Joffe and Staerkle, 2007). Attributions of controllability vary, for example, HIV/AIDS is deemed more controllable than SARS or TB (e.g. Mak et al., 2006). However, the construct of controllability replicates existing societal biases. One of the primary methods for controlling COVID-19 is social distancing, through staying at home or shielding. However, only more advantaged workers with permanent jobs can work from home, leaving those with more precarious public facing jobs, such as poorer women and ethnic minority groups, disadvantaged (Fouad et al., 2020). Furthermore, attributions of controllability are moral. Blame ensues if people do not follow what is considered sensible and reasonable. Tom Hanks was not condemned for spreading COVID-19 to a new population in Australia in March 2020. However, by February 2021, celebrity apologies for breaking lockdown rules were ubiquitous (e.g. British celebrity Rita Ora, https://www.bbc.co.uk/news/entertainment-arts-55213784, accessed 27 February 2021). Perceptions of controllability, and thus of culpability, have mutated over time. Those who transgress ‘reasonable avoidance’ are blamed, especially if their risk factors are also perceived to be controllable or ‘achieved’ (Falk, 2001), such as obesity (Flint, 2020).
Although similar, COVID-19 stigma does not share complete lineage with other contagious diseases. HIV/AIDS has been multiply stigmatized through its association with perceived deviant sexual behaviour in outgroups (the so-called ‘gay plague’) and, underlying this, connotations of biblical punishment (Crawford, 1994). COVID-19 is not classified as a sexually transmitted disease; its disclosure correspondingly less taboo. However, discourses of ‘defying nature’ have been remade for COVID-19 times, for example, apocryphal (and untrue) tales abounded of dolphins swimming in the canals of Venice. Pollution levels dropped as capitalist production was locked down, at least temporarily. The discourse of plagues as acts of ‘purification’ of a sinful world continues.
Second, the rhetorical use of lineage for socio-political purposes has been pronounced. Some diseases such as (the Big C) cancer are metaphorically more scary, beyond biology; others such as flu less so (Sontag, 1988). Early on in the pandemic, Donald Trump, then President of the US, compared COVID-19 to the flu, a largely non-stigmatized and tolerated illness, minimizing the sense of risk. However, by mid-April 2020, Trump’s language had shifted towards far more feared associations: COVID-19 was ‘a great and powerful plague’ which had come from outside, in this instance, a ‘Chinese plague’. Leaving aside the xenophobia of this language (which I consider later), this anchors COVID-19 into a long line of ‘plagues’, and with them, connotations of cataclysm, chaos, and the end of the world.
The anchoring of new knowledge into older representations is as much an emotional process as a rational one. COVID-19 is not always visible; asymptomatic transmission is common. However, it pulls strongly on the underlying emotions of stigma: fear and disgust (Lupton, 2015). It reminds us of bodily processes, what Rozin has called ‘animal-reminder’ disgust (Rozin et al., 2008). Transmission occurs through breath and contact with others; dying of COVID-19 has been described as ‘drowning’. Disgust towards being polluted through breath, which fails to observe bodily boundaries, is noted in other stigmas, such as smoking (Farrimond and Joffe, 2006). COVID-19’s invisible transmission, which can leave no ‘mark’ on the carrier, provokes chaotic fear of contamination. Identifying the origins of the disease offers an opportunity to make order out of chaos but also to assign blame, particularly towards those perceived to have started it, such as ‘Patient Zero’ and original carrier communities.
## Lineage in origin narratives
The identification of the ‘other’ as the primary source of risk is pronounced within origin narratives. Origin (or outbreak) narratives are cultural tropes, amplified in film and media, which explain the emergence of novel disease, track its contagious progress, and end (ideally) with its containment by scientists and epidemiologists (Wald, 2008). Origin stories often perpetuate the lineage of already existing stigma. They are moral stories. Accusations of immorality and blame are projected across entire groups, away from the self (Crawford, 1994). Higher status groups also use outbreak narratives to shore up their own power; for example, the Global North tends to downplay the social determinants of disease and blame it instead on the anti-modernity of the Global South (Wald, 2008).
The origin narratives of COVID-19 in the West show rapid ‘othering’; identifying the source of COVID-19 as Chinese, and triggering a wave of anti-Chinese and Asian sentiment towards them as ‘carrier groups’ (e.g. Darling-Hammond et al., 2020; Ma and Zhan, 2020; Van Daalen et al., 2021; Wu et al., 2021). The first, most potent COVID-19 origin story is that the virus mutated from animals to humans in Wuhan, China, through a ‘wet market’ which kills and sells animals for consumption. The US rock singer Bryan Adams had to apologize after a rant against ‘bat eating, wet market animal selling, virus making greedy bastards’ (Beaumont-Thomas, 2020). The wet market story has all the elements of potential stigmatization from a Western perspective: threat, foreign ‘other’, dark, and disgusting practices relating to animals. It also fits with Weal’s hypothesis that the Global North shores up its own cultural ideologies through identifying others as anti-modern. The idea that *Asia is* a source of plague-type viruses already has lineage, for example, in terms such as ‘Asian flu’. COVID-19 has thus triggered existing racial prejudices towards Asians, increasing the mental health burden on Chinese/Asian people (Wu et al., 2021).
The ideological ‘weaponising’ (to use Scambler’s term) of anti-*Asian stigma* for political gain is also visible. Conservative elites in the US ‘racialized’ the pandemic, with then President Trump using phrases such as the ‘Wuhan Virus’, ‘China Virus’, ‘Chinese Plague’, and even ‘Kung Flu’ (Ma and Zhan, 2020; Reny and Barreto, 2020). The World Health Organization (WHO) condemns using location names as stigmatizing, offering a different nomenclature based on Greek letters to denote key variants (https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/, accessed 02 June 2021). The pejorative labelling by Trump was arguably deliberate, part of wider political actions against China, such as withdrawing from the perceived Chinese sympathetic WHO and starting a trade war; it also worked to divert attention from domestic deficiencies in COVID-19 policy. Origin narratives are thus not created from scratch, but from lineages of socio-political outgroup discrimination. For example, in India, existing tensions over Northeast Indians, and their identification as non- or ‘unwanted’ Indians, has been exacerbated by COVID-19 (Haokip, 2021) with a similar patterning emerging for Chinese South Americans in Chile (Chan and Strabucchi, 2021).
The origin story of COVID-19 has already mutated. Theories that began as conspiracies, such as the releasing of the virus from a Wuhan laboratory have been resurrected and disputed by the WHO, US and UK state agencies. More positive discourses which position Asian cultures such as Singapore and South Korea as ‘good at pandemics’ have also emerged in relation to technologically superior track and trace systems and the cultural propensity to wear masks, although this label is a precarious one, given that often apparently successful nations can go on to experience later COVID waves. Political resistance to denigration is also occurring, through anti-stigma campaigns in the US such as ‘Stop Asian Hate’. In the next section, I outline some of the ways COVID-19 stigma is mutating beyond the first wave of origin stories to identify new ‘risky’ groups.
## Beyond origin stories: newer ‘risky’ groups
Origin stories emerge quickly and potently in the first wave of any new virus. However, the patterning of the virus, and thus, the patterning of stigma, often changes again as newer ‘carrier groups’ are identified. Again, unsurprisingly, this identification of deviance often follows existing fault-lines (Roelen et al., 2020); this is not co-incidental, but expected to some extent, as bodies which are already disadvantaged are more likely to be adversely affected by viruses.
The association between the virus and patterns of inequality is particular pronounced for COVID-19 (e.g. Paremoer et al., 2021). The risk of severe disease and death is highest for older people (particularly the ‘oldest old’), those with pre-existing health conditions, socio-economically disadvantaged people, Black and ethnic minority groups, those with severe psychiatric conditions, the displaced/homeless and the obese. This list of COVID-19 ‘risky groups’ reads as a list of stigmatized groups within Western society. What, then, are the implications for COVID-19 stigma? One implication is that stereotypes for already stigmatized groups are at hand. New stereotypes, integrating COVID-19, are thus easily created. Second, identifying these groups as ‘at risk’ epidemiologically, is socially ‘risky’ for them. Stigma often takes the form of projections of risk and unhealthiness onto whole communities, not just those affected (Crawford, 1994). These groups then become the repositories of blame for society’s inability to control the virus.
Attributions of blame are particularly acute if membership of the group is deemed controllable, such as obesity (Flint, 2020). Weight stigma is heavily entrenched in Western society, carrying connotations of laziness and loss of control. Two key assumptions underpin the moral evaluation of ‘fatness’; that obesity is a costly threat to national and global health; and that obesity is preventable, hence the moral censure of those who ‘let themselves’ become obese (Throsby, 2007). Even within public health, individualistic discourses of lifestyle change position the obese as blameworthy, downplaying the structural obesogenic environment (Lupton, 2015). Weight stigma is being replicated in stigmatizing media discourses on COVID-19 risk (Flint, 2020). The UK prime minister, Boris Johnson, after recovering from COVID-19 himself as an overweight person, declared a ‘War on Fat’, rhetoric reminiscent of previous, often stigmatizing, anti-obesity campaigns. More recently, high body mass index (BMI) has been included as a qualifier for earlier vaccination, leading to moral debates. It is difficult to argue COVID-19 stigma is driving weight stigma in the West when the latter is already so pronounced. However, COVID-19 represents the further retrenchment of obesity as a prime risk to the social body that Throsby identified. Furthermore, obesity also patterns by social inequalities, creating a ‘constellation’ of COVID-19 stigma for multiply vulnerable groups.
The identification of a given group as being ‘at risk’ or ‘vulnerable’ for COVID-19 creates a tension for already stigmatized communities. On one hand, being identified as ‘at risk’ allows groups to be protected, for example, appearing on ‘shielding’ lists and having vaccination priority. On the other hand, stigmatized groups risk being identified WITH the disease, so that they become symbolic of it. Theirs is a fragile social identity which simultaneously identifies them as victims in need of protection but also as a risk to the social body, potentially leading to devaluation and discrimination. This is why I refer to them as ‘risky’ groups, rather than ‘at risk’ groups, as from a sociological point of view, their status as ‘at risk’ is risky for them, in terms of derogated social identity.
This vulnerability or danger tension is not just relevant to already stigmatized groups. Often considered ‘heroes’ within disaster scenarios, substantive global reports of the stigmatization of COVID-19 health-care workers are emerging (e.g. Dye et al., 2020; McKay et al., 2020; Taylor et al., 2020). In COVID-19 times, doctors and nurses simultaneously tread a path of being both ‘heroes’ but also a contamination risk, risking social disapproval and discrimination if they wear uniforms outside medical settings (Dolezal and Rose, 2020). Dolezal and Rose interpret this through the work of Kearney who suggests that moments of terror or war create simultaneous ‘gods’ and ‘monsters’ out of ‘others’; health-care workers are therefore positioned as both saviours and sinners because of their close contact with the infected.
The portrayal of older people in COVID-19 also draws on the ‘gods/monsters’ dichotomy. Existing stereotypes of elderly people orient around them as fragile, vulnerable but sweet (e.g. ‘doddering but dear’; Cuddy and Fiske, 2002). Alternatively, they can be positioned as heroic, exemplified by the media storm surrounding veteran Sir Tom Moore who completed 100 laps of his garden for his 100th birthday, raising 22 million pounds for the National Health Service (NHS), subsequently dying with COVID. The veneration of Sir Tom fits with a particular version of British history, prominent within Brexit rhetoric (the leaving of the UK from the European Union), of ‘Blitz Spirit’; a pandemic version of wartime propaganda. War metaphors reoccur as useful political narratives in epidemics to promote collective action but also to suppress less palatable narratives such as political unpreparedness (De Waal, 2021). In the story of Sir Tom, wartime rhetoric is also linked to the NHS representing the core of Britishness; itself linked to longer histories of imperialism (Fitzgerald et al., 2020). The heroic can, however, also be stigmatized. Very elderly people cost money. As the economic impact of COVID-19 lockdown policies are counted, these discourses of ‘value’ have become more pronounced, particularly concerning those considered not fully human, such as people with dementia, ‘the living dead’ (Behuniak, 2011). The prioritization of the oldest old for COVID-19 vaccination in the UK can thus be read simultaneously in two ways: as an indicator of the preciousness of the oldest old in our society and as the prioritization of the most expensive health-care group; the two narratives are not mutually exclusive. Old people are thus both the embodiment of the NHS, but also its biggest threat. In the next section, I move beyond mutations visible in the UK and the US (those most obvious from my interpretive lens of UK lockdown writing) to consider further emergent cultural variations in stigma.
## Cultural variation
Paying attention to cultural variation goes beyond considering context in the narrow sense. Rather, the enactment of stigma is understood as a set of embedded and embodied cultural practices. Thus, stigma is localized, pulling on older lineages of division and discrimination. For example, in mid-2020, discourses emerged in many Eastern European countries concerning the risk their diaspora posed in terms of bring in COVID-19 when returning home (Paun, 2020). Such discourses were seized on by political groups to further politicised (or to use Scambler’s term ‘weaponised’) stigma against those who were denigrated for ‘abandoning’ the mother country for economic gain.
Socio-political variation in the intensity and manifestation of stigma can also be seen in reporting from Iraq. Iraq was one of the first countries after China to experience a significant epidemic, and given its history of political and economic instability, was unprepared (Jadoo et al., 2020). Intense stigmatization of sufferers occurred. Central government struggled to enact national policies even though willingness to comply with social distancing was relatively high, at least in educated urban populations (Jadoo et al., 2020). One particular driver of stigmatization concerned traditional death practices which are conceptualized as extremely private and family-based (Rubin, 2020). COVID-19 prevention, which requires quarantine both before and after death, was antithetical to this. Other drivers of stigma include religious beliefs concerning disease as punishment and a deep distrust of health-care providers, perceived as agents of government and feared due to high hospital mortality rates (Rubin, 2020). This tension between prevention protocols and traditional death practices has been observed in prior epidemics, such as in West Africa in relation to Ebola (Manguvo and Mafuvadze, 2015). The experience of being COVID-19 positive in the UK in mid-2020 and in Iraq during the same time period was thus very different; openness about one’s ‘Covid status’ being relatively common in the former and almost unheard of in the latter. Adapting preventive practices to create cultural acceptability is likely to lead to greater uptake (Manguvo and Mafuvadze, 2015). That said, commonalities (e.g. the prevalence of conspiracy theories concerning government) can also be observed globally. Stigma is thus both local and global at the same time, and mutates globally and locally as a consequence.
## Stigma strength
The case of Iraqi COVID-19 stigma also speaks to the final dimension of mutation I consider: the strengthening and weakening of stigma over time. ‘ Stigma strength’ as a term does not denote anything about the power of any given stigma experience to affect an individual; multiple small micro-aggressions over prolonged periods can be very damaging (Sue, 2010). Rather, I use the term ‘stigma strength’ to refer to wider macro-level waves of stigma which increase and decrease over time. So far, I have focused on stigma intensifying; for example, the immediate resurgence of anti-Chinese/*Asian stigma* in the first COVID-19 wave. Importantly, I argue that pandemic stigma can amplify at particular cultural moments, but also weaken, producing less virulent strains, either deliberately, through anti-stigma public health interventions, or through broader cultural processes. This strengthening and weakening are also driven, particularly for COVID-19, by social media. The ‘infodemic’ offers almost limitless informational content on COVID-19 from social media platforms (e.g. Weibo, Twitter, and Facebook), most unverified by public health sources, stemming from influencers or celebrities as well as political organizations (hidden and visible; Cinelli et al., 2020). Social media can quickly amplify stigma. Tweets concerning the ‘Chinese Virus’ increased by $650\%$ by March 2020 (Darling-Hammond et al., 2020). Equally, counter-stigmatizing Tweets can circulate globally within minutes.
## Public health interventions to mitigate stigma
Turning first to public health intervention, COVID-19 has specific features, namely its contagiousness and dangerousness to vulnerable groups, which make traditional anti-stigma interventions such as community engagement and participatory design more difficult (Logie and Turan, 2020). Roelen et al. [ 2020], while calling for the participation of marginalized groups in COVID-19 responses in low and middle-income countries, also acknowledge that participation itself can risk stigma. Universalism, rather than highlighting specific ‘at risk’ groups, can be effective at reducing stigma. Evidence of universalism was seen in the UK government’s message in early 2020: ‘Anyone can catch it. Anyone can spread it’. Universalist language is desirable because it both protects vulnerable groups without labelling them, and avoids stigmatizing messages based on fear or disgust, such as early public health campaigns for HIV/AIDS (Herek, 1999). However, universal messages can come to contradict other public knowledge, such as the strong social patterning of COVID-19. Public health messaging can also risk perpetuating stigma rather than countering it. A proposed NHS campaign to get ‘national treasures’ to publicly have vaccines was filled with White celebrities; a subsequent (privately funded) health campaign was produced by British Asian celebrities, given that group’s lower vaccination rates (https://www.bbc.co.uk/news/entertainment-arts-56101990, accessed 13 March 2021). Offering a processual account of pandemic stigma therefore identifies a key role for public health; to engage and disrupt stigma processes. However, given the history of public health interventions which have inadvertently or deliberately used stigmatizing or divisive tactics, this engagement with stigma processes has to be self-reflective (e.g. actively auditing the likely effect on social justice/stigma of any given intervention; Goldberg, 2017).
## Stigma weakening through de- and counter-stigmatization
Turning now to consider wider societal trends, one highly distinctive feature of the COVID-19 pandemic has been the sheer quantity of celebrities, politicians, and sports stars declaring their COVID status (Mututwa and Matsilele, 2020). In politics, this has included (the then) President Trump, EU Chief Negotiator Michael Barnier and in the UK Prime Minister Boris Johnson and Prince Charles. In the celebrity world, Tom Hank’s disclosure was followed by actor Idris Elba and countless others. In China, the actress Tang Yifei declared three of her relatives to have the virus. Given the intense taboo of declaring one’s positive status in previous epidemics, such as Ebola/HIV, this is perhaps surprising. What is distinct about COVID-19 that allows individuals, at least in most Western countries, not to fear disclosure, at the same time that COVID-19 hostility has been gathering pace?
COVID-19 is not unique in linking viral status and celebrity. Most famously, Magic Johnson, an African American basketball superstar, came out as HIV positive in 1991. His statement shifted perceptions away from HIV/AIDS as a ‘gay disease’ (Kalichman and Hunter, 1992). More recently, in the immediate aftermath of actor Charlie Sheen coming out as HIV positive, online searches for HIV/AIDS increased measurably; the ‘Charlie Sheen effect’ (Ayers et al., 2016). However, there are significant differences between the cultural history of HIV/AIDS stigma and COVID-19. The first is the delay. Magic Johnson came out as HIV positive 10 or more years into the epidemic; Charlie Sheen in 2016. COVID-19 status was openly declared within weeks. Second, as a primarily sexually transmitted disease, HIV/AIDS remains highly stigmatized. Not many celebrities have followed Magic Johnson and Charlie Sheen. Practically, the intrusive nature of the COVID-19 ‘infodemic’ has meant hiding one’s status, when in the public eye, is more difficult. Declaring oneself ‘coronapositive’ can be read both as a culturally affirmative act and a damage limitation exercise. Importantly, in socio-political terms, the declarations of openness about COVID have come from those with a voice; primarily high-status individuals with influence in the West. Even if high-status individuals possess some of the features of derogated identity, this does not lead necessarily to their devaluation (Link and Phelan, 2001).
The impact of who was the earliest public COVID-19 sufferer may also be important. Tom Hanks (and his wife Rita Wilson) was the ideal poster-boy for de-stigmatizing COVID-19, a wholesome successful White actor, an Oscar winner and much admired for his portrayal as a gay man with HIV/AIDS in the 1990s. Data collected within 24 hours after his disclosure showed for many, it put a (reassuring) face to a scary and unknown virus (Myrick and Willoughby, 2021). Furthermore, his immediate disclosure modelled what healthy non-stigmatized behaviour would look like in relation to knowing one’s status. Subsequently, celebrities, footballers, and politicians have disclosed their coronastatus, often posting at length, although others have hidden it (e.g. UK’s Prince William). Indeed, the ability of privileged groups to access tests early in the pandemic was the subject of backlash (Li and Shakib, 2021). Subsequently, many celebrities have posted vaccine related content. The country singer and philanthropist Dolly Parton rewrote her famous song ‘Jolene’ into ‘Vaccine’ and was videoed having her injection. From a public health perspective, the idea of harnessing social media and celebrity or influencers is highly tempting. However, public health has perhaps under-estimated the extent to which it controls celebrity content; examples of both ‘on’ and ‘off’ messaging (e.g. perpetuating conspiracy theories) are rife (Wong et al., 2020). However, what I term the ‘Hanx Effect’ demonstrates the efficacy of early high-profile anti-stigma messaging by high status individuals, and its potential to create new positive social norms.
However, celebrity behaviours do not always engender positive new norms. Celebrities perceived to be ‘tone deaf’ to the cultural atmosphere of a pandemic, such as Kim Kardashian’s private island 2020 birthday or Ellen De Generes’ joke about living in gay isolation being similar to prison, are shamed. Being perceived as good is not always about what one actually does, but what we display (Finch, 2007). Displays of care, caution, and humility, exemplified by Tom Hanks’ tweet are deemed good; displays of ostentatiousness, greed, or rule-bending (e.g. flying to Dubai for ‘work’) are deemed immoral. We can conclude, therefore, that stigma strength is an important dimension of stigma mutation. It is also highly complex; amplifying and weakening forces may be in operation simultaneously, in the media as well as elsewhere, creating further (and not always predictable) stigma mutation over time.
## Conclusion: future mutation
Stigma is the dark social shadow of biological disease. This article contributes to the sociology of stigma by offering a novel process-oriented articulation of stigma emergence and change, using the metaphor of ‘mutation’. My interest in offering this articulation has been to flesh out the ‘plagues of fear, panic, suspicion and stigma’ that Strong identified; prompted by the suddenness of COVID-19. Using the language of ‘stigma lineage’, ‘stigma variation’, and ‘stigma strength’ has allowed me to draw attention to both the continuity and change of stigma processes, so that my account of COVID-19 stigma has somewhat of a story-like quality. It tells of initial intense and reactive stigma, contextualized in particular places at particular times towards already stigmatized racial/ethnic groups, often driven by fear but also political agendas, with a multiplying of stigma possibilities or ‘variants’ as the pandemic moves on.
The story of COVID-19 stigma emergence is also a complicated one. Traditionally valued groups such as health-care workers have experienced hostility. Not all vulnerable groups, in vulnerable situations, have been highly stigmatized. Actively chosen anti-stigma language by public health, coupled with the modelling of positive social norms of disclosure, have (possibly) weakened the hold of pandemic stigma in some localities. Where does this story go next? Will inter-generational stigma be exacerbated, as young and old both move forward to claim limited resources? Will vaccine stigma emerge against those who cannot or will not be vaccinated? Perhaps the story (or stories, as they are multiply enacted) will change in unpredictable ways; it has been argued the exposure of billions of people to extreme life stress may lead to greater openness and de-stigmatization about mental health (Venkatesh and Edirappuli, 2020). Using the language of ‘mutation’ allows us to track and unpack the story of COVID-19 stigma mutation as it occurs in both global and local contexts.
There is considerable utility for the metaphor of stigma mutation beyond the COVID-19 pandemic context. In particular, identifying the three orienting concepts of lineage, variation, and strength, gives us a vocabulary for articulating how stigma exhibits continuity and change over time, as it mutates through events, interventions, and longer term cultural shifts; particularly in relation to other stigma within given socio-political locations. Crucially, stigma mutation offers an opportunity not just for process articulation, but process engagement. As we better understand the processes through which stigma mutates, this opens up the opportunity to disrupt stigma, remaking it in less deadly forms, or even preventing its emergence altogether.
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76. Wu C, Qian Y, Wilkes R. **Anti-Asian discrimination and the Asian-White mental health gap during COVID-19**. *Ethnic and Racial
Studies* (2021) **44** 819-835
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title: GLSP and GLSP-derived triterpenes attenuate atherosclerosis and aortic calcification
by stimulating ABCA1/G1-mediated macrophage cholesterol efflux and inactivating
RUNX2-mediated VSMC osteogenesis
authors:
- Guobin Zheng
- Yun Zhao
- Zhenhao Li
- Yunqing Hua
- Jing Zhang
- Yaodong Miao
- Yang Guo
- Lan Li
- Jia Shi
- Zhengwei Dong
- Shu Yang
- Guanwei Fan
- Chuanrui Ma
journal: Theranostics
year: 2023
pmcid: PMC10008734
doi: 10.7150/thno.80250
license: CC BY 4.0
---
# GLSP and GLSP-derived triterpenes attenuate atherosclerosis and aortic calcification by stimulating ABCA1/G1-mediated macrophage cholesterol efflux and inactivating RUNX2-mediated VSMC osteogenesis
## Abstract
Background and Purpose: *Atherosclerosis is* the main pathophysiological foundation of cardiovascular disease, which was caused by inflammation and lipid metabolism disorder, along with vascular calcification. Aortic calcification leads to reduced plaque stability and eventually causes plaque rupture which leads to cardiovascular events. Presently, the drug to treat aortic calcification remains not to be available. Ganoderma lucidum spore powder (GLSP) is from *Ganoderma lucidum* which is a Traditional Chinese Medicine with the homology of medicine and food. It has multiple pharmacological effects, but no research on aortic calcification during atherosclerosis was performed. This study investigated the effects of GLSP on atherosclerosis and aortic calcification and revealed the underlying mechanism.
Methods: In vivo, 8-week-aged male LDLR-/- mice were fed a high-fat diet to induce atherosclerosis along with aortic calcification. Simultaneously, the mice were treated with GLSP at the first week of HFD feeding to determine the protection against early and advanced atherosclerosis. Subsequently, the mice tissues were collected to evaluate the effects of GLSP on atherosclerosis, and aortic calcification, and to reveal the underlying mechanism. In vitro, we determined the major components of GLSP triterpenes by HPLC, and subsequently assessed the protective effects of these main active components on lipid metabolism, inflammation, and calcification in RAW264.7 and HASMC cells.
Results: We observed GLSP attenuated plaque area and aortic calcification in the mice with early and advanced atherosclerosis. GLSP reduced the number of foam cells by improving ABCA1/G1-mediated cholesterol efflux in macrophages. In addition, GLSP protected against the aortic endothelium activation. Moreover, GLSP inhibited aortic calcification by inactivating RUNX2-mediated osteogenesis in HASMCs. Furthermore, we determined the major components of GLSP triterpenes, including Ganoderic acid A, Ganoderic acid B, Ganoderic acid C6, Ganoderic acid G, and Ganodermanontriol, and found that these triterpenes promoted ABCA1/G1-mediated cholesterol efflux and inhibited inflammation in macrophage, and inactivated RUNX2-mediated osteogenesis in VSMC.
Conclusions: This study demonstrates that GLSP attenuates atherosclerosis and aortic calcification by improving ABCA1/G1-mediated cholesterol efflux and inactivating RUNX2-mediated osteogenesis in LDLR-/- mice. GLSP may be a potential drug candidate for the treatment of atherosclerosis and vascular calcification.
## Introduction
Cardiovascular diseases have high morbidity and mortality and have become the major cause of death worldwide 1. Atherosclerosis is the major pathogenesis of cardiovascular disease and is characterized by lipid-loaded lesions in the vascular wall. Lipids deposition in the vascular wall causes chronic inflammation, leading to the activation of vascular endothelium and secretion of a large number of adhesion molecules, which recruit circulating monocytes to infiltrate vascular endothelium and differentiate into macrophages. Subsequently, lipid-overloaded macrophages transfer into foam cells 2. The apoptosis of foam cells accelerates the formation of a necrotic core and promotes the development of lesions 3. Dysregulation of macrophage phenotypes is a major driver of atherosclerosis. In addition, macrophages with high plasticity polarize to pro-inflammatory M1 type and secrete a large number of inflammatory factors, which further accelerate atherosclerosis 4. As atherosclerosis develops, inflammation and lipid accumulation contribute to calcium deposition in the plaque, which leads to the generation of unstable plaque and cardiovascular events 5.
Foam cells are an important indicator for the development of atherosclerotic plaques. Promoting lipid efflux from macrophages is an important means to improve macrophage lipid homeostasis and inhibit foam cell formation 6. ATP-binding cassette transporters are members of the highly conserved membrane transporters superfamily, which play an important role in the transmembrane transport of lipids and lipoproteins in macrophages 7-9. ABCA1 and ABCG1 are important regulators of intracellular cholesterol homeostasis by transporting excess lipids from macrophages to HDL and apolipoprotein A-I, which are then transported to the liver in the form of cholesterol esters and metabolized to bile for excretion, namely cholesterol reverse transport 10. Upregulation of ABCA1 and ABCG1 expression can inhibit macrophage-derived foam cell formation and thereby effectively reduce the onset and progression of atherosclerosis 6.
Calcification is one of the factors leading to the instability of plaques 11. Vascular calcification is characterized by the deposition of inorganic calcium salts in the vascular walls, which leads to loss of elasticity and hemodynamic changes 12. Calcification in atherosclerotic plaques occurs in the vascular media and intima. Intimal calcification is triggered by inflammatory and cytokine stimuli, whereas medial calcification is driven by osteoblastic cells 13. As the calcification process proceeds, excessive production of microcalcifications combined with increased levels of inflammatory factors cause destabilized calcifications, which leads to plaque instability and even rupture 14. The mechanism underlying atherosclerotic calcification is mainly related to the transformation of vascular smooth muscle cells (VSMC) into osteoblast-like cells 15. The current clinical treatment for atherosclerosis mainly focuses on the strategy of lowering lipids, such as statins 16 and PCSK-9 inhibitors 17. Nevertheless, the hepatotoxicity and muscle toxicity of statins as well as the high cost of PCSK9 inhibitor treatment limit further utilization 17, 18. Moreover, although these drugs have been widely prescribed, there is still a large portion of patients experiencing cardiovascular disease. Most importantly, there is no effective treatment for vascular calcification during atherogenesis.
Ganoderma lucidum is a traditional natural medicine with the same origin as food and medicine. Modern pharmacological studies have shown that *Ganoderma lucidum* has multiple beneficial effects, such as lipid-lowering, anti-inflammation, anti-oxidative stress, and anti-apoptosis effects 19-22. Ganoderma lucidum spore powder (GLSP) is the germ cells from Ganoderma lucidum, which contains the biological components of *Ganoderma lucidum* 23. The above studies showed that GLSP has antiatherogenic potential. However, the protective effects of GLSP on atherosclerosis and vascular calcification as well as the underlying mechanism remains unknown. We investigated the antiatherogenic effects and mechanism of GLSP in LDLR-/- mice with early or advanced atherosclerosis. This study revealed that GLSP can attenuate atherosclerosis and aortic calcification by reducing inflammation and apoptosis as well as VSMC osteogenesis. Subsequently, we detected the major components of GLSP by HPLC and performed the in vitro experiments to determine the protective effects of the triterpenoid content of GLSP on foam cell formation and inflammation in macrophages as well as calcification in VSMCs. Mechanistically, GLSP inhibited foam cell formation and aortic calcification by upregulating ABCA1/G1-mediated cholesterol efflux and inactivating RUNX2-mediated VSMC osteogenesis, respectively. Collectively, GLSP may be a new strategy for the treatment of atherosclerosis with calcification.
## GLSP attenuated early atherosclerosis and reduced plaque vulnerability in LDLR-/- mice
To study whether GLSP could attenuate early atherosclerosis, 8-week-aged LDLR-/- male mice were used to induce the early atherosclerosis model by feeding with HFD for 12 weeks, namely the Ctrl group. The other two groups were treated with atorvastatin (ATO) or GLSP, respectively, and the ATO group was indicated as the positive control (Figure 1A). In this study, the body weight of mice with early atherosclerosis showed no significance after the treatment of GLSP (Figure S1A-B). In addition, ultrasound data showed that cardiac function is not affected in mice with early atherosclerosis after administration of GLSP (Figure S2A). Subsequently, we examined the plaque area in the whole aorta and the aortic root. The oil red O staining revealed that the plaque area in the GLSP group was significantly reduced compared to the Ctrl group (Figure 1B-C). Increasing apoptosis within the plaque can contribute to the formation of a necrotic core during the development of atherosclerosis 24. The collagen-rich fibrous cap on the outside of the plaque becomes progressively thinner and leads to plaque instability 25. Thus, we examined the necrotic core, collagen content, and apoptotic levels of the plaque by HE, Sirius red, and TUNEL staining respectively (Figure 1D-F). After GLSP treatment, the area of the necrotic core within the plaque was significantly reduced, the collagen content was increased and the level of apoptosis was remarkably decreased. Taken together, GLSP markedly alleviated early atherosclerosis in LDLR-/- mice and enhanced plaque stability, and this is achieved by reducing lipid deposition, apoptosis, shrinking the necrotic core, and increasing the collagen content in the plaque.
## GLSP regressed advanced atherosclerosis and enhanced the plaque stability in LDLR-/- mice
Advanced atherosclerotic plaque is prone to rupture and thereby leads to a cardiovascular event. Therefore, we further determined the ameliorative effect of GLSP on advanced atherosclerosis. The advanced atherosclerosis model was induced by 36 weeks of HFD treatment in the presence of ATO or GLSP (Figure 2A). Cardiac ultrasound results and the body weight of mice with advanced atherosclerosis showed no significant changes after GLSP administration (Figure S1C, Figure S2B). Intriguingly, GLSP decreased the area of advanced plaques with necrotic cores (Figure 2B-D), indicating that GLSP can repress advanced atherosclerosis. In addition, GLSP reduced the level of apoptosis, and increased the collagen content in the plaque (Figure 2E-F), suggesting that GLSP reduced plaque vulnerability. Collectively, these data suggest that GLSP regresses advanced atherosclerosis and enhances plaque stability in LDLR-/- mice.
## GLSP inhibited the inflammation in both early and advanced atherosclerosis mice
Inflammation is involved in all stages of atherosclerosis development. In the early stage of atherosclerosis, modified lipids activated inflammatory cells in the endothelium, secreting chemokines as well as adhesion factors to stimulate further inflammation. Subsequently, macrophages differentiated from monocytes also secreted inflammatory factors, and collectively these factors contributed to the development of atherosclerosis 26. Therefore, to determine the inhibitory effects of GLSP on inflammation in atherosclerosis, we first examined the serum levels of inflammatory factors in mice with early and advanced atherosclerosis, respectively. The serum IL-1β, IL-6, and TNF-α levels were significantly reduced, whereas IL-10 was greatly increased in mice with early and advanced atherosclerosis after GLSP treatment (Figure 3A-B). Next, immunofluorescence staining showed that intraplaque IL-1β and caspase1 levels were significantly reduced whereas Arg1 levels were significantly increased in early and advanced atherosclerosis after GLSP administration (Figure 3C-D). Macrophage-mediated inflammatory responses play a key role in atherogenesis. We extracted peritoneal macrophages from mice with early or advanced atherosclerosis and detected the expression levels of inflammatory factors by qRT-PCR. The level of the pro-inflammatory factor IL-1β was significantly decreased and the levels of anti-inflammatory factors eNOS, TGFβ, Arg1, and IL-10 were significantly increased after GLSP treatment in early and advanced atherosclerosis (Figure 3E-F). Collectively, these results suggest that GLSP can reduce inflammation, by which alleviating atherosclerosis.
## GLSP attenuated endothelium injury and reduced oxidative stress
In addition to the inflammatory response, oxidative stress is considered another essential cause of atherosclerosis development. Large clusters of reactive oxygen species lead to the oxidation of lipids on the vascular endothelial cell membrane, resulting in endothelial dysfunction and monocyte adhesion. Therefore, we conducted an assay to evaluate the ability of GLSP to reduce endothelial damage and oxidative stress. We performed co-localization of immunofluorescence staining of CD31 and ICAM-1 or VCAM-1 in the aortic root sections of two stages of atherosclerosis. The results indicated that either atorvastatin or GLSP significantly reduced the levels of ICAM-1 and VCAM-1 in early as well as advanced plaques (Figure 4A-B), suggesting that GLSP could inhibit endothelial injury. In addition, to investigate the improvement of GLSP on oxidative stress, we examined the serum levels of ROS as well as SOD in mice. The results revealed that GLSP significantly increased serum SOD levels whereas decreased the serum ROS level in mice with early or advanced atherosclerosis (Figure 4C-D). Taken together, GLSP can reduce endothelial damage and oxidative stress, by which reducing the monocyte recruitment to the endothelium.
## GLSP inhibited macrophage-derived foam cell formation by improving ABCA1/G1-mediated cholesterol efflux
Macrophage-derived foam cells are a major part of atherosclerosis plaque, which is inextricably linked to cellular lipids and especially cholesterol deposition 27, 28. To further explore the effects of GLSP on cholesterol homeostasis and foam cell formation, we extracted peritoneal macrophages in mice with early and advanced atherosclerosis and stained them with oil red O solution. The results showed that GLSP could significantly reduce lipid accumulation in macrophages (Figure 5A-D), which suggested that GLSP inhibited macrophage-derived foam cell formation. Subsequently, we verified the mechanism of GLSP in the regulation of cholesterol efflux by western blot assay, and the results showed that GLSP promoted the expression of ABCA1 and ABCG1 in peritoneal macrophages from the mice with early and advanced atherosclerosis (Figure 5E-F), which was further supported by the results of immunofluorescence staining of ABCA1 and ABCG1 in aortic root cross-sections (Figure 5G-H). Moreover, the qRT-PCR results showed that GLSP significantly upregulated genes related to lipid catabolism, and simultaneously downregulated genes related to cholesterol synthesis and lipid synthesis in peritoneal macrophages from the mice with early and advanced atherosclerosis (Figure S4A-B), which suggested that GLSP improved lipid metabolism in macrophages by promoting cholesterol efflux and lipid catabolism, as well as inhibiting lipid synthesis. In summary, GLSP can reduce lipid accumulation in macrophages by promoting cholesterol efflux and lipid catabolism as well as reducing lipid synthesis, which inhibits the transformation of macrophages into foam cells and plaque formation.
## GLSP improved the lipid metabolism in the liver under the condition of atherosclerotic dyslipidemia
Lipid metabolism has a crucial role in the pathogenesis of both NAFLD and atherosclerosis. Serum lipids were examined in early and advanced LDLR-/- mice, and the results showed that GLSP significantly downregulated TG in the early model. GLSP significantly downregulated TC, TG, and ox-LDL, and upregulated HDL in the advanced model (Figure 6A-B). Abnormal hepatic lipid metabolism not only leads to NAFLD but also drives the development of atherosclerotic dyslipidemia 29, 30. To investigate the effect of GLSP on hepatic lipid metabolism, we measured the weight of the liver and subsequently gained the hepatosomatic index (liver weight /body weight, LW/BW). The results showed that the liver weight and hepatosomatic index of mice treated with GLSP tended to decrease without significance (Figure S1B, D). In addition, oil red O and HE staining were performed to evaluate the hepatic lipid accumulation. The staining results showed that the liver lipid accumulation and histopathological changes were significantly improved under GLSP treatment in mice fed with an HFD (Figure 6C, D). Furthermore, we extracted RNA from mouse livers and examined the expression of genes related to hepatic lipid synthesis and catabolism. The results of early atherosclerosis mice showed that GLSP was able to downregulate fatty acid synthesis-related genes (FASN, ACC1) and upregulate fatty acid catabolism-related genes (ATGL) (Figure 6E). In the results of advanced atherosclerosis mice, GLSP was able to downregulate fatty acid synthesis-related genes (FASN, PPARγ, SCD1) and upregulate fatty acid catabolism-related genes (HSL) (Figure 6F). Taken together, GLSP can improve hepatic lipid metabolism by reducing fatty acid synthesis and increasing fatty acid catabolism.
## GLSP reduced aortic calcification by inactivating RUNX2 signaling
With the development of atherosclerosis, vascular calcification occurs under the regulation of calcification-related molecules. To explore the effects of GLSP on vascular calcification, we performed alizarin red S staining on the whole aorta and aortic root cross-sections as well as quantitative determination of calcium content in the aorta by calcium content assay kit. The results showed that calcification in the whole aorta, aortic root plaques, and calcium content in the whole aorta was significantly reduced after GLSP treatment (Figure 7A-D). In addition, to further explore the mechanism by which GLSP ameliorates aortic calcification, we performed immunofluorescence staining on the major regulators of calcification, including ALP, Osx, and RUNX2, in aortic root cross-sections. RUNX2 is the transcription factor activating osteoblast differentiation and ALP/BMP2 expression 31. Osx is also a downstream gene of RUNX2 32. The results showed that GLSP significantly downregulated the expression of ALP, Osx, and RUNX2 in plaques (Figure 7E-F), indicating that GLSP can inhibit arterial calcification by downregulating ALP, Osx, and RUNX2. Taken together, GLSP attenuates vascular calcification by inactivating the RUNX2 signaling pathway.
## The triterpenes from GLSP inhibited macrophage-derived foam cell formation and inflammation
Triterpenes are the major components of GLSP 33, 34. In this study, we identified thirteen triterpene components in GLSP by HPLC and measured their contents (Table S1, Figure S3). HPLC results indicated the five triterpenoids, including Ganoderic acid A (GAA), Ganoderic acid B (GAB), Ganoderic acid C6 (GAC6), Ganoderic acid G (GAG), and Ganodermanontriol (GMT). The cytotoxicity of these five triterpenoids was measured by CCK-8 assay and then these five triterpenoids were used for the in vitro experiments in determined concentrations (Figure S5). After treatment by triterpenoids, the oil red O and immunofluorescence staining were performed to determine their effects on lipid metabolism and inflammation in RAW264.7 cells. The results showed that five triterpenes of GLSP, including GAA, GAB, GAC6, GAG, and GMT, significantly reduced the number of foam cells (Figure 8A). Accordingly, GAA and GAG significantly upregulated the expression of ABCA1, and GAA, GAB, GAG, and GMT significantly upregulated the expression of ABCG1 in RAW264.7 cells (Figure 8B). In addition, the qRT-PCR results showed that all the above components significantly upregulated the expression of ABCA1, while GAA and GAG significantly upregulated the expression of ABCG1 (Figure 8C). To determine the anti-inflammatory effect of these components of GLSP, we incubated the RAW264.7 cells with the GLSP components respectively. Intriguingly, GAA, GAB, GAC6, and GAG significantly downregulated IL-1β and TNF-α expression and simultaneously upregulated Arg1 expression (Figure 8D). Accordingly, the qRT-PCR results showed that triterpenes from GLSP significantly downregulated the expression of IL-1β in RAW264.7 cells, and downregulated the expression of IL-6 and TNF-α to a different degree; GAA and GAB significantly upregulated the expression of IL-10. ( Figure 8E). Taken together, the major components of GLSP, including GAA, GAG, GAC6, and GMT, could attenuate the foam cell formation and inflammation in macrophages, which may account for the protective effect of GLSP against atherogenesis.
## The triterpenes from GLSP inhibited VSMC osteogenic differentiation by inactivating RUNX2 expression and nuclear translocation
Calcium deposition in VSMCs significantly contributes to vascular calcification. To further determine the effects and the underlying mechanism of triterpenes from GLSP on VSMC calcification, calcium deposits in VSMC were induced by a calcification medium (CM) and determined by alizarin red S staining as well as calcium quantitative assay. We initially observed that treatment of HASMCs with triterpenes reduced cellular deposition of calcium (Figure 9A-B). In addition, we assessed the expression of vascular calcification markers, such as ALP and BMP2, and their upstream regulator RUNX2. RUNX2 is the transcription factor activating osteoblast differentiation and ALP/BMP2 expression 31. As the figure showed, CM induced the expression of ALP, Osx, and RUNX2 whereas triterpenes reduced ALP, Osx, and RUNX2 expression in HASMCs (Figure 9C). Furthermore, the result of the immunofluorescent staining showed that calcification-induced expression and nuclear translocation of RUNX2 and ALP were substantially attenuated by triterpenes (Figure 9D). Furthermore, the gene expression of BMP2 in HASMCs was measured by qRT-PCR, and the results showed that the osteogenic gene BMP2 was significantly downregulated in HASMCs under the treatment of triterpenes (Figure 9E). Next, the plasmid was transfected into HASMCs to induce RUNX2 overexpression, and the overexpression of RUNX2 in HASMCs was detected by Western blot (Figure 9F). GAA, GAB, GAC6, GAG, and GMT were administered to HASMC under the condition of overexpression of RUNX2, and the results showed that the inhibitory effect of GAA, GAB, GAC6, GAG, and GMT on calcium deposition and calcium content was markedly antagonized by RUNX2 overexpression in HASMCs (Figure 9G, H). Taken together, the major components of GLSP, including GAA, GAG, GAC6, and GMT, could attenuate VSMC calcification by inactivating RUNX2 expression, which partially accounts for the protective effect of GLSP against aortic calcification.
## Discussion
Cardiovascular disease remains the leading cause of death worldwide, which can increase the incidence of cardiovascular complications such as stroke, heart disease, and heart failure. Inflammation and lipid dysfunction are important contributors to both early and advanced atherosclerosis. Targeting inflammation and lipid metabolism are available strategies to treat atherosclerosis. Noticeably, during atherosclerosis development, vascular calcification always comes along and results in plaque vulnerability, which leads to the heavy risk of plaque rupture and the following cardiovascular event. However, at the present, no drug was available for treating vascular calcification during atherosclerosis development. In this study, we determined the protective effect of GLSP on atherosclerosis with calcification in LDLR-/- mice and revealed the underlying mechanism in vitro experiments.
Foam cells, a characteristic feature of atherosclerosis which mainly mediated by hyperlipidemia, are also a major contributor and promoter of atherosclerotic plaque development 35, 36. Therefore, reducing the formation of foam cells or decreasing inflammatory factor levels induced by foam cells may be possible strategies to ameliorate atherosclerosis. ABCA1 and ABCG1, play a very important role in cholesterol efflux. Upregulation of their expression can promote cholesterol efflux and thus reduce foam cell formation to a certain extent 37, 38. For example, betulin can reduce atherosclerosis by upregulating ABCA1 and ABCG1 expression 39 or inhibiting the degradation of ABCA140. Our study showed that GLSP can reduce lipid deposition in macrophage-derived foam cells, and then we examined the gene expression levels related to lipid production, consumption, and efflux, and found that GLSP can reduce lipid synthesis and increase lipid efflux, which mainly improves ABCA1/G1-mediated cholesterol efflux and further reducing atherosclerosis.
The liver is the terminal site of lipid metabolism and an important element in reverse cholesterol transport, which can metabolize lipids from circulation and cellular transport into bile acids for excretion to maintain lipid homeostasis 41. Accumulation of hepatic lipids can lead to hepatic steatosis, which can lead to decreased hepatic lipid metabolism and more lipid deposition in blood vessels and cells, leading to atherosclerosis. Therefore, reducing lipid accumulation in the liver and liver steatosis, and maintaining the normal physiological structure of the liver is a prerequisite for the liver to perform the function of lipid metabolism, and can inhibit atherosclerosis to a certain extent 29. Our study revealed that GLSP could downregulate genes of lipid synthesis and upregulate genes of lipolysis in the liver, by which reducing hepatic lipid accumulation and steatosis.
Calcification is an important contributor to atherosclerotic plaque rupture. Calcification is divided into media calcification and intimal calcification, which are involved by VSMC, and the former predominates in the atherosclerotic process. VSMC change from a contractile phenotype to an osteo/chondrocyte-like phenotype in response to inflammatory factors, oxidative stress, and mechanical stimuli 42, 43. This phenotypic shift is accompanied by a change in markers, such as decreased expression of smooth muscle cell markers SM22α, α-SMA, and increased expression of bone/chondrogenic markers RUNX2, and osteopontin 42. RUNX2 is a key transcription factor in the regulation of VSMC osteogenic differentiation and calcification. BMP2, ERK/MAPK, and PI3K/AKT signaling pathways induce RUNX2 expression in VSMC, promoting vascular calcification and atherosclerosis while pharmacological inhibition or degradation of RNUX2 can reduce calcification. For instance, microRNA-34a could reduce vascular calcification through the NOTCH1-RUNX2 signaling pathway 44. In this study, alizarin red S staining of the aortic root and measurement of aortic calcium content showed a significant reduction in calcification after GLSP treatment. Moreover, the osteogenic genes, including RUNX2, Osx, and ALP, were found to decrease significantly, suggesting that GLSP can significantly improve atherosclerotic calcification.
To determine the triterpenes from GLSP on atherosclerosis and vascular calcification. We determined the major triterpenes of GLSP by HPLC, including Ganoderic acid A, Ganoderic acid B, Ganoderic acid C6, Ganoderic acid G, and Ganodermanontriol. Subsequently, these components were utilized to incubate the macrophages and VSMC to assess the inhibiting effect on foam cell formation, inflammation, and VSMC osteogenic differentiation and calcification. In line with the results of in vivo experiments, these components inhibited foam cell formation by enhancing ABCA1/G1-mediated cholesterol efflux and attenuated calcification by inactivating RUNX2-mediated osteogenesis. Taken together, the anti-atherosclerotic effects of GLSP and its triterpenoids were achieved by enhancing ABCA1/G1-mediated cholesterol efflux and Inhibiting RUNX2-mediated osteogenesis.
In this study, we determined the protective effect of GLSP against atherosclerosis and vascular calcification in LDLR-/- mice, which may be attributed to inhibiting inflammation and apoptosis in plaque; and reducing lipid accumulation in plaque and liver. Mechanistically, these pharmacological properties of GLSP were through enhancing ABCA1/G1-mediated cholesterol efflux and reducing RUNX2-mediated VSMC osteogenic differentiation and calcification. At present, there is no effective treatment for vascular calcification during atherosclerosis development clinically. Therefore, GLSP may act as a novel therapeutic strategy for treating atherosclerosis and vascular calcification.
## Reagents
Antibodies for Arg1 (Cat#: ab212522), CD31 (Cat#: ab28364) and ABCG1 (Cat#: ab52617) were purchased from Abcam (Cambridge, MA). Antibodies for IL-1β (Cat#: sc-52012), ICAM-1 (Cat#: sc-107), VCAM-1 (Cat#: sc-13160), ALP (Cat#: sc-365765), Οsx (Cat#: sc-393325) and RUNX2 (Cat#: sc-390351) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA). Antibodies for Cleaved Caspase1 (Cat#: 4199), ABCA1 (Cat#: 96292), β-actin (Cat#:4970) were purchased from Cell Signaling Technology (Danvers, MA). TG assay kit (Cat#:100020090), total cholesterol assay kit (Cat#: 100020080), LDL-C assay kit (Cat#:100020245), HDL-C assay kit (Cat#:100020235) were purchased from Biosino Bio-Technology and Science INC (Beijing, China). Alizarin Red S solution (Cat#: G3280) was purchased from Solarbio & Technology Co., Ltd (Beijing, China). IL-1β Elisa kit (Cat#: SEA563Mu), IL-6 Elisa kit (Cat#: SEA079Mu), TNF-α Elisa kit (Cat#: SEA133Mu), IL-10 Elisa kit (Cat#: SEA056Mu) and SOD Elisa kit (Cat#: SES134Mu) were purchased from Cloud-Clone Corp. (Wuhan, China). Mouse ROS Elisa kit (Cat#: YX-181519M) and Mouse ox-LDL Elisa kit (Cat#: YX-152412M) were purchased from Sino Best Biological Technology CO., Ltd (Shanghai, China). Calcium Colorimetric Assay kit (Cat#: MAK022-1KT) was purchased from Sigma-Aldrich. All other reagents were purchased from Sigma-Aldrich except where indicated.
## Cell culture
RAW264.7 cells and HASMCs were cultured in this study. The frozen cells were taken out from the liquid nitrogen and transferred to the 37 °C warm water bath quickly for about 1 min; the resuscitated cells were then placed into DMEM/F12 or RPMI 1640 medium containing $10\%$ fetal bovine serum and 50 μg/mL penicillin/streptomycin and 2 mM glutamine. The suspended cells were transferred to a centrifuge tube for centrifugation at 800 rpm for 10 min, and the supernatant culture medium was removed. DMEM/F12 or RPMI 1640 medium was added, and the cells were blown gently and distributed to culture dish, then cells were incubated in the cell incubator at 37 °C with $0.5\%$ CO2. The drugs were administered when the cell density reached more than $70\%$. The cells were slightly rinsed with sterile PBS, and then the serum-free DEME medium was added. Subsequently, the drugs prepared with DMSO were added into the medium at the indicated concentration to incubate the cells.
## Animal studies
All animal care and experimental protocols for in vivo studies conformed to the Guide for the Care and Use of Laboratory Animals published by the NIH (NIH publication no. 85-23, revised 1996) and approved by First Teaching Hospital of Tianjin University of Traditional Chinese Medicine. Male LDLR-/-mice (8 weeks old) were purchased from the Changzhou Kavens Laboratory Animal Co. Ltd. (Nanjing, Jiangsu, China). These mice were maintained at the Animal Center of Chu Hsien-I Memorial Hospital with free access to food and water. Mice were allowed to acclimatize to their housing environment for at least 7 days before experiments. LDLR-/- mice were randomly divided into 3 groups: Ctrl group, ATO group, and GLSP group, in which atorvastatin was used as a positive drug and GLSP was used as a treatment drug. The atherosclerosis model was induced by feeding a high-fat diet (HFD, $21\%$ fat, $0.5\%$ cholesterol, MD12015HL, medicience Ltd.). Based on the formula for conversion of body surface area between mice and humans, the dose of atorvastatin (Pfizer Pharmaceuticals Ltd.) used in mice was converted as 4 mg/day/kg body weight, and the dose of GLSP (Zhejiang ShouXianGu Botanical Drug Institute) was converted as 1400 mg/day/kg body weight. Calculated drugs were mixed with the high-fat diet. The mice were fed HFD continuously for 12 weeks in the early atherosclerosis model and 36 weeks in the advanced atherosclerosis model, respectively. The treatment during the experiment was conducted blindly. During the treatment, the animals were checked daily for intake of food and water, and weekly for their body weight.
## Foam cells detection
Three mice in each group were randomly selected to extract the abdominal macrophages. Briefly, each mouse was injected intraperitoneally with 3 mL of $4\%$ sulfur gelatin. 4 days later, mice were sacrificed and 10 mL sterile PBS was injected intraperitoneally. The PBS containing macrophages was extracted with a syringe and carefully rinsed repeatedly. The extracted liquid was pumped into a 15 mL centrifuge tube, and red blood cell lysis buffer was added to remove red blood cells. The cells were centrifuged at 800 rpm for 10 min and the supernatant was discarded. The complete RPMI 1640 medium containing $10\%$ FBS and 50 μg/mL penicillin/streptomycin was added into the centrifuge tube to resuspend cells, and cells were seeded in 24-well plates. After 1 day, the adherent cells were washed gently with PBS, and the pre-filtered oil red O solution was added to each well for 1 h. Subsequently, the oil red O solution was discarded and hematoxylin solution was added to each well for 10 min. Cells were photographed under a 40× visual field eventually and foam cells were counted as previously described 45.
## Lesion detection by Oil Red O staining
The lesion was indicated by the lipid content in the whole aorta and aortic arch which was determined by oil red O staining. Briefly, the aorta of mice was separated and the excess tissue was stripped. Before staining, the oil red O solution was filtered. The aorta was stained with oil red O solution for 1 h. Photographs were subsequently taken under a stereomicroscope. Frozen sections of the aortic root were also stained by oil red O solution for 1 h and were photographed with a microscope.
## HE staining, Sirius red staining, and immunofluorescent staining
The frozen sections of the aortic root were stained with hematoxylin staining for 2 min and gently washed with running water, then the frozen sections were stained with eosin staining for 30 s, and the necrotic core areas in the plaques were counted to evaluate plaque stability. Moreover, the frozen sections of the aortic root were stained with sirius red staining for 1 h, then stained with hematoxylin staining for 10 min, and collagen areas in plaques were counted to evaluate plaque stability. The frozen sections of the aortic root were stained with immunofluorescent staining. Briefly, the frozen sections of aortic roots were sealed with $2\%$ BSA for 1 h, then a diluted primary antibody was added and placed in the refrigerator at 4 °C overnight. The next day, the frozen sections were washed three times with PBS for 10 min each time and incubated with secondary antibody for 1 h at 37 °C. Then the frozen sections were washed three times with PBS again and sealed in the dark. Subsequently, the fluorescence intensity of inflammatory cytokine and calcification-related molecules was measured by a fluorescence microscope.
## Determination of calcification in vivo and in vitro
Vascular calcification formed in vivo or in vitro was determined by alizarin red S staining of aortic root cross sections and calcium quantitative assay. Briefly, the frozen sections of aortic roots were stained with alizarin red S for 5 min, differentiated with McGee-Russell differentiation solution for several seconds, and then stained with hematoxylin for 2 min. The calcium quantitative assay was performed according to the steps in the kit instructions. In vitro, HASMCs were induced calcification by culture in complete DMEM/F12 medium (1:1) containing 5 mM Pi (mixture of NaH2PO4 and Na2HPO4, ratio 1:2, pH7.4) and 50 μg/mL ascorbic acid or plus treatment for 7 days, followed by alizarin red S staining and calcium quantitative assay 46, 47.
## Western blot and qRT-PCR analysis
The total protein of cells or tissues was extracted, then the protein concentration was measured by the BCA protein quantification kit, and the protein was quantified to 1 μg/μL. Subsequently, protein expression of ABCA1, ABCG1, and β-actin was determined by Western blot. Total RNA of cells or tissues was extracted by RNA extraction assay kit, the concentration of RNA was measured and mRNA was reverse transcribed to cDNA, next, primers and SYBR mix were added to detect the gene expression of eNOS, TGFβ, Arg1, IL-1β, IL-10, SRA, ABCA1, ABCG1, HMGCR, FASN, CPT1α, ACC1, ATGL, HSL, SREBP1, SCD1, PPARγ, IL-6, TNF-α. Primer sequences were shown in supplementary data table S2. All reactions were performed three times.
## Determination of triterpenes in GLSP by HPLC
Ganoderic acid I, Ganoderic acid C2, Ganoderic acid C6, Ganoderic acid G, Ganoderic acid B, Ganoderic acid N, Ganoderic acid B, Ganoderic acid A, Ganoderic acid H, Ganoderic acid D2, Ganoderic acid D, Ganoderic acid C1, and Ganodermanontriol control substance were weighed with 10mg, and then dissolved in methanol to make the working solution. The corresponding concentration was 10.24 mg/mL, 19.78 mg/mL, 10.33 mg/mL, 16.29 mg/mL, 5.02 mg/mL, 4.66 mg/mL, 21.15 mg/mL, 21.18 mg/mL, 18.22 mg/mL, 8.63 mg/mL, 5.56 mg/mL, 14.42 mg/mL and 9.68 mg/mL. Subsequently, the testing solution was prepared for testing. Briefly, 1.0 g GLSP was dissolved in 40 mL methanol for ultrasonic extraction for 30 min and filtered by 0.22 mm microporous membrane for liquid chromatograph determination. The determination was performed on a chromatographic column (Waters CORTECS T3, 4.6 mm×150 mm, 2.7 mm). Mobile phase A was $0.1\%$ formic acid aqueous solution, mobile phase B was acetonitrile, and the gradient elution condition was 0-26 min, $25.0\%$-$25.5\%$ B; 26-29 min, $25.5\%$-$30\%$ B; 29-34 min, $30\%$-$30\%$ B; 34-40 min, $30\%$-$40\%$ B; 40-54 min, $40\%$-$70\%$ B; 54-55 min, $70\%$-$100\%$ B; 55-62 min, $100\%$-$100\%$ B. The flow rate was 1.0 mL/min; the column temperature was 40 °C; the detection wavelength was 254 nm, and the injection volume was 5 mL.
## Plasmid transfection for RUNX2 expression
Cells were seeded to a 24-well plate. On the day of transfection, for each well of cells, 0.5 mg DNA was diluted in 50 mL serum-free medium and 1.5 mL liposomal nucleic acid transfection reagent was diluted in 50 mL serum-free medium. Diluted DNA and diluted liposomal nucleic acid transfection reagent were mixed, gently blended, and incubated at room temperature for 20 min to form DNA-liposome complexes. Subsequently, 100 mL of the complex was added to each well of the cell culture plate, and the plate was shaken and gently mixed. The 24-well plates were incubated for 48 h in a 37 °C, $5\%$ CO2 incubator.
## Statistical analysis
The data and statistical analysis comply with the recommendations on experimental design and analysis in pharmacology. All data are expressed as mean ± SEM or mean ± SD. An unpaired Student's t test was used for comparisons between two groups, or One-way ANOVA for comparisons between multiple groups followed by Turkey's method. Significance was accepted when $P \leq 0.05.$
## Author Contributions
G Zheng, Y Zhao, and Z Li conducted the experiments; S Yang edited the paper; L Li, J Shi, Y Miao, Y Guo, J Zhang, Z Dong, and Y Hua offered advice. G Fan and C Ma designed the experiments and wrote the paper.
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|
---
title: KLF5 inhibition potentiates anti-PD1 efficacy by enhancing CD8+ T-cell-dependent
antitumor immunity
authors:
- Qi Wu
- Zhou Liu
- Zhijie Gao
- Yao Luo
- Fubing Li
- ChuanYu Yang
- Tiantian Wang
- Xiangyu Meng
- Haijun Chen
- Juanjuan Li
- Yanjie Kong
- Chao Dong
- Si Sun
- Ceshi Chen
journal: Theranostics
year: 2023
pmcid: PMC10008740
doi: 10.7150/thno.82182
license: CC BY 4.0
---
# KLF5 inhibition potentiates anti-PD1 efficacy by enhancing CD8+ T-cell-dependent antitumor immunity
## Abstract
Background: Immune checkpoint blockers (ICBs) are revolutionized therapeutic strategies for cancer, but most patients with solid neoplasms remain resistant to ICBs, partly because of the difficulty in reversing the highly immunosuppressive tumor microenvironment (TME). Exploring the strategies for tumor immunotherapy is highly dependent on the discovery of molecular mechanisms of tumor immune escape and potential therapeutic target. Krüppel-like Factor 5 (KLF5) is a cell-intrinsic oncogene to promote tumorigenesis. However, the cell-extrinsic effects of KLF5 on suppressing the immune response to cancer remain unclear.
Methods: We analyzed the immunosuppressive role of KLF5 in mice models transplanted with KLF5-deleted/overexpressing tumor cells. We performed RNA sequencing, immunohistochemistry, western blotting, real time-PCR, ELISA, luciferase assay, chromatin immunoprecipitation (ChIP), and flow cytometry to demonstrate the effects of KLF5 on CD8+ T cell infiltration and related molecular mechanism. Single-cell RNA sequencing and spatial transcriptomics analysis were applied to further decipher the association between KLF5 expression and infiltrating immune cells. The efficacy of KLF5/COX2 inhibitors combined with anti-programmed cell death protein 1 (anti-PD1) therapy were explored in pre-clinical models. Finally, a gene-expression signature depending on KLF5/COX2 axis and associated immune markers was created to predict patient survival.
Results: KLF5 inactivation decelerated basal-like breast tumor growth in a CD8+ T-cell-dependent manner. Transcriptomic profiling revealed that KLF5 loss in tumors increases the number and activated function of T lymphocytes. Mechanistically, KLF5 binds to the promoter of the COX2 gene and promotes COX2 transcription; subsequently, KLF5 deficiency decreases prostaglandin E2 (PGE2) release from tumor cells by reducing COX2 expression. Inhibition of the KLF5/COX2 axis increases the number and functionality of intratumoral antitumor T cells to synergize the antitumorigenic effects of anti-PD1 therapy. Analysis of patient datasets at single-cell and spatial resolution shows that low expression of KLF5 is associated with an immune-supportive TME. Finally, we generate a KLF5/COX2-associated immune score (KC-IS) to predict patient survival.
Conclusions: Our results identified a novel mechanism responsible for KLF5-mediated immunosuppression in TME, and targeting the KLF5/COX2/PGE2 axis is a critical immunotherapy sensitizer.
## Background
Recently, cancer immunotherapy has achieved remarkable breakthroughs in clinical practice. The clinically developed immunotherapeutic strategies comprise inhibitory immune checkpoint blockers (ICBs), enhanced costimulators, oncolytic viruses, various vaccines and adoptive cell therapies 1. Programmed cell death protein 1/programmed cell death ligand 1 (PD1/PD-L1) is considered a main immune checkpoint, and its blockers have been approved by the US Food and Drug Administration (FDA) 2. In recent years, atezolizumab (PD-L1 inhibitor) combined with paclitaxel chemotherapy has achieved efficacy in PD-L1-positive triple-negative breast cancer (TNBC) patients 3. Likewise, neoadjuvant toripalimab with or without celecoxib resulted in a favorable pathological complete response rate in patients with mismatch repair-deficient or microsatellite instability-high, locally advanced, colorectal cancer 4. However, PD1/PD-L1 immune checkpoint blockade only benefits a small subset of patients and fails to generate durable responses in most patients 5, 6. Intrinsically and extrinsically immunosuppressive mechanisms endow tumors with the capacity to resist anticancer therapies 7, 8. Hence, developing comprehensive strategies or fire-new drugs is crucial to form an immune-supportive microenvironment and surmount resistance to immunotherapy.
Krüppel-like Factor 5 (KLF5), a member of the Krüppel-like factor family, controls essential cellular processes, including proliferation, differentiation, and migration 9. Structurally, KLF5 has a triple zinc-finger DNA-binding domain at its C-terminus, which mainly binds to CACC or GC boxes in DNA and further modulates the transcription of downstream target genes, such as fibroblast growth factor-binding protein 1 (FGF-BP1) 10, p27 11, Cyclin D1 12, TNFAIP2 13, mPGES1 14, Slug 15 and IGFL1 16. KLF5 is an oncogene in basal-like breast cancer (BLBC), colorectal cancer and pancreatic cancer relevant to tumor stemness, proliferation, invasion, metastasis, and the tumor microenvironment (TME) 9, 17, 18. Furthermore, KLF5 is a potent therapeutic target for BLBC and other cancers. Our previous studies have shown that metformin, mifepristone, the bromodomain 4 (BRD4) inhibitors 19, mithramycin A 20, CDK7 inhibitor 19, PRMT5 inhibitor 21, RSK2 inhibitor 22, and HDAC inhibitor 23 retard tumor growth by downregulating KLF5 expression 10, 19, 24. In advanced colorectal cancer, mesenchymal stromal cell-derived CCL7 stimulated the acetylation of KLF5 by p300, subsequently acetylated KLF5 and transcriptionally activated CXCL5 expression to facilitate tumor metastasis 25. Likewise, lysine demethylase 3A (KDM3A) could upregulate the transcription of epidermal growth factor receptor (EGFR) by recruiting KLF5 and SMAD family member 4 (SMAD4). KLF5 knockdown sensitized tumors to PD-1 blockade by increasing CD4+ and CD8+ T cells and reducing myeloid-derived suppressor cells (MDSCs) 26. Given the functions of KLF5 in the tumor-immune microenvironment (TIME), knowing the mechanisms by which KLF5 influences the composition of the TIME is crucial.
Cyclooxygenases (COXs), as catabolic enzymes, enable the conversion of arachidonic acids to prostaglandin G2 (PGG2) and H2 (PGH2), which are further transformed to prostaglandin I2 (PGI2), prostaglandin D2 (PGD2), and prostaglandin E2 (PGE2). COXs primarily comprise constitutive COX1 and inducible COX227. PGE2 plays a pivotal role in various human diseases, such as cardiovascular disease, cancer, and neurological diseases 28-30. In the tumor microenvironment, PGE2 can be released by multiple cell types, such as tumor cells, cancer-associated fibroblasts and MDSCs 28. PGE2 production is regulated by diverse inflammatory stimuli and transcription factors, including KLF5 and p6514, 28. Specifically, KLF5 binds to the mPGES1 gene proximal promoter and activates its transcription to promote PGE2 synthesis 14. As a proinflammatory lipid metabolite, PGE2 interacts with a family of G protein-coupled receptors—E-type prostaglandin receptors 1-4 (EP1-4) 31. Notably, PGE2 exerts a protumorigenic effect by stimulating the proliferation and metastasis of neoplastic cells and tumor angiogenesis 28. Likewise, PGE2 has a pivotal immunosuppressive effect via multiple mechanisms, including directly impairing the proliferation and activation of NK cells and effector T cells, suppressing the antigen presentation of dendritic cells and increasing the infiltration of MDSCs and regulatory T cells (Tregs) 31. Additionally, inhibition of the COX2/mPGES1/PGE2 axis or EPs antagonists enhances the efficacy of PD-1 blockers to improve antitumor activity in various tumor models 32-34. Therefore, specific interruption of PGE2 generation or antagonism of its receptors may be used as adjuvants to synergize with immune-targeting drugs.
In this study, we found that KLF5 deficiency inhibits progressive tumor growth and enhances antitumor immunity in a CD8+ T-cell-dependent manner. Mechanistically, KLF5 promotes PGE2 release by transcriptionally activating COX2 expression. Additionally, KLF5 knockdown, a KLF5 inhibitor or a COX2 selective inhibitor, synergized with the efficacy of the anti-PD1 blocker by increasing the infiltration and activating function of CD8+ T cells. Ultimately, we identified a gene signature that integrates the KLF5/COX2 axis and proliferation and activity of CD8+ T cells. *This* gene signature score showed independent prognostic value in BLBC.
## Cell culture and reagents
The mouse cancer cell lines TC1, MCA205, MC38, CT26, EMT6 and 67NR were obtained from Guido Kroemer's lab. The abovementioned mouse cancer cells, mouse breast cancer lines 4T1 and E0771 and human breast cancer MDA-MB-231 cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with $10\%$ (v/v) fetal bovine serum (FBS) at 37 °C in a humidified atmosphere with $5\%$ CO2. Human breast cancer HCC1806 cells were cultured in Roswell Park Memorial Institute (RMPI)-1640 medium supplemented with $10\%$ FBS. Lipopolysaccharide (LPS) and celecoxib (CEL) were purchased from MedChemExpress (Shanghai, China). FZU-00,004 was synthesized by Haijun Chen (College of Chemistry, Fuzhou University, China).
## Lentivirus preparation and transfection
KLF5 siRNA and cDNA lentivirus were obtained from GeneChem Biotechnology (Shanghai, China). Cells were cultured at 5 × 105 cells/well in 6-well plates. After incubation for 24 h, the cells were transfected with the aforementioned lentivirus and control vectors (GeneChem Biotechnology, China) following the manufacturer's instructions. Selection was performed using puromycin (1 μg/mL; Sigma) in cell culture media for 48 h after transfection. Cell lysates were then collected, and protein expression was detected by Western blotting (WB). The sequence information is provided in Table S1.
## Patients
A total of 67 formalin-fixed paraffin-embedded (FFPE) colon cancer tissue samples were obtained from Renmin Hospital of Wuhan University. All the patients involved in the study provided written informed consent. Patients did not receive financial compensation. Clinical information was extracted from medical records and pathology reports, and the detailed clinicopathological characteristics of the patients are shown in Table S4. The patients were all followed-up for at least 38.1 months from the date of the first diagnosis. All the procedures were performed in accordance with the Declaration of Helsinki and relevant guidelines and local regulations. The study was approved by the Institutional Ethics Committee of Renmin Hospital of Wuhan University (approval no. 2018K-C09).
## ELISA
Tissue samples (~30 mg) were dissociated in tubes containing 1 mL of radio immunoprecipitation assay buffer (RIPA) lysis buffer using a homogenizer (Servicebio, China) at 6,500 rpm for 5 min, followed by centrifugation at 14,000 × g for 15 min to collect the supernatant containing soluble proteins. For cells, the media were collected via centrifugation at 14,000 × g for 15 min at 4 ℃. The PGE2 level was measured using a mouse PGE2 ELISA kit (CSB-E07966m; CUSABIO) following the manufacturer's instructions. The PGE2 levels were standardized by the tissue weight or the cellular protein concentration.
## Western blotting
The protein extracts were dissolved in RIPA buffer for 30 min on ice, and then the samples were centrifuged at 12,000 × g for 15 min to collect the supernatant containing soluble proteins. The protein concentration was measured using the BCA Assay (Bio-Rad, Hercules, CA, USA). The protein solution was mixed with 4×loading buffer and heated at 100 °C for 10 min before being subjected to WB. The total protein samples (~ 30 μg) were subjected to SDS‒PAGE and then blotted onto 0.2 μM polyvinylidene fluoride (PVDF) membranes (#1620177; Bio-Rad). The membranes were blocked with $0.05\%$ Tween 20 (#P9416; Sigma Aldrich) v:v in Tris-buffered saline (TBS) (TBST) (#ET220; Euromedex) supplemented with $5\%$ nonfat powdered milk (w:v in TBS), followed by overnight incubation at 4 °C with primary antibodies specific for KLF5 (#AF3758; 1:1000; R&D Systems), COX2 (#66351-1-Ig; 1:1000; Proteintech), CyclinD1 (#55506; 1:1000; Cell Signaling Technology) and Vinculin (#13901; 1:2000; Cell Signaling Technology). The membranes were washed with TBST three times for 10 min before incubation with HRP-conjugated secondary antibody for 1 h at room temperature. Next, the membranes were washed again and subjected to chemiluminescence detection using the Amersham ECL Prime detection reagent kit (GE Healthcare, Piscataway, NJ, USA) on an ImageQuant LAS 4000 software-assisted imager.
## RNA extraction and quantitative RT‒PCR
Total mRNA was collected by TRIzol reagent (Invitrogen). Reverse transcription was performed using the TaqMan® mRNA Reverse Transcription Kit (Vazyme, China), and mRNA levels were quantified using RT Real-Time SYBR Green/Rox PCR master mix (Vazyme, China) on the ABI-7900 system. The mRNA primer sequences are provided in Table S2.
## Chromatin immunoprecipitation assay
ChIP was performed using 67NR wt/KLF5-3F OV and HCC1806 cells following a protocol provided by Abcam (Cambridge, MA, USA). The diluted DNA-protein complex (25 μg protein) was incubated with different antibodies (anti-KLF5 Ab and goat IgG) overnight at 4 ℃ in the presence of herring sperm DNA and protein A/G beads or anti-Flag magnetic beads. PCR was performed on 67NR using primers for the PTGS2 promoter to amplify the -929 to -918 region: 5'-CAAGAACGTACAGTTTAGTTG-3' (forward) and 5'-TTGCCTAGAGAGGTGATGTTTTTGAT-3' (backward); a nonspecific KLF5-binding site: 5'-GGCAGCTTATAACTTTCTATAACTATAGT-3' (forward) and 5'-TATTTATTTATTTATTTATTTATTTATTTATTTTGTGTG-3' (backward). For HCC1806, the primer sequences were as follows: the putative KLF5-binding site, 5'-CATAAAACATGTCAGCCTTTCTTAACCTTAC-3' (forward) and 5'-AATCTGAGCGGCCCTGAGGTC-3' (backward); a nonspecific KLF5-binding site: 5'-AGTTCTTTGATTAAGGTAGTAGTTACAC-3' (forward) and 5'-AACCAGGAAACTGATCTTGGTA-3' (backward).
## Dual luciferase assay
The COX2 proximal promoters were amplified using normal human DNA and mouse genomic DNA as templates. The PCR products were cloned into pGL3-BASIC (Promega, Madison, WI, USA). The inserts were confirmed by DNA sequencing. 293T cells were seeded into 24-well plates at 1×105 cells per well. The next day, the cells were transfected in triplicate with the COX2 gene promoter reporter constructs (500 μg per well) and an internal control pRL-TK (50 μg per well). Twenty-four hours after transfection, the cells were infected with a GFP control adenovirus and a KLF5 adenovirus for 4 h (~$50\%$ cells were infected under a fluorescence microscope). At 24 h after infection, luciferase activities were measured using the dual luciferase reporter assay system (Promega).
## Immunohistochemistry
A cohort of 67 human colon cancer specimens was collected from Renmin Hospital of Wuhan University from 2016 to 2017. Immunohistochemistry (IHC) staining was performed, and the staining results were scored using ImageJ software as previously described35. The infiltrating level of CD8+ cells was counted per square millimeter in each colon cancer specimen, while the protein expression level of KLF5 was described by the percentage of positive cells calculated using ImageJ software. The optimal cutoff values for all expression levels were determined using X-tile Software.
## Mouse models
All experiments involving animals were handled according to the protocol (SMKX-20160305-08) approved by the Animal Ethics Committee of the Kunming Institute of Zoology, CAS. All the mice were maintained in a temperature-controlled and pathogen-free environment with 12 h light/dark cycles and access to food and water ad libitum. All the animal experiments were performed in accordance with relevant guidelines and local regulations.
For virus-induced tumorigenesis, FVB/N mice carrying Klf5 alleles flanked by LoxP sites (Klf5fl/fl) have been described previously 15. The lentivirus carrying polyoma middle T-antigen (PyMT) or PyMT-Cre was intraductally injected into different sides of the same FVB/N Klf5fl/fl mice at 5 weeks of age. After being isolated and dissociated, the tumors were further cultivated in DMEM/Ham's F-12 ($\frac{50}{50}$) medium containing $10\%$ FBS. After verification of the Klf5 levels, the neoplastic cells were inoculated into the mammary fat pads of FVB/N mice. For tumor growth experiments, six-week-old female BALB/c mice were purchased from SJA Laboratory Animal Co., Ltd. (Hunan, China). Mouse mammary carcinoma EMT6 wild-type cells (3 × 105) or EMT6 Klf5-knockdown cells (3 × 105), 67NR wild-type cells (4 × 106) or Klf5-overexpressing cells (4 × 106), mouse colon cancer CT26 cells (5 × 105) or CT26 Klf5-knockdown cells (5 × 105) were subcutaneously injected into BALB/c hosts. When tumors grew to approximately 20 mm3 in volume, the mice were treated with CEL dissolved in corn oil (30 mg/kg, gavage daily for two weeks), FZU-00,004 (dissolved in $5\%$ DMSO, $40\%$ PEG300, $5\%$ Tween 80, and $45\%$ PBS; 1 mg intraperitoneal injection) or an equivalent volume of vehicle alone or in combination with 200 μg of anti-Pd-1 antibody (Clone 29 F.1A12; BioXcell, West Lebanon, NH, USA). The mouse weight and tumor growth were monitored and documented on subsequent days. The tumor area was defined as (longest diameter) × (shortest diameter) × 4/π and was measured once every 3 days using a Vernier caliper. Animals were sacrificed when the tumor size reached the endpoint or signs of obvious discomfort were observed following the advice of the Ethical Committee.
## Ex vivo phenotyping of the tumor immune infiltrate
The tumors were harvested, weighed and transferred on ice into gentle tubes containing 1 mL of RPMI medium. The tumors were dissociated first mechanically with scissors and then enzymatically using DNase I/Collagenase IV with shaking (> 200 rpm) at 37 ℃ for 1 h. The dissociated bulk tumor cell suspension was resuspended in RPMI 1640, sequentially passed through a 70 μm Smart-Strainer and washed twice with PBS. Finally, bulk tumor cells were resuspended in PBS at a concentration corresponding to 250 mg of the initial tumor weight per ml. Intracellular cytokine samples were restimulated with 200 μl of stimulation medium with brefeldin A (#423303; BioLegend) ex vivo for 5 h. Cell viability was determined using the LIVE/DEAD® Fixable UV Dead Cell dye (Thermo Fisher Scientific) to discriminate viable cells from damaged cells. Before staining tumor-infiltrating lymphocytes (TILs) for flow cytometry analysis, the samples (~50 mg) were incubated with anti-mouse Cd16/Cd32 (clone 2.4G2; Mouse BD Fc Block; BD Pharmingen) to block the Fc receptors. Surface staining of murine immune cell populations infiltrating the tumor was performed using the following fluorochrome-conjugated antibodies: anti-Cd45-BV650, anti-Cd3-Percp-cy5.5, anti-Cd8-FITC, anti-Cd4-PE, anti-Cd25-APC/Cy7, anti-Cxcr6-PE/Cy7 and anti-Pd-1-BV510 (BioLegend). Next, the cells were fixed and permeabilized in Foxp3 Fix/Perm buffer (BioLegend) and stained for intracellular Foxp3 (anti-Foxp3-BV421) and Ifnγ (anti-Ifnγ-APC). Finally, stained samples were run through a flow cytometer (LSR Fortessa; BD). The data were acquired using BD FACS-Diva software (BD Biosciences) and analyzed using FlowJo software (TreeStar). Absolute counts of leukocytes and tumor cells were normalized considering the following parameters: weight of the harvested tumor and total volume of the dissociated tumor cell suspension (cell concentration typically set to 250 mg/mL in PBS), proportion of the whole cell suspension and proportion of the cell suspension used for cytometry 36.
## Single-cell mRNA sequencing and analysis
Single-cell RNA-seq data were obtained from our previous data (GSE198745) and the public dataset (GSE176078) in Gene Expression Omnibus (GEO). Downstream single-cell data analyses were conducted using the Seurat package in R. Each sample was individually quality checked. Cells were filtered using the following criteria: at least 200 detected genes and no more than $15\%$ mitochondrial reads per cell. Cells with extremely high numbers of reads or genes detected were filtered to minimize the occurrence of doublets. Genes expressed in fewer than 3 cells for individual samples were filtered. Multiple single-cell sample integration and batch effect correction were performed using the mutual nearest neighbors (MNN) method and “RunFastMNN” function from the SeuratWrappers package. The principal component dimensions 1:15 were used for all dimension reduction and integration steps. We conducted principal component analysis (PCA) on the normalized expression matrix using the top 2000 highly variable genes identified by the ''FindVariableGenes'' function in Seurat. For dimensionality reduction visualizations, we used the uniform manifold approximation and projection (UMAP) algorithm. Finally, the clusters were compared pairwise using the “FindAllMarkers” function to detect the cluster-specific expressed genes, which were used to achieve annotations for the clusters. We chose 6 triple-negative breast cancer (TNBC) patients (CID4523, CID4515, and CID4465 in the KLF5-high group and CID44041, CID4495, and CID4513 in the KLF5-low group) using paired bulk RNA-seq for subsequent immune cell analysis. The identification of diverse T-cell subpopulations referred to a single-cell resolved pancancer study of tumor-infiltrating T cells by Zhang et al. 37. *The* gene signatures of 186 metabolic and signaling pathways were curated from the KEGG subset of canonical pathways from the C2 collection using MSigDB. Single-cell signature scores were calculated using the Gene Set Variation Analysis (GSVA) method and GSVA package from Bioconductor. The differential metabolic and signaling pathways in the KLF5-high and KLF5-low groups were computed using the limma package.
## Spatial transcriptomics
The spatially resolved transcriptomic data and images of breast cancer patients in a previous study are available in GEO (GSE198745). Additionally, the public spatially resolved transcriptomics data and images of 4 TNBC patients (CID4465, CID44971, 1142243F, 1160920F) could be obtained from the *Zenodo data* repository (https://doi.org/10.5281/zenodo.4739739). The basal signature score was computed using GSVA based on the basal cell signature genes (KRT5, KRT14, KRT17). The CD4+ and CD8+ T signature scores were computed using GSVA based on the CD4+ and CD8+ T-cell signature genes (CD3D and CD4 for CD4+ T cells, CD3D and CD8A for CD8+ T cells).
## Bioinformatic Analysis of Patient Datasets
A total of 360 TNBC patients with RNA-seq data were obtained from Fudan University Shanghai Cancer Center (FUSCC) (https://www.biosino.org/node/analysis/detail/OEZ000398). The data of breast cancer patients in The Cancer Genome Atlas (TCGA) were downloaded from UCSC Xena (http://xena.ucsc.edu/), and TNBC patients were selected using the PAM50 classifier. For the RNA-seq data of FUSCC, after transforming the transcriptomics data to normalized transcripts per million values (TPM), we performed differential analysis of TGFB1-low patients grouped by the expression level of KLF5 by the limma package. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) pathway analyses were executed using the clusterProfiler package. The abundance of diverse immune subpopulation infiltration was estimated using TIMER2.0 (http://timer.comp-genomics.org/). The 'cancer-promoting (CP)' and 'cancer-inhibitory (CI)' inflammatory genes whose expression was regulated by KLF5 in the mouse models are shown in Table S3. To obtain KLF5-IS, the signature scores were calculated as the ratio of the mean expression (normalized TPM) of CP and CI signature genes. A total of 360 TNBC patients were then stratified based on the level of KLF5-IS scores, and survival curves were generated using the Kaplan‒Meier method and the survival package. We used log-rank test statistics to assess the significance between groups.
## Statistical analysis
Statistical analyses were performed using GraphPad Prism version 8.0. All experiments were performed at least three times independently. The results are presented as means ± SD. The relative increase in protein expression was quantified using ImageJ software and was normalized to control protein expression in each experiment. Datasets obtained from different experimental conditions were compared with t test when comparing only 2 groups. Multiple comparisons between groups were performed using the Mann-Whitney U test or Tukey's multiple comparison test. Survival probabilities for recurrence-free survival (RFS) were estimated using the Kaplan-Meier method, and variables were compared using the log-rank test. Pearson's correlation was used to evaluate the correlations. In the bar graphs, a single asterisk (*) indicated $p \leq 0.05$, two asterisks (**) indicated $p \leq 0.01$, and three asterisks (***) indicated $p \leq 0.001.$
## The deficiency of Klf5 in tumors decelerates tumor progression depending in part on the functions of Cd8+ T cells
To explore whether Klf5 contributes to tumorigenesis, we developed a murine breast cancer model with Klf5 knockout (KO)15. FVB/N Klf5-LOXP mice at 5 weeks of age were intraductally injected with lentiviruses carrying polyoma middle T-antigen (PyMT) and PyMT-Cre. Next, these tumors were isolated, dissociated and cultivated. The Klf5 level in tumor cells was verified by WB (Figure S1C). Subsequently, neoplastic cells with or without Klf5 expression were seeded into the mammary fat pads of FVB/N mice (Figure S1A). Tumors with Klf5 knockout grew more slowly than those in the control group (Figures 1A and S1D-E). Furthermore, we screened Klf5 expression in multiple murine cancer cells, showing that Klf5 was highly expressed in murine breast cancer EMT6 cells and murine colon cancer CT26 cells but expressed at low levels in 67NR cells (murine breast cancer cells) (Figure S1B). *We* generated EMT6 cells with Klf5 knockdown (KD) and 67NR cells overexpressing Klf5 (Figure S1F, I). We next measured tumor growth in immunocompetent BALB/c mice by injection of different cells. Depletion of Klf5 in EMT6 cells substantially retarded tumor growth (Figure 1D and S1F-H), while tumors derived from 67NR cells overexpressing Klf5 grew faster than those from control cells (Figure 1G and S1I-K).
To investigate whether Klf5-modulated tumor growth was mediated by Cd8+ T cells, the infiltrating level of Cd8+ T cells was detected in the abovementioned mouse models by immunohistochemistry. A marked increase in Cd8+ T cells was found in tumors with Klf5 KO or KD compared with those in the control group. By contrast, tumors carrying cells with Klf5 overexpression showed reduced Cd8+ T-cell infiltration (Figure 1B-C, E-F and H-I). Furthermore, an anti-Cd8 neutralizing antibody was applied to block Cd8+ T cells in the EMT6 mouse model, indicating that Cd8+ T-cell depletion facilitated tumor growth in both the control and Klf5 KD groups (Figure 1J and S1L-M). Taken together, the results demonstrated that Klf5 contributes to accelerating tumor growth partly by impairing the infiltration of Cd8+ T cells.
## Transcriptome profiling reveals that Klf5 regulates the tumor immune microenvironment
To evaluate whether Klf5 alters the tumor-immune microenvironment, we performed RNA-sequence analysis on tumor tissues from EMT6 or 67NR mouse tumor models. *Significant* gene expression with changes > 1.5-fold and $p \leq 0.05$ was considered (Figure 2A and S2A). To characterize the biological processes underlying the transcriptional changes in control tumor tissues and Klf5 KD tumor tissues, KEGG signature scores and Gene Ontology (GO) analysis were performed. A striking enrichment of T-cell proliferation, differentiation, chemotaxis and activation and other antitumor immune pathways was observed in tumors with low Klf5 expression (Figure 2B-C). Among the profoundly upregulated genes in the Klf5 KD group, most were associated with T-cell differentiation (such as Eomes, Irf4 and Foxp3), proliferation (such as Cd28), chemotaxis (such as Ccl5, Ccr7, Ccr9, Cxcr3 and Cxcr6) and activation, including Il-12, Il-2 and interferon γ (Ifnγ) production and Gzmg and Gzmf overexpression (Figure 2D). Subsequently, we deduced the cell composition in tumors from the control and Klf5 KD groups via the X-cell method. The analysis demonstrated that naïve and activated dendritic cells (DCs), NK cells, Cd4+ effector memory T cells and Cd8+ effector memory T cells were enriched within tumors carrying Klf5 KD cells (Figure 2E). Conversely, the tumors from 67NR overexpressing Klf5 were enriched in multiple pathways involving immune functions: “lymphocyte migration”, “chemokine-mediated signaling pathway” and “lymphocyte chemotaxis” (Figure S2B). Likewise, gene expression analysis revealed a profound increase in immunosuppressive markers (such as Cxcl1 and Il10) but a decrease in immune-supporting genes (such as Tnfrsf19, Tnfrsf18, Cxcr3, Cx3cr1 and Cxcl13) (Figure S2C). Concerning cell composition in the TME, a reduction in the number of naïve and activated dendritic cells (DCs), naïve Cd8+ T cells, Cd4+ effector memory T cells, Cd8+ effector memory T cells and in the total Immunoscore was found in tumors with Klf5 overexpression (Figure S2D). Thus, tumor-intrinsic Klf5 expression may contribute to the alteration of overall immune compositions in the TME by mediating the proliferation, differentiation, chemotaxis and activation of T cells.
## KLF5 promotes PGE2 production by augmenting COX2 gene transcription
Our previous study revealed that KLF5 promoted PGE2 production in TNBC by inducing mPGES1 transcription 14. We reanalyzed the transcription profiles mentioned above and found that hallmarks of the arachidonic acid catabolic pathway were substantially changed in tumor tissues. Interestingly, Ptgs2 (encoding Cox2 protein) expression was positively related to the Klf5 levels (Figure 3A). To further verify whether KLF5 promotes COX2 expression, EMT6 and 67NR cells were treated with lipopolysaccharide (LPS, an inducer of COX2 expression 38) or COX2 inhibitor (celecoxib, CEL). Klf5 KD by siRNA silencing decreased Ptgs2 mRNA and protein expression, while ectopic Klf5 overexpression profoundly upregulated Ptgs2 mRNA and protein (Figure 3B-D). Consistently, LPS failed to induce Cox2 expression after Klf5 was knocked down. By contrast, Klf5 overexpression amplified the effect of LPS-induced Cox2 expression (Figure 3C-D). We also verified that Klf5 positively regulated mPegs1 expression in EMT6 and 67NR cells (Figure S3E-G). The KLF5 transcription factor regulates PTGS2 mRNA transcription through the PTGS2 promoter. To test this, we found several potential KLF5 binding sites on website tools and after a review of the literature 39. Next, we generated luciferase reporter constructs by cloning the PTGS2 gene promoter (mouse: -1000/+101; human: -1100/+100) into the PGL3-BASIC plasmid. Dual luciferase assays in HEK293T cells found that the luciferase reporter constructs were significantly activated by KLF5 (Figure 3E; Figure S3B). To further validate whether the predicted KLF5 binding site is responsible for KLF5-mediated transcriptional activation, we mutated the predicted binding site. Indeed, the mutation completely abrogated KLF5-mediated PTGS2 gene promoter activation in HEK293T cells (Figure 3E; Figure S3B), confirming that the putative KLF5 binding site is necessary for PTGS2 gene promoter activation by KLF5. Finally, we demonstrated that Klf5 binds to the *Ptgs2* gene promoter using chromatin immunoprecipitation (ChIP) assays in 67NR wt/Klf5-3F OV cells (Figure 3F-G). Consistently, only the anti-KLF5 antibody, but not the control goat IgG, specifically immunoprecipitated the promoter in HCC1806 cells (Figure S3C-D). As expected, depletion of the Klf5 or COX2 inhibitor observably reduced PGE2 levels in vitro and in vivo. Conversely, KLF5 upregulation and LPS stimulated PGE2 secretion (Figure 3H-J). Finally, we detected Cox2 expression and Cd8+ T-cell infiltration in mice inoculated with control or Klf5 KD tumor cells in the presence or absence of CEL. For this analysis, the Cox2 levels in tumors carrying Klf5 KD tumor cells were lower than those in the control group. Conversely, Cd8+ T cells were significantly more abundant in the Klf5 KD group than in the control group (Figure 3K-M). Therefore, KLF5 facilitates COX2 and mPGES1 transcription to increase PGE2 production and decrease CD8+ T cell infiltration.
## Inhibition of the Klf5/Cox2 axis increases the number and functionality of intratumoral antitumor T cells
To address the necessity of Klf5/Cox2 axis for tumor immunity regulation, we developed subcutaneous tumor mouse models with 67NR wt/Klf5-OV cells (Figure 4A-B). Klf5 overexpression induced pro-tumorigenic effect and Pge2 enhancement should be partially reversed by COX2 inhibitor (CEL) in vivo (Figure 4C-F). Additionally, Klf5/Cox2 axis activation reduced the number of tumor-infiltrating CD8+ T cells, and CEL partially increased CD8+ T cell infiltration (Figure 4G-J). To decipher the Klf5-mediated alteration of the immune landscape in the TME, multicolor flow cytometry was performed to profile the infiltrating immune cell components in the TME (Figure 4K). Genetic ablation of Klf5, FZU00,004 (a KLF5 inhibitor) and CEL failed to reduce the frequency of tumor-infiltrating Tregs (Cd4+Cd25+Foxp3+ T cells), but Klf5 depletion increased the number of Cd3+Cd8+ T cells and increased the Cd3+Cd8+/Treg ratio (Figure 4L-N). The proliferation and function of T cells were further examined. Inducible costimulator (ICOS) is a conserved marker of proliferated T cells 40. Klf5 silencing led to marked augmentation of Icos-positive populations in both Cd4+ and Cd8+ T cells (Figure 4O, R). Regarding T-cell functionality, Klf5 knockdown in tumors facilitated Cd4+ T cells to secrete interferon gamma (Ifnγ), while blockade of the Klf5/Cox2 axis enhanced the Ifnγ release of Cd8+ T cells (Figure 4P, S). Additionally, we detected the number of Pd1+ cells, showing that Klf5 deficiency resulted in a reduced number of Cd8+Pd1+ T cells but not Cd4+ T cells (Figure 4Q, T). In the transcription profile, we observed increased Cxcr6 expression of Klf5 KD tumors. Cxcr6 is a classical biomarker of resident memory CD8+ T (Trm) cells to sustain tumor control 41, 42, and our results demonstrated that Klf5 deletion specifically promoted the infiltration of Cd8+Cxcr6+ T cells in tumors (Figure 4N). Thus, blocking the Klf5/Cox2 pathway within cancer cells may increase the quantity and activity of antineoplastic T-cell populations, causing the expansion of Trm cells, which protect against tumorigenesis.
## Blocking the Klf5/Cox2 pathway synergizes with the antitumorigenic effects of anti-Pd1 therapy
To investigate whether inhibition of the Klf5/Cox2 axis reinforces the efficiency of immune checkpoint blockade and considering the high expression of Klf5 in EMT6 and CT26 cell lines, ablation of cancer cell-intrinsic Klf5 in the CT26 colon and EMT6 breast cancer models was first applied to test the hypothesis. Mice with EMT6 or CT26 tumors were unresponsive to anti-Pd1 monotherapy, while the anti-Pd1 blocker resulted in obvious tumor regression and prolonged survival in mice with Klf5 KD tumors (Figures 5A and S4A). Furthermore, we combined FZU00,004 or celecoxib and an anti-Pd1 inhibitor in murine tumor models. Monotherapy with FZU00,004 or celecoxib could moderately reduce tumor growth, whereas the combination markedly controlled tumor growth, resulting in tumor eradication in several cases and increased overall survival in two tumor models (Figure 5B-C and S4A). Additionally, these mice inoculated with control or Klf5-deficient EMT6 cells experienced complete tumor remission, then they were rechallenged with EMT6 cells or CT26 cells, and they were resistant against EMT6 cells but facilely developed CT26 tumors, suggesting that they formed immune memory (Figure 5C). These results highlight that Klf5/Cox2 blockade can potentiate the efficacy of immune-targeting drugs in preclinical models.
## Single-cell and spatial analyses decipher KLF5-mediated alterations in tumor-infiltrating immune compartments
To evaluate whether KLF5 alters the human (TIME), we reanalyzed our previous and public datasets, including scRNA-seq, bulk RNA-seq and spatial transcriptome (ST)43, 44. In the public dataset, 6 TNBC samples were simultaneously detected by scRNA-seq and bulk RNA-seq. These samples were divided into two groups (3 for each group) based on KLF5 expression in bulk RNA-seq (Figure 6A-B). First, we performed unsupervised clustering analysis on integrated single-cell profiles from KLF5low and KLF5high tumors to define major immune cell clusters. A total of 10 distinct clusters were annotated based on the expression of classic biomarkers (Figure 6C and S5A).
Compared with the number in KLF5high tumors, the number of CD4+ and CD8+ T lymphocytes was significantly increased in samples from KLF5low tumors, whereas the percentage of monocytes was markedly reduced (Figure 6D). Furthermore, we reclustered T cells into several subpopulations, and seven subsets were identified (Figure 6E). T lymphocyte subpopulations were primarily defined as CD8+ or CD4+ T cells. CD4+ T lymphocytes were identified as follicular helper T cells (Tfh), central memory T cells (Tcm), effector memory T cells (Tem) and regulatory T cells (Treg) based on the expression of the corresponding markers (Figure 6E and S5B). Likewise, CD8+ T lymphocytes were characterized as exhausted T cells (Tex), effector memory T cells (Tem) and tissue-resident memory T cells (Trm) based on the classical markers (Figure 6E and S5B) 37. The proportions of CD4+IFNγ+ Tem, CD8+GZMB+ Tem and CD8+CXCR6+ Trm cells were dramatically upregulated in KLF5low tumors, while the relative ratio of CD8+LAG3+ Tex cells was low (Figure 6F). When we performed functional analysis of CD4+IFNγ+ Tem cells and CD8+CXCR6+ Trm cells, the genes (ICOS and IFNγ) involved in the proliferation and function of effector T cells were enriched in KLF5low cells (Figure 6G). Additionally, we scored the gene signatures within all CD8+ T lymphocytes, revealing that T-cell receptor signaling, the IFNα response, the IFNγ response, oxidative phosphorylation and PI3K/AKT/mTOR signaling were enriched in KLF5low tumors; by contrast, o-glycan biosynthesis, angiogenesis and linoleic acid metabolism were enriched in KLF5high samples (Figure 6H). In addition, we explored whether KLF5 affects the spatial distribution of T lymphocytes. ST analysis was performed to map the location of KLF5, CD4+ T cells and CD8+ T cells. KLF5 was coexpressed with basal markers (KRT5, KRT14 and KRT17) in breast cancer tissue. By contrast, CD4+ and CD8+ T lymphocytes spatially prevailed in KLF5low regions (Figure 6I-J and S5E-F). The expression of these biomarkers in the ST sample was also validated by immunohistochemistry (IHC), and similar results were observed (Figure S5D). Taken together, the results demonstrated that both CD4+ and CD8+ T lymphocytes were abundant in KLF5low tumors and displayed enhanced proliferation and functionality.
## A KLF5-associated immune gene score exhibits independent prognostic utility
To explore whether the molecular features of the KLF5/COX2-driven immune microenvironment exist in human BLBC, we assessed transcriptomic profiles from Fudan University Shanghai Cancer Center (FUSCC)45-47. TGFβ has been found to inhibit KLF5-induced protumor activity 25, 48; consequently, cases with low TGFβ expression in FUSCC were collected for further analysis. First, we examined the expression of KLF5 and COX2 in the subtypes of TNBC, revealing that both KLF5 and COX2 were highly expressed in the BLBC subpopulation (Figure S6A). Next, the distinct gene expression between KLF5 low expression and high expression with changes > 1.5-fold and $p \leq 0.05$ was considered (Figure 7A). GSEA and GO analysis were performed to evaluate the biological processes based on the transcriptional changes in the samples with low and high levels of KLF5. Several immune-associated pathways, including “T-cell receptor signaling”, “cytokine‒cytokine receptor interaction” and “negative regulation of T-cell apoptosis”, were enriched in the KLF5low group (Figure 7B and Figure S6B). Additionally, bioinformatic analysis of immune cell composition demonstrated that intratumoral NK cells, naïve CD4+ T cells, CD4+ T helper 1 (Th1) cells, total CD8+ T cells, CD8+ central memory cells and effector memory T cells were abundant in the KLF5low group, while M2 macrophages and CD4+ T helper 2 (Th2) cells were positively associated with KLF5 expression (Figure 7C). To assess the prognostic value of KLF5/COX2-driven immune profiles, we further generated a KLF5/COX2-associated immune score (KC-IS) based on the integration of KLF5/COX2-mediated immune genes (Table S3). The BLBC patients were stratified according to the KC-IS, showing that patients with high KC-IS exhibited a poor prognosis (Figure 7D). Similarly, in the colon cohort, KLF5+/CD8- was associated with poor survival (Figure S6C-D). In summary, KC-IS is a potent indicator of the outcome in BLBC and colon cancer.
## Discussion
Given that the KLF5 transcription factor promotes tumor proliferation, invasion and stemness in diverse cancers 9, its role in antitumor immunity remains largely unknown. In the present study, KLF5 deficiency impeded breast tumor growth by increasing the infiltration and functionality of antineoplastic T cells. Mechanistically, KLF5 modulates PGE2 production by transcriptionally activating COX2. Genetic or pharmacological inactivation of the KLF5/COX2 axis develops an immune-supportive microenvironment and sensitizes tumors to anti-PD1 therapy. In single-cell analysis, low expression of KLF5 was positively correlated with enrichment of CD4+IFNγ+ Tem, CD8+GZMB+ Tem and CD8+CXCR6+ Trm cells. Importantly, KLF5/COX2-mediated immune profiles display prognostic value in breast and colon cancer.
Accumulating evidence has shown that KLF5 may remodel the tumor microenvironment. In our results, genetic ablation of KLF5 not only expedites the proliferation and function of both CD4+ and CD8+ T cells but also induces the accumulation of Cxcr6+ Trm cells in tumors. CXCR6 was highly expressed in CD8+ T cells and was considered a typical marker of Trm cells 41, 42. Trm cells extensively spread over the liver, lung, intestine and regional lymph nodes. In the TME, CCR7+ dendritic cells recruit CXCR6+ Trm cells by releasing the CXCR6 ligand CXCL1642. ICOS stimulation hinged the optimal production of Trm cells 49. Additionally, our results showed that Klf5 deletion contributed to a profound increase in CD8+ICOS+ T cells, which may cause the accumulation of Trm cells in KLF5-deficient tumors. Functionally, CXCR6+ Trm cells are required to sustain the proliferation and antitumor effects of cytotoxic T lymphocytes 42, 50. CXCR6+ Trms control tumor growth and metastasis 42, 50, 51 and are equipped with immunosurveillance to restrain tumor recurrence 52, 53. A recent study demonstrated that Klf5 loss led to a reduced number of myeloid-derived cells, particularly granulocytic myeloid-derived suppressor cells (gMDSCs), but an augmented number of both CD4+ and CD8+ T cells in pancreatic cancer models 26. However, the molecular mechanisms were not completely addressed. First, cancer stem cells (CSCs) were found to mediate tumor immune evasion. These CSCs secrete chemokines such as CCL1 and CCL5 to recruit MDSCs; in turn, MDSCs support CSC proliferation 54. Notably, KLF5 is a key transcription factor that maintains tumor stemness 9, suggesting that KLF5 may impair antitumor immunity through the sustainability of neoplastic stemness. Additionally, tumor cells release many damage-associated molecules (DAMs), including double-stranded DNA (dsDNA), dsRNA, and single-stranded RNA (ssRNA), under anaerobic and esurient conditions. These DAMs stimulate innate and adaptive immune responses by interacting with their pattern recognition receptors (PRRs) 55. In these processes, dsDNA sensors, such as the cGAS/STING axis, and RNA susceptors, including several Toll-like receptors (TLRs), RIG-1 and MDA5, contribute to activating the production of type I interferon, which strengthens the antitumor immune response or induces PD-L1-mediated immunotolerance 56, 57. A recent study showed that ablation of KLF5 reduced the mRNA levels of STING and MDA5 58. KLF5 was hypothesized to be responsible for sustaining high PD-L1 expression by increasing STING and MDA5 transcription, which resisted the immune killing effect. Ultimately, KLF5 modulated the secretion of various inflammatory chemokine factors. KLF5 silencing lessened the mRNA expression and release of interleukin 6 (IL6) and IL8 59. Likewise, an acetylation-mimicking mutant of KLF5 resulted in a marked increase in cancer-promoting IL18, IL6 and IL11 60, and acetylated KLF5 functioned as a tumor suppressor 48. Mechanistically, unacetylated KLF5 inhibits the activity of STAT1 and STAT3, two main transcription factors of inflammatory chemokine factors 61, 62. Our transcriptomic analysis suggested that CXCL5 was elevated in the KLF5low group. Likewise, p300-acetylated KLF5 was reported to increase CXCL5 transcription 25. The potential mechanism may be that acetylated KLF5 is prone to ubiquitination and degradation 63. Therefore, KLF5 contributes to the formation of a protumorigenic microenvironment by facilitating the release of inflammatory factors.
The COX2/PGE2 pathway is a key determinant of the inflammatory response. However, the influence of KLF5 on this pathway remains unclear. Initially, KLF5 deletion reduces COX2 mRNA expression, further inhibiting the release of PGE2 and PGF2a 59. Furthermore, KLF5 binds to the COX2 gene promoter to increase COX2 expression at the transcriptional level 39. In the present study, COX2-associated lncRNAs (Ptgs2os2 and Ptgs2os) were positively correlated with Klf5 expression. These lncRNAs activated the transcription of COX2 (encoding the *Ptgs2* gene) in an RNA-enhancing manner 64. LncRNAs may mediate KLF5-activated COX2 expression. As a key enzyme, mPGES1 directly converts PGG2 or PGH2 to PGE2, and it is highly expressed in TNBC 14. Our previous study demonstrated that mPGES1 is a direct target gene of KLF5, and inhibition of KLF5/mPGES1 signaling decreased the conversion of PGE2 from PGH214. Hence, KLF5 likely contributes to PGE2 production twofold. Although Ptgs2 inductions in cDC1s contributes to CD8+T cell expansion, they have just examined the impact of Ptgs2 on the priming stage for anti-tumor immunity, rather than in the TME, which involves many distinct processes 65. In TMEs, the COX2/PGE2 axis in tumor cells or stromal cells are both equipped with immunosuppression 32, 33, 66. With growing interest in the interactions between stromal cells and immune cells 67, there has been reported that COX2+ lung adventitial fibroblasts (AdvFs) drive myeloid cell dysfunction or immunosuppression. Furthermore, Tumor-driven IL-1b reinforces myeloid cell reprogramming by COX2+ lung AdvFs 68. Mechanically, PGE2 induces CXCL12 expression to recruit MDSCs by interacting with its receptor CXCR4 69. Additionally, PGE2 blocks the differentiation of monocytes to dendritic cells (DCs) but redirects monocytes developing to MDSCs 70. Furthermore, PGE2 promotes PD-L1 expression on tumor-associated macrophages and MDSCs 71. Overall, PGE2 promotes the recruitment and activation of immunosuppressive cells to destroy antitumor immunity. Similarly, PGE2 directly impairs antitumor effector cells, including NK cells and T cells. Deletion of PGE2 receptors on NK cells enhances the cytotoxic activity of NK cells and further activates the T-cell-mediated adaptive antitumor immune response 33. During the process, NK cells secrete CXCL1 and CCL5, which recruit conventional type 1 dendritic cells (cDC1) and CD8+ effector T cells, respectively. Furthermore, cDC1 stimulates the proliferation and functionality of CD8+ effector T cells by releasing IL12. Consistently, genetic silencing of KLF5 in tumors resulted in a marked increase in CXCL1, CCL5 and their receptors and enhanced IL12 production. Consequently, the KLF5/COX2 pathway may destroy NK cells and T-cell-modulates antitumor immunity by producing PGE2.
Targeting the KLF5/COX2/PGE2 axis may be an effective therapeutic strategy in diverse cancers, including BLBC. Mifepristone is an effective inhibitor of KLF524. Similarly, mifepristone led to immunogenic cell death of tumor cells, subsequently increasing the infiltration of MHC-II+ DCs, natural killer cells and CD8+ central memory T cells to sensitize the tumors to anti-PD1 blockers 72. Given this evidence, anti-inflammatory drugs targeting COX2 or mPGES1 succeeded in improving immune escape and synergizing with the efficacy of ICBs 32, 33, 73, 74. Mechanistically, inhibition of COX2 or mPGES1 decreased the infiltration of MDSCs but increased the number and functions of cytotoxic cells such as NK cells and CD8+ T cells. Because COX2 inhibitors have cardiac side effects, blocking PGE2 receptors may be a promising method. Several inhibitors targeting EP2 or EP4 have been found to potentiate anti-PD1 efficacy and shift the “cold” to the “hot” tumor microenvironment 34, 66, 75.
In summary, our results indicate the potential of the KLF5/COX2/PGE2 axis as a therapeutic target to improve the efficacy of ICBs in BCLC and other cancers.
## Conclusions
In conclusion, the present findings decipher the effect of KLF5-induced PGE2 generation modulation on cancer immune escape, highlighting an immunostimulatory role of KLF5 inhibitors for cancer therapy. Furthermore, KLF5 blockers in combination with ICBs may provide a novel therapy in cancer immunotherapy.
## Funding
This work was supported by the National Key Research and Development Program of China (2020YFA0112300 and 2018YFC2000400), National Natural Science Foundation of China (82203629, 82060542, 81830087, 82273216, 81773149 and U2102203), Basic Research Major Project of Yunnan Province (202101AS070010), Top Young Talents of Ten Thousand Talents Plan in Yunnan Province (YNWR-QNBJ-2019-275), Yunnan Fundamental Research Projects (202101AS070010 and 202101AS070050), the Leader in Oncology of Yunnan Province (D-2019029), the Precision Oncotherapy Innovation team of Kunming Medical University (CXTD202109), Shanghai Pujiang Program (22PJD054), Shenzhen Municipal Government of China (JCYJ20210324103603011) and GuangDong Basic and Applied Basic Research Foundation Special Projects---Hybribio Biotech CO. Joint Funds [22202104030000530].
## Author contributions
Qi Wu designed and performed most experiments. Zhou Liu and Tiantian Wang helped perform the animal experiments, receiving help from Yao Luo for mouse model establishment. Chuanyu Yang performed IHC staining. Fubing Li and Juanjuan Li collected and collated documents. Zhijie Gao and Xiangyu Meng performed bioinformatic analysis. Chao Dong provided financial support and analyzed the data. Ceshi Chen and Si Sun designed the project and supervised the study. All the authors contributed to the article and approved the submitted version.
## Availability of data and materials
The datasets used and analyzed during the current study are available within the manuscript and its additional files.
## Ethics approval and consent to participate
The study was approved by the Institutional Ethics Committee of Renmin Hospital of Wuhan University (approval no. 2018K-C09).
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|
---
title: The delta subunit of the GABAA receptor is necessary for the GPT2-promoted
breast cancer metastasis
authors:
- Na Li
- Xiang Xu
- Dan Liu
- Jiaxin Gao
- Ying Gao
- Xufeng Wu
- Huiming Sheng
- Qun Li
- Jun Mi
journal: Theranostics
year: 2023
pmcid: PMC10008743
doi: 10.7150/thno.80544
license: CC BY 4.0
---
# The delta subunit of the GABAA receptor is necessary for the GPT2-promoted breast cancer metastasis
## Abstract
Objectives: Glutamic pyruvate transaminase (GPT2) catalyzes the reversible transamination between alanine and α-ketoglutarate (α-KG) to generate pyruvate and glutamate during cellular glutamine catabolism. The glutamate could be further converted to γ-aminobutyric acid (GABA). However, the role of GPT2 in tumor metastasis remains unclear.
Methods: The wound healing and transwell assays were carried out to analyze breast cancer cell migration and invasion in vitro. Gene ontology analysis was utilized following RNA-sequencing to discover the associated molecule function. The mass spectrometry analysis following phosphoprotein enrichment was performed to discover the associated transcription factors. Most importantly, both the tail vein model and Mammary gland conditional Gpt2-/- spontaneous tumor mouse models were used to evaluate the effect of GPT2 on breast cancer metastasis in vivo.
Results: GPT2 overexpression increases the content of GABA and promotes breast cancer metastasis by activating GABAA receptors. The delta subunit GABRD is necessary for the GPT2/GABA-induced breast cancer metastasis in xenograft and transgenic mouse models. Gpt2 knockout reduces the lung metastasis of the genetic Gpt2-/- breast cancer in mice and prolongs the overall survival of tumor burden mice. Mechanistically, GPT2-induced GABAA receptor activation increases Ca2+ influx by turning on its associated calcium channel, and the surged intracellular calcium triggers the PKC-CREB pathway activation. The activated transcription factor CREB accelerates breast cancer metastasis by upregulating metastasis-related gene expressions, such as PODXL, MMP3, and MMP9.
Conclusion: In summary, this study demonstrates that GPT2 promotes breast cancer metastasis through up-regulated GABA activation of GABAAR-PKC-CREB signaling, suggesting it is a potential target for breast cancer therapy.
## Introduction
Increased glutamine metabolism is a hallmark of cancer. Proliferating tumor cells utilize glutamine's carbon for energy production and its nitrogen for the biosynthesis of nonessential amino acids, nucleotides, and other molecules 1. For example, renal cell carcinomas and colorectal cancers are glutamine-addicted 2, 3. Glutamine is instantly converted to glutamate by glutaminase, of which activity was also correlated with a malignant phenotype 4-10.
The reversible transamination is catalyzed by a transaminase GPT between pyruvate/glutamate and alanine/α-ketoglutarate (α-KG). GPTs play essential roles in gluconeogenesis and amino acid metabolism in many tissues, including skeletal muscle, kidney, and liver 11. GPT1 locates in the cytosol; a biomarker used clinically in liver diseases. The GPT2 protein is more abundant than GPT1, especially in muscle and fat, suggesting a distinct role of GPT2 in the metabolism and homeostasis of glucose, amino acids, and fatty acids 12. Under metabolic stress, GPT2 expression is upregulated in various tumor cells, including breast carcinomas, and the viability of pancreatic cancer cells was decreased when GPT2 activity was inhibited 13-16.
In the meantime, glutamate can also be converted into γ-aminobutyric acid (GABA) by glutamate decarboxylase. GABA is a primary inhibitory neurotransmitter and activates specific GABA receptors expressed in the central nervous system and many non-neuronal peripheral tissues 17-19. GABA receptors include three distinct classes, GABAA, GABAB, and GABAC. GABAC receptors are classified as a subtype of GABAA receptors 20, and both GABAA and GABAC receptors are ionotropic or channel receptors 21, 22. GABA signaling plays a vital role in cell differentiation and proliferation of peripheral organs and tumorigenesis 19, 23, 24. However, its role in tumor metastasis is controversial. The GABA was shown to promote tumor cell migration by inducing extracellular metalloproteinases (MMPs) 24-29, whereas the early studies showed GABA had a strong inhibitory effect on sympathicus-driven cancers 30-32. To date, it is still unclear about the detailed mechanism of how GABA signaling regulates tumor metastasis.
GABAA receptors are heteromeric complexes composed of 2α, 2β, and either one of the γ-subunit or δ, ε, θ, π, or ρ subunit, which were encoded by GABR genes GABRA (1 to 6), GABRB (1 to 3), GABRG (1 to 3), GABRD, GABRE, GABRQ, GABRP, and GABRRs, respectively 33 34. In contrast, GABAB receptors are obligatory heterodimers composed of R1 and R2 subunits 35 36. GABAA receptors are reported to modulate calcium influx by associating with calcium channels 37-39. Calcium signaling is essential in breast cancers since the breast is intrinsically linked to calcium production during lactation. Calmodulin is a critical calcium sensor and regulates many protein kinases/phosphatases' activities through a Ca2+-dependent manner 40-43, including calcium-dependent protein kinases and calmodulin-binding proteins. The distribution of these calmodulin-regulated proteins varies among tissues 44. Identifying and characterizing these calmodulin-binding/targeting proteins are essential to define the pathway by which Ca2+-regulated signals are transduced.
In this study, we found that GPT2 promoted breast cancer metastasis by activating the GABAA receptor, and the delta subunit is necessary for this activation. The activated GABAA receptor increased calcium influx, and the latter upregulated the CREB-targeted gene expression, which drives breast cancer metastasis.
## Tissue specimens
Clinical breast cancer samples were collected from Ruijin Hospital, affiliated with Shanghai Jiao Tong University School of Medicine. The Ruijin Hospital Medical Ethical Committee approved the clinical ethics. All patients in this study had a pathological breast cancer diagnosis before surgery and signed informed consent. All experiments were performed following the local government policy and the Helsinki declaration.
## Mice
The animal studies were approved by the Animal Care and Use Committee of Shanghai Jiao Tong University School of Medicine, and conducted in accordance with the established national and institutional guidelines for the use of laboratory animals.
## Cells and reagents
Human breast cancer cell MCF-7 and mouse breast cancer cells (PY8119) were cultured in Dulbecco's modified Eagle's medium (DMEM) (Cat # L110KJ, BasalMedia, Shanghai, China); and the human breast cancer cells BT-549 were cultured in the RPMI-1640 (Cat# L210KJ, BasalMedia, Shanghai, China). MDA-MB-453 were cultured in Leibovitz's L-15 Medium (Cat# 11415064, Thermo Fisher Scientific, CA, USA). All media were supplemented with $10\%$ FBS (fetal bovine serum, Cat# 10270-106, Gibco, NY, USA) and 50 IU of penicillin/streptomycin (Cat# S110JV, BasalMedia, Shanghai, China) in a humidified atmosphere with $5\%$ CO2 at 37 °C.
Antibodies against GPT2 (16757-1-AP), GAD1 (10408-1-AP), PKA (55382-1-AP), CaMKII (12666-2-AP), NF-κB p65 (10745-1-AP), and IκB (15649-1-AP) were purchased from Proteintech. Antibodies against PKC (#9372), phospho-PKC (Thr$\frac{410}{403}$) (#9378), phospho-CaMKII (Thr286) (#12716), phospho-PKA (Thr197) (#4781), phospho-NF-κB p65 (Ser468) (#3039), and phospho-IκBα (Ser32) (#2859) were purchased from Cell Signaling Technology. Antibodies against CREB (ab32515) and phospho-CREB(Ser133) (ab32096) were purchased from Abcam. 3-MPA [63768] and GABA (A2129) were purchased from Sigma. BAPTA (S7534), GABA (S4700) and 666-15 (S8846) were purchased from Selleck. Picrotoxin (HY-101391) and CGP52432 (HY-103531) were purchased from MCE.
## Western blotting
Cells were washed twice with PBS and lysed on ice for 20 min in RIPA lysis buffer supplemented with protease inhibitors [1 mM PMSF, 1 mg/L aprotinin, 1 mg/L leupeptin, and 1 mg/L pepstatin] and phosphatase inhibitors [1 mM Na3VO4 and 10 mM NaF]. The protein concentration was measured using a BCA assay kit (Cat# BCA02, Dingguo, Beijing, China). Subsequently, the PAGE-separated proteins were transferred to PVDF membranes that were then separately incubated with indicated antibodies. The blots were visualized using a LAS 4000 instrument (GE Healthcare).
## Phosphorylated protein enrichment
Phosphoprotein enrichment (Cat# BB-3108, Bestbio) was performed following the manufacturer's instructions for phosphorylated protein enrichment assays as previously described. Cells were washed three times with $0.9\%$ saline and then lysed in prechilled lysis buffer (500 μL per 5 × 106 cells) at 4 °C for 40 min. Extracts were centrifuged at 14,000 × g for 15 min at 4 °C, after which all samples were adjusted to a protein concentration of 0.25-0.5 mg/mL and passed through a phosphoprotein-enriching column. Phosphorylated proteins were eluted with 400 μL elution buffer. Samples were stored at -80 °C for mass spectrometry or immediately boiled with 1 × SDS loading buffer for 10 min and then analyzed by Western blotting.
## Real-time PCR
Total RNA was isolated with TRIzol reagent (Cat# 15596026, Invitrogen, CA, USA), and cDNA was synthesized using a PrimeScript RT Reagent Kit (Cat# RR037A, Takara, Kyoto, Japan) for real-time PCR with a mixture of oligo dT and random primers after genomic DNA elimination. Real-time PCR was performed with an ABI-7500 instrument (Applied Biosystems) to measure mRNA expression using a 2 × SYBR Green qPCR Master kit (Cat# A0001, EZBioscience, MN, USA) according to the manufacturer's instructions. The relative expression levels were calculated by determining the samples' threshold cycle (Ct) values. All data were normalized to the internal control β-actin.
The primers used for the real-time PCR analysis were as follows: GPT2, forward (5′- GGAGCTAGTGACGGCATTTCTACGA-3′) and reverse (5′-CCCAGGGTTGATTATGCAGAGCA -3′); β-actin, forward (5′-GCGGGAAATCGTGCGTGACATT-3′) and reverse (5′-GATGGAGTTGAAGGTAGTTTCG-3′); MMP2, forward (5'-CAGGCTCTTCTCCTTTCACAAC-3') and reverse (5'-AAGCCACGGCTTGGTTTTCCTC-3'); MMP3, forward (5′-CTGGACTCCGACACTCTGGA-3′) and reverse (5′-CAGGAAAGGTTCTGAAGTGACC-3′); MMP-9, forward (5'-TGGGCTACGTGACCTATGACAT-3') and reverse (5-GCCCAGCCCACCTCCACTCCTC-3'); PODXL, forward (5′-TCCCAGAATGCAACCCAGAC-3′) and reverse (5′-GGTGAGTCACTGGATACACCAA-3′); and PLAT, forward (5′-AGCGAGCCAAGGTGTTTCAA-3′) and reverse (5′-CTTCCCAGCAAATCCTTCGGG-3′).
## RNA-Seq
RNA-sequencing analysis was performed by BGI accomplished. After total RNA extraction, mRNA was isolated by Oligo Magnetic Beads and cut into tiny fragments for cDNA synthesis. Following the manufacturer's instructions, libraries were generated using the NEB Next UltraTM RNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA) in the Illumina system. Sequencing was conducted using the Illumina Hiseq XTEN platform.
## Gene knockout by the CRISPR/CAS9
For knock-out GABRA subunits, the gRNAs were designed and synthesized: GABRA1-sgRNA: AGCTGAATGTCCGATGCATTTGG; GABRA5-sgRNA: CAACAGACTTCGGCCCGGGCTGG; GABRB1-sgRNA: CAGGGCCCCCCGTCGACGTTGGG;
GABRB2-sgRNA: GCTGCTTTCTTTTGGCGTTGGGG; GABRB3-sgRNA: CCACTCGATTGTCAAGCGTGAGG; GABRD-sgRNA: ACACGCCGCGGTTCCTCCGCAGG; GABRE-sgRNA: ACAGAGGCGTTCGTCGTACCAGG;
GABRP-sgRNA: CACTCTGGATGCCCGCCTCGTGG; GABRQ-sgRNA: GAACGGTGCGGTACGGCATCCGG; GABRG3-sgRNA: AAGAGTCACGTCGGTGTCTTGGG.
*These* gene fragments were cloned into the vector of plentiCRISPRv2. Lentiviruses were generated by co-transfection into HEK293T cells with one of the above recombinant plasmids and packaging plasmids (psPAX2 and pMD2G). After 48 h of lentivirus infection, Puromycin (1 mg/mL) was added to the cells to screen for stable cells.
## Luciferase reporter assays
A traditional dual-luciferase assay consisting of NF-κB, NFAT, or CREB-binding sites reporter was used to determine the transcription factors in response to GPT2 overexpression or GABA treatment as previously described. Briefly, cells were co-transfected with luciferase reporter constructs pGMNF-κB-Luc, pGMNFAT-Luc, pGMCREB-Luc vectors, and Renilla reporter plasmid. Twenty-four hours after transfection, the luciferase activity was examined by a dual-luciferase reporter assay system (Promega). The firefly luciferase activity was normalized to the Renilla activity. Luciferase activities are presented as folds increased over the luciferase activities in unstimulated conditions.
## Cell migration and invasion assays
BD cell culture inserts (24-well insert, 8-μm pore size) were utilized following the manufacturer's instructions. *In* general, cells were pre-treated with the inhibitors for 24 h. Then, cells (2 × 104) suspended in a 200 μL serum-free medium were seeded into the upper chamber of the inserts. The 500 μL medium of $10\%$ FBS was added to the lower sections. The rooms were incubated at 37 °C for 24 h. After incubation for 24 h at 37 °C, cells on the upper of the membrane were scraped by a cotton swab. Then, the membrane was fixed with $4\%$ paraformaldehyde and stained with $0.5\%$ crystal violet solution. Five random fields per well were counted under a light microscope, and each independent experiment was repeated at least three times. The control group normalized the migration ratio. For invasion assay, cells (5 × 104) were loaded into BD cell culture inserts (24-well insert, 8-μm pore size), which were coated with Matrigel (BD Biosciences, Franklin Lakes, NJ, USA).
## Scratch assay
Cells grew to a cell density of approximately $100\%$ in six-well plates. Straight scratches were made with 200 μL pipette tips, after which the cells were washed twice with PBS and cultured in RPMI-1640. The lengths of scratches were recorded every 24 h.
## Calcium imaging
Cells overexpressing or depleted of GPT2 were incubated in Hank's balanced salt solution containing the calcium-sensitive fluorescent dye Fluo-2/AM (Cat# F1201, Invitrogen, CA, USA) for 30 min at 37 °C, and picrotoxin or CGP52432 were added at concentrations of 100 μM and 33 μM, respectively. Fluorescence intensity was measured with an Olympus confocal laser scanning microscope (DU-897D-CS0) and MetaMorph software, where excitation was performed at 340 and 380 nm, after which a comparative analysis of the two emission values was performed. Serial scanning was performed at excitation wavelengths of 340 and 380 nm at 2 s intervals, and the fluorescence intensity of each emission from each cell was detected. The fluorescence intensity changes (F340/F380) indicate the intracellular calcium concentration.
## Liquid chromatography-mass spectrometry analysis
After treatment, one million cells were collected in 1 mL of -80 °C $80\%$ methanol. Samples were vigorously vortexed and frozen, followed by thaw on ice. The freeze and thaw were repeated three times. Then, the supernatant was collected for HPLC analysis of GABA after centrifugation at 13,000 × g for 15 min.
One million cells were cultured with a DMEM medium (1mM Gln -13C (Sigma)) of $10\%$ dialyzed FBS to trace the flux of metabolites. After 24 h, the cell culture medium and cells were collected separately, and the content of glutamine metabolite GABA in cells or the cell culture medium was detected. The samples were lysed as described above. The supernatant was evaporated, and the resulting metabolites were resuspended for LC-MS analysis (Thermo Fisher Q Exactive). The HILIC column (150 × 2.1 mm, 3 μm particle size; Waters Inc) was eluted with $5\%$ mobile phase A (10 mM ammonium formate and $0.1\%$ formic acid in water) for 1 min, followed by a linear gradient to $80\%$ mobile phase B (acetonitrile with $0.1\%$ formic acid) over 25 min. The raw data were processed using Thermo Xcalibur 3.0 software (Thermo Fisher).
## Histochemistry and Immunohistochemistry (IHC)
For H&E staining, the sections of human and mouse tumors were deparaffinized, hydrated with deionized water, and immersed in eosin red solution for 30 min at room temperature. Slides were washed with running water for 10 min and counterstained in hematoxylin. Immunohistochemistry staining was performed as previously described 13.
For Immunohistochemistry staining, tissue sections were incubated with GPT2 antibody (1:200, Proteintech), pCREB (1:200, Abcam), pPKC (1:200, Abcam), or MMP9 (1:200, Abcam) overnight at 4 °C following de-paraffinization, and antigen retrieval. Secondary biotin-labeled IgG was then incubated with sections for 30 min at 37 °C. Finally, diaminobenzidine (DAB) was used for visualization.
Histochemistry score (H-SCORE) is based on the percentage of positive-staining area (0 = <$5\%$, 1 = $6\%$ - $25\%$, 2 = $26\%$ - $50\%$, 3 = $51\%$ - $75\%$ and 4 = $76\%$ - $100\%$) and staining intensity (negative, weak, moderate and strong, graded as 0, 1, 2 and 3, respectively). The proportion and intensity scores were added together and the percentage of positive-staining area in each field was counted as positive area/total area × $100\%$.
## Breast cancer metastasis models via tail vein injection
The sixth week female C57BL/6J mice were randomly divided into Ctrl, GPT2-OE, NC, and GPT2-KD groups, with six mice in each group. 1 × 106 mouse breast cancer firefly luciferase-PY8119 cells suspended in 100 μL PBS were injected into the tail-vein. After three days, the mice were injected with GABA (25 mg/kg), picrotoxin (2 mg/kg), or 666-15 (10 mg/kg) by intraperitoneal injection every other day.
Four weeks later, live animal imaging was performed. The mice were anesthetized with isoflurane (#R510-22, WRD). Each mouse was injected intraperitoneally with D-luciferin potassium salt (#ST196, Beyotime) dissolved in PBS at 150 mg/kg concentration. Ten minutes later, the mice were placed on the IVIS stage and imaged in the bioluminescence to evaluate breast cancer metastasis capacity by using an IVIS Spectrum In Vivo Imaging System (PerkinElmer). Living image software was used to analyze bioluminescent images.
## Mammary gland conditional Gpt2-/- tumor metastasis mouse models
To study the GPT2 effect on breast cancer metastasis in vivo, conditional *Gpt2* gene knockout mice in the mammary gland were generated. First, homozygous floxed Gpt2 alleles (Gpt2fl/fl) mice were generated by means of the CRISPR/Cas9 and Cre-loxP technology. The *Gpt2* gene ID of mice was 108682 and the ATGGCGGTGAACACTAAGGTGGG and GTGCTAGGCAGGCGGGATATAGG were selected for construction of Gpt2 guide RNA. Then, Gpt2fl/fl mice were crossed with MMTV-PyMT transgenic mice 45, which express the polyoma virus middle T oncogene (PyMT) under the control of the mouse mammary tumor virus (MMTV) LTR promoter and serve as metastatic model of autochthonous breast cancer. All mice have been maintained on a C57BL/6 background.
PCR verified the genetic type of mice. Genomic DNA was prepared from the mouse tail using the fast tissue-to-PCR kit (#K1091, Fermentas). The primer pairs used were as follows: The Gpt2 flox: Forward, 5′-GGATGGATGAGCCCAAATA-3′ and Reverse, 5′-AGGCTGCCACATCTTCACG-3′. DNA band was visualized on $2\%$ agar gels stained with GelRed (41003, Biotium). All tumor and lung tissues used in this study are from female mice. For statistical analysis of mice with lung metastases from primary breast cancer, Gpt2+/+ and Gpt2-/- MMTV-PyMT C57BL/6J mice ($$n = 10$$) at 20 weeks were sacrificed to observe the foci of lung by gross appearance and H&E staining. Tumor growth was monitored by palpation, twice per week from 8 weeks. The real death of mice or the tumor size more than 2 cm3 was identified as a survival endpoint to draw a Kaplan-Meier survival curve.
## Statistical analysis
The data are presented as the means ± SD. The unpaired two-tailed t-test and two-way ANOVA were used as indicated. Statistical significance was defined as $p \leq 0.05$ unless otherwise stated. Each experiment was repeated independently with similar results. * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001.$
## GPT2 promotes breast cancer metastasis
We previously found that GPT2 promotes tumorigenesis in breast cancer 13. Then, to further investigate the association of GPT2 with breast cancer metastasis, we first analyzed the GPT2 expression by IHC in breast cancer with or without metastasis. As shown in Figure 1A, GPT2 expression was increased in metastatic breast cancers compared to primary breast cancers ($p \leq 0.001$). Figures 1B & S1A were the presentative photos of GPT2 immunohistochemistry staining. Further analysis showed that the GPT2 high expression breast cancers were more aggressive, and $40\%$ of breast cancers with GPT2 high expression were prone to metastasis. In comparison, only $27\%$ of GPT2 low expression breast cancers were flat to metastasis (Figures 1C & S1B), indicating that GPT2 is associated with breast cancer metastasis.
To determine whether GPT2 promotes breast cancer metastasis, we performed RNA sequencing on breast cancer BT549 cells. And the gene expression profiles were analyzed by gene ontology analysis in breast cancer cells with or without GPT2 overexpression. As shown in Figure 1D, cell migration and adhesion pathways were two of the top ten activated pathways, suggesting that GPT2 was involved in the metastatic process of breast cancer. Furthermore, the wound healing assay, transwell evaluation, and invasion test showed that GPT2 overexpression significantly promoted migration and invasion of breast cancer BT549 cells (Figures 1E, 1F & S1C); in contrast, depletion of GPT2 inhibited migration and invasion of breast cancer BT549 cells (Figures 1G, 1H & S1D).
In the meantime, we found that GPT2 overexpression also accelerated the migration of breast cancer MCF7 cells (Figure S1E). In contrast, GPT2 depletion reduced the migration of breast cancer MDA-MB-468 cells (Figure S1F), representing a high endogenous GPT2. Moreover, BT549 cells overexpressing GPT2 were more efficient in metastasis after a tail vein injection (Figure 1I). These observations suggested that GPT2 may promote breast cancer metastasis.
## GABA mediates GPT2-promoted migration and invasion of breast cancer cells
As a schematic drawing in Figure 2A, GPT2 is a critical enzyme participating in glutamine metabolism; thus, the glutamine metabolites were analyzed by LC-MS/MS to determine the mechanism GPT2 promotes breast cancer metastasis. As shown in Figure 2B, the intercellular and extracellular GABA concentration decreased in BT549 cells depleted GPT2. In contrast, the GABA content increased in BT549 cells expressing GPT2. Moreover, the glutamine metabolic flux analysis confirmed that GPT2 overexpression increased intracellular GABA production, suggesting GPT2 increased GABA content by increasing glutamate concentration (Figure 2C). Since GABA is an important signal molecule, we proposed that GABA might regulate breast cancer metastasis.
To determine whether GPT2 promoting breast cancer cell migration depends on GABA, we assessed the migration and invasion capability by the wound healing evaluation and the transwell assay in BT549 cells treated with GABA. As shown in Figures 2D-E & S2A, GABA promoted breast cancer cell migration and invasion. Moreover, GABA recovered the migration and invasion ability of breast cancer cells depleted of GPT2 (Figures 2F, 2G & S2B). Moreover, the conditional media from GPT2 overexpressing BT549 cells significantly promoted the cell migration of breast cancer cells depleted of GPT2 compared to the media from parental cells (Figure S2C).
Most importantly, the GABA catabolic enzyme GAD (glutamate decarboxylase) inhibitor 3-mercaplopropionic acid (3-MPA) significantly suppressed breast cancer cell migration (Figure 2H). In the meantime, GAD1 overexpression facilitated breast cancer migration. And the GAD1 overexpression also partially recovered the GPT2 depletion-reduced breast cancer migration (Figure 2I). These observations suggested that GABA as a signal molecule mediates GPT2 promotion of breast cancer cell migration.
## The delta subunit is necessary for GABAA receptor-mediated breast cancer migration
To determine whether the GABA-induced cell migration/metastasis depends on the GABA receptors, we tested the cell migration capability by the wound healing evaluation and transwell assay in breast cancer cells expressing GABA receptors. These cells were separately treated with the GABAA receptor inhibitor picrotoxin or the GABAB receptor inhibitor CGP 52432. As shown in Figures 3A-B, the GABAA receptor inhibitor picrotoxin significantly suppressed GABA-induced cell migration in two breast cancer cells in a dose-dependent manner ($p \leq 0.01$), but not the GABAB receptor inhibitor CGP 52432 (Figures 3C-D). Picrotoxin also inhibited GPT2-induced breast cancer cell migration (Figure 3E).
To further confirm that the GABA-induced cell migration depends on the GABAA receptors, but not GABAB, we knocked out the GABAA-unique β subunits. The GABRB1, GABRB2, and GABRB3 were individually or entirely knocked out. We found that the individual depletion of GABRB1, GABRB2, and GABRB3, all of them suppressed GABA or GPT2-induced breast cancer cell migration, to some extent (Figures 3F/S3A-B & 3G/S3C-D). The total depletion of three subunits dramatically inhibited GABA or GPT2-induced breast cancer cell migration (Figures 3F/S3A-B & 3G/S3C-D), suggesting the GABA-induced cell migration depends on the GABAA receptors, but not GABAB.
To explore the critical subunit(s) of the GABAA receptors involved in GABA-induced breast cancer cell migration, we first analyzed the expression of GABAA receptor subunits based on the TCGA database. We found that the seven GABAA receptor subunits were upregulated in TNBC breast cancers, including GABRA1, GABRA5, GABRD, GABRE, GABRP, GABRQ, and GABRG3 (Figure S3E). Then, these seven upregulated GABR genes were knocked out by CRISPR/Cas9 technology to determine whether these subunits promote cell migration in TNBC cells (Figure S3F). As shown in Figure S3G, the cell migration was reduced or unchanged in BT549 cells knocked out of GABRA1, GABRD, GABRP, GABRQ, or GABRG3, but not the GABRA5 and GABRE depletion.
Only the GABRD knockout inhibited the GABA-induced cell migration in response to GABA treatment. While the knockout of GABRA1, GABRD, GABRP, GABRQ, and GABRG3 have little effect on cell migration (Figures 3H & S3H). Moreover, GABRD depletion inhibited GPT2-induced breast cancer cell migration (Figures 3I & S3I-J). Meanwhile, higher GABRD expression in lymph node positive metastases of breast cancer patients was significantly correlated with poor prognosis (Figure S3K). These results suggested that the GABAA receptor mediates GPT2/GABA-induced breast cancer cell migration, and the δ subunit is necessary for this induced cell migration.
*The* gene ontology (molecule function) analysis showed that calcium-binding signaling was significantly activated in breast cancer cells treated with 100 µM of GABA (Figure 3J), consistent with the previous finding that GABAA receptor activation couples with calcium influx 21, 22, 37-39. Thus, the intracellular calcium influx/concentrations were analyzed to determine whether calcium signaling mediates GPT2/GABA-regulated breast cancer cell migration. As shown in Figures 3K-L, GPT2 overexpression increased calcium influx/concentration while GPT2 depletion reduced calcium influx/concentration. The GABAA receptor inhibitor picrotoxin but not GABAB inhibitor CGP 52432 suppressed calcium influx/concentration (Figure 3M). In addition, the calcium chelator BAPTA also significantly inhibited GPT2-induced breast cancer cell migration (Figure 3N).
To exclude redox's effect on glutamine metabolism on breast cancer cell migration, we analyzed the cellular ROS (reactive oxygen species) by flow cytometry in BT549 cells overexpressing GPT2. As shown in Figure S3L, GPT2 overexpression did not significantly change cellular ROS levels. Also, the oxidative reductase NAC did not markedly affect breast cancer cell migration (Figure S3M). These observations suggested that GABAA receptors mediate GPT2/GABA-induced breast cancer cell migration via modulating calcium influx.
## CREB activation is critical for GPT2/GABA-induced cell migration
As a signal molecule, calcium regulates cellular function mainly through calmodulin to activate various protein kinases and protein phosphatases and consequently phosphorylate/dephosphorylate the downstream targets 44, 46, 47. Thus, the phosphorylated proteins were analyzed by protein mass spectrometry following enrichment. *The* gene ontology analysis of these phosphoproteins showed that the cell-cell adhesion pathway was markedly regulated after GPT2 overexpression in BT549 cells, confirming that GPT2 expression promotes tumor metastasis (Figure 4A).
Five transcription factors that potentially regulate metastasis were selected from the 156 phospho-proteins (Figure 4B). The five transcription factors, including CREB, were further verified by immunoblotting in protein samples collected from the phosphorylation protein enrichment experiments. As shown in Figure 4C, GPT2 overexpression and GABA treatment increased the phosphorylation level of one transcription factor and two histone modification enzymes among five. At the same time, GPT2 depletion decreased the phosphorylation levels (Figure 4C). Therefore, as a well-known transcription factor regulated by calcium signaling, CREB was chosen to determine whether GPT2/GABA promoted breast cancer metastasis via CREB activation.
The promoter-luciferase assay showed that GPT2 overexpression and GABA treatment significantly increased the CREB activity, but not the NF-κB nor the NFAT activity in BT549 cells. Moreover, GPT2 depletion only reduced the CREB activity (Figures 4D & S4A), which was further supported by the markers in their pathways (Figure 4E) and was consistent with the immunoblotting data (Figure 4C). Function analysis showed that CREB overexpression increased the GPT2 depletion-reduced breast cancer cell migration (Figures 4F & S4B). In contrast, the CREB inhibitor 666-15 decreased the GPT2-promoted breast cancer cell migration (Figures 4G & S4C).
The volcano graphic displayed the differentially expressed genes in BT549 cells overexpressing GPT2, including PLAT, PODXL, and MMPs (Figure 4H). The quantitative PCR showed that the GPT2-induced PODXL, MMP3 and MMP9 expression was suppressed by the CREB inhibitor 666-15 (Figure 4I). The upregulation of PODXL, MMP3 and MMP9 are believed to promote cell migration 48-50, suggesting CREB activation is critical for GPT2/GABA-induced breast cancer migration. In the end, we identified the calcium-regulated kinases that potentially phosphorylate CREB from three kinases, CaMKII, PKA, and PKC. As shown in Figure 4J, PKC activation was increased in breast cancer cells expressing GPT2 or treated with GABA. In contrast, GPT2 depletion inhibited PKC activation, indicating that PKC may phosphorylate CREB in response to GABA-induced calcium influx.
## GPT2 knockout inhibits breast cancer metastasis in mice
Finally, we utilized the mice model to verify the promoting effect of GABA/calcium/CREB signaling on tumor metastasis. As shown in Figure 5A, the GABA treatment recovered the GPT2 depletion-suppressed breast cancer metastasis. In contrast, the GABAA receptor inhibitor picrotoxin and CREB inhibitor 666-15 inhibited the GPT2-promoted breast cancer metastasis (Figure 5B).
In order to investigate the effect of GPT2 on breast cancer metastasis, we first introduced *Gpt2* gene knockout C57BL/6 mice, and then mated with MMTV-PyMT mice to generate MMTV-PyMT; Gpt2+/- and MMTV-PyMT; Gpt2-/- mice (Figure 5C). As shown in Figure 5D, the knockout of Gpt2 to some extent extended the overall survival of tumor burden mice compared to the wildtype breast cancer mice model. In the mammary gland conditional Gpt2-/- mouse model, Gpt2 knockout significantly decreased lung metastatic nodules and prolonged the overall survival of tumor burden mice (Figure 5E-F). The immunohistochemistry staining analysis also showed that Gpt2 knockout markedly reduced GABA synthesis, PKC and CREB activation, and MMP9 expression in mouse breast tumors (Figure 5G).
## Discussion
Many studies demonstrated that glutamine metabolism is a hallmark of cancer. Glutamine catabolism is increased in highly proliferating cells for biosynthesis and energy production 51-53. Moreover, the binding of glutamate to its receptors activates SRC family kinases and its downstream signaling, consequently promoting cell proliferation, apoptosis resistance, migration, and invasion of various cancer cell lines 54. In this study, we found that glutamate-derived GABA increases Ca2+ influx through GABAA receptor, and the latter activates transcription factor CREB to promote breast cancer metastasis. We further determined that the GABRD (delta subunit), but not GABRP (pi subunit), is necessary for the GPT2/GABA-induced breast cancer metastasis, which was distinct from the previous finding that patients with metastatic breast cancer expressed eight times of GABRP compared to stages II-IV patients without metastasis 55. Moreover, the other study also showed that GABA promotes pancreatic cancer growth through the GABRP 56. The importance of delta subunit is rare reported.
Calcium, as a second messenger, regulates various cellular functions by binding calmodulin. PKC/CREB signaling was activated in response to GPT2-induced calcium influx and consequently promoted breast cancer metastasis. However, the selectivity of calcium influx-activated signaling is still unclear, which will be determined in the future.
Although the current data strongly support that GPT2 promotes tumor metastasis through the GABA-increased calcium influx, we could not entirely exclude the other possibility for GPT2-induced tumor metastasis since GPT2 regulates the α-KG generation. The latter is also involved in tumor metastasis 57, 58. In addition, it's well known that MDA-MB-231 cells are prone to metastasis. However, none of the GABAA receptors were upregulated in these cells, suggesting there are multiple mechanisms promoting breast cancer metastasis besides GABA-triggered calcium influx.
In brief, this study demonstrated that GPT2 activated GABAA receptors by increasing GABA secretion. The calcium influx-triggered CREB is critical for breast cancer metastasis, suggesting that glutamine metabolism regulates breast cancer metastasis and that the GABAA receptor is a potential target for breast cancer therapy.
## Author contributions
N.L., X.X., and D.L. performed most of the experiments; Y.X. and J.G. performed some of the experiments; Y.G. and X.W. provided reagents and revised the paper; H.S. and Q.L. completed the clinical study. J.M. initiated the project, led the project team, designed experiments, analyzed results, and wrote the article with input from all authors.
## Data availability
All other data information may be obtained from the corresponding author upon reasonable request. The RNA-sequences have been deposited to NCBI under accession number PRJNA877026.
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---
title: B cell-derived anti-beta 2 glycoprotein I antibody mediates hyperhomocysteinemia-aggravated
hypertensive glomerular lesions by triggering ferroptosis
authors:
- Xing Du
- Xiaolong Ma
- Ying Tan
- Fangyu Shao
- Chun Li
- Yang Zhao
- Yutong Miao
- Lulu Han
- Guohui Dang
- Yuwei Song
- Dongmin Yang
- Zhenling Deng
- Yue Wang
- Changtao Jiang
- Wei Kong
- Juan Feng
- Xian Wang
journal: Signal Transduction and Targeted Therapy
year: 2023
pmcid: PMC10008839
doi: 10.1038/s41392-023-01313-x
license: CC BY 4.0
---
# B cell-derived anti-beta 2 glycoprotein I antibody mediates hyperhomocysteinemia-aggravated hypertensive glomerular lesions by triggering ferroptosis
## Abstract
Hyperhomocysteinemia (HHcy) is a risk factor for chronic kidney diseases (CKDs) that affects about $85\%$ CKD patients. HHcy stimulates B cells to secrete pathological antibodies, although it is unknown whether this pathway mediates kidney injury. In HHcy-treated 2-kidney, 1-clip (2K1C) hypertensive murine model, HHcy-activated B cells secreted anti-beta 2 glycoprotein I (β2GPI) antibodies that deposited in glomerular endothelial cells (GECs), exacerbating glomerulosclerosis and reducing renal function. Mechanistically, HHcy 2K1C mice increased phosphatidylethanolamine (PE) (18:$\frac{0}{20}$:4, 18:$\frac{0}{22}$:6, 16:$\frac{0}{20}$:4) in kidney tissue, as determined by lipidomics. GECs oxidative lipidomics validated the increase of oxidized phospholipids upon Hcy-activated B cells culture medium (Hcy-B CM) treatment, including PE (18:$\frac{0}{20}$:4 + 3[O], PE (18:0a/22:4 + 1[O], PE (18:$\frac{0}{22}$:4 + 2[O] and PE (18:$\frac{0}{22}$:4 + 3[O]). PE synthases ethanolamine kinase 2 (etnk2) and ethanolamine-phosphate cytidylyltransferase 2 (pcyt2) were increased in the kidney GECs of HHcy 2K1C mice and facilitated polyunsaturated PE synthesis to act as lipid peroxidation substrates. In HHcy 2K1C mice and Hcy-B CM-treated GECs, the oxidative environment induced by iron accumulation and the insufficient clearance of lipid peroxides caused by transferrin receptor (TFR) elevation and down-regulation of SLC7A11/glutathione peroxidase 4 (GPX4) contributed to GECs ferroptosis of the kidneys. In vivo, pharmacological depletion of B cells or inhibition of ferroptosis mitigated the HHcy-aggravated hypertensive renal injury. Consequently, our findings uncovered a novel mechanism by which B cell-derived pathogenic anti-β2GPI IgG generated by HHcy exacerbated hypertensive kidney damage by inducing GECs ferroptosis. Targeting B cells or ferroptosis may be viable therapeutic strategies for ameliorating lipid peroxidative renal injury in HHcy patients with hypertensive nephropathy.
## Introduction
Homocysteine (Hcy) is an intermediate aminothiol derived from methionine catabolism. Generally, a high plasma Hcy level (>15 μM), known as hyperhomocysteinemia (HHcy), is widespread in Asians due to dietary patterns and genetic factors.1,2 Especially in patients with chronic kidney disease (CKD), the proportion of HHcy is as high as $85\%$, while it is only 5–$7\%$ in the general population.3–5 HHcy has been identified as a risk factor of CKD and contributes to cardiovascular complications.6–9 A cross-sectional survey reveals that hypertension is a major cause of CKD,10 but the molecular mechanisms by which HHcy mediates hypertension-associated kidney damage remains poorly understood.
Immune system disorders contribute to the progression of kidney disease.11,12 Lymphocytic infiltration was identified in the renal interstitial spaces adjacent to damaged glomeruli and tubules in patients with hypertensive renal damage.13 And clinical studies suggest that B cell activation and IgG production is involved in the pathogenesis of hypertension and end organ damage.14,15 Our laboratory has innovatively reported that Hcy promotes B cell proliferation and antibody (Ab) secretion by up-regulating glycolytic metabolism.16,17 Importantly, we noted that human and mouse plasma anti-beta 2 glycoprotein I (β2GPI) antibody levels are also significantly increased by HHcy, which exacerbates abdominal aortic aneurysm (AAA) progression.18 Kidney damage is a well-recognized complication of the antiphospholipid syndrome (APS), which is a systemic autoimmune disease defined by thrombotic or obstetrical events that occur in patients with persistent antiphospholipid antibodies (aPL).19,20 Anti-β2GPI antibodies, one of the aPLs, are associated with an increased risk of kidney diseases, and B cell depletion with CD20 monoclonal antibodies (mAbs) is effective in restoring the aPL-related decline in renal function.21,22 Therefore, we hypothesized that B cell-derived anti-β2GPI IgG induced by HHcy might be involved in the development of hypertensive renal injury.
Ferroptosis is a newly discovered metabolic cell death driven by iron-dependent lipid peroxidation, and characterized by accumulation of redox-active iron, loss of antioxidant capacity, and peroxidation of phospholipid-containing polyunsaturated fatty acyl tails (PL-PUFAs).23–25 Excess iron catalyzes reactive oxygen species (ROS) production via the Fenton reaction and attacks unsaturated membrane phospholipids.26–28 SLC7A11/xCT and glutathione peroxidase 4 (GPX4), as key components of elimination lipid peroxides, can protect cell from ferroptosis.29,30 Ferroptosis mediates the progression of multiple kidney diseases, including ischemia-reperfusion injury and diabetic nephropathy.31,32 *It is* consistent with previous reports that abnormal lipid metabolism is not only a clinical manifestation of kidney disease, but also an important pathogenic factor.33,34 We have reported that HHcy increases the anabolism and catabolism of lipids in lymphocytes, macrophages and adipocytes.35–37 However, whether HHcy-induced anti-β2GPI antibodies mediate renal injury by triggering ferroptosis needs to be investigated.
The present study showed that B-cell-derived anti-β2GPI IgG production and deposition at glomerular endothelial cells (GECs) in HHcy hypertensive renal injury mice triggered ferroptosis and glomerulosclerosis. The anti-CD20 mAb and Fer-1, a ferroptosis inhibitor, could effectively ameliorate HHcy-aggravated hypertensive renal injury. This finding provides a novel mechanistic explanation for disease progression in the CKD population with HHcy and potential targets for intervention in HHcy-associated renal injury.
## HHcy aggravates hypertensive kidney damage mediated by B cell-derived anti-β2GPI IgG
HHcy is frequently present in patients with hypertension and exacerbates kidney damage,6,7,38 but the underlying mechanisms are largely unknown. To explore them, we established a mouse model of renal vascular hypertension using 2K1C surgery with drinking water supplemented with or without 1.8 g/L Hcy for 28 days (HHcy 2K1C or 2K1C mice, respectively) (Supplementary Fig. 1a). The levels of plasma Hcy were elevated in Hcy administration groups (Supplementary Fig. 1b). Hypertension and renin-angiotensin-aldosterone system (RAAS) activation were successfully induced in both HHcy 2K1C and 2K1C mice (Supplementary Fig. 1c–f). HHcy did not affect systolic blood pressure (SBP) (Supplementary Fig. 1c), which was consistent with previous studies.39 Plasma creatinine (Cre), blood urea nitrogen (BUN), and urinary microalbumin, the markers of renal function, were increased in 2K1C mice and were further elevated by HHcy (Fig. 1a–c). Morphologically, the kidneys on the clamped side atrophied, while the contralateral kidney showed compensatory hypertrophy (Supplementary Fig. 1g). Compared with the sham group, the contralateral kidneys of 2K1C mice showed significant glomerulosclerosis, such as fixation of partial glomerular capillaries, deposition of collagen in the renal capsule cavity, thickening of glomerular basement membrane and expansion of mesangial matrix (Fig. 1d). HHcy also induced glomerulosclerosis and further aggravated the pathological changes in 2K1C mice (Fig. 1d). Cysteine β-synthase (CBS) and cysteine γ-lyase (CSE) catalyze Hcy catabolism through the transsulfuration pathway, and inadequate systhesis of these two enzymes is often present in renal diseases.40 We observed that 2K1C induced downregulation of CBS but not CSE expression (Supplementary Fig. 1h, i). These results indicate that we have successfully established a HHcy 2K1C mouse model in which HHcy aggravates hypertensive renal injury. Fig. 1HHcy aggravates hypertensive kidney damage mediated by B cell-derived anti-β2GPI IgG. C57BL/6J mice (8 weeks old) treated with sham or 2K1C surgery were given drinking water with or without Hcy (1.8 g/L) for 4 weeks. To assess the role of B cells in renal injury, Rituximab was administered to HHcy 2K1C mice at the start of modeling (i.p. 75 μg/20 g body weights every other day for 4 weeks). Plasma Cre (a), BUN (b), and urinary microalbumin (c) were assayed to evaluate renal function using ELISA. d Representative histochemical staining of kidney paraffin sections with hematoxylin and eosin (HE, top) and periodic acid-Schiff (PAS, bottom) (scale bar, 50 μm). Quantification of the glomerular matrix index was performed for each group (0 = normal glomeruli, 1 = thickening of the GBM, 1.5 = glomerular thickening plus segmental hypercellularity, 2 = mild segmental hyalinosis (<$25\%$), 2.5 = severe segmental hyalinosis (>$50\%$), 3 = glomerular hyalinosis (‘blobs’ of hyaline material deposition), and 4 = diffuse glomerular sclerosis with total tuft obliteration and collapse, 50 glomeruli/mice, $$n = 6$$/group). e, f Plasma samples were collected and evaluated for total IgG and anti-β2GPI IgG using ELISA. g, h Kidney tissue lysates were prepared and evaluated for total IgG and anti-β2GPI IgG using ELISA. i Representative immunofluorescent staining of β2GPI (red), IgG (green), and nuclei (blue) in frozen kidney sections. White dashed boxes indicate the colocalization of IgG and β2GPI (scale bar, 25 μm). j Representative immunofluorescent staining of β2GPI (red), CD31 (j) (green) (scale bar, 25 μm), nephrin (k) (green) (scale bar, 10 μm), PDGFRβ (l) (green) (scale bar, 25 μm) and DAPI (blue) in frozen kidney sections. HHcy hyperhomocysteinemia, 2K1C 2-kidney, 1-clip, Cre creatinine, BUN blood urea nitrogen, UCAR urinary creatinine albumin ratio, GBM glomerular basement membrane, PDGFRβ platelet-derived growth factor receptor beta. All data are expressed as the means ± SEM. $$n = 5$$–6, *$P \leq 0.05$, **$P \leq 0.01$ We previously reported the pathogenic roles of Hcy-induced B-cell activation and antibody production, especially pathological anti-β2GPI IgG, in atherosclerosis and abdominal aortic aneurysm in humans and mice.16,18 In the present study, HHcy treatment significantly increased the levels of total IgG and anti-β2GPI IgG in plasma and kidney homogenates of sham and 2K1C mice (Fig. 1e–h). Immunofluorescence staining of kidney tissue sections showed significant colocalization of IgG and β2GPI in HHcy 2K1C mice, but not in 2K1C mice (Fig. 1i). β2GPI co-localized with CD31-positive glomerular endothelial cells (GECs) (Fig. 1j) but less with PDGFRβ-labeled glomerular mesangial cells (Supplementary Fig. 1j) and nephrin-labeled podocytes (Supplementary Fig. 1k), indicating that anti-β2GPI IgG deposited in GECs and possibly participated in HHcy-induced glomerular damage. To explore the roles of B cells and their anti-β2GPI IgG in this process, HHcy 2K1C mice were injected with rituximab (RTX, a CD20 mAbs, 75 μg/20 g body weights every other day) for 4 weeks to deplete B cells (Supplementary Fig. 2a). Flow cytometry showed that RTX significantly reduced the percentages of CD19+ B cells in the spleen and kidneys of HHcy 2K1C mice (Supplementary Fig. 2b, c), and that total IgG and anti-β2GPI IgG were also significantly reduced in plasma and kidneys (Fig. 1e–h). Plasma Cre, BUN, and urinary microalbumin levels were significantly decreased after RTX treatment (Fig. 1a–c), while glomerular basement membrane thickening and mesangial expansion were also alleviated (Fig. 1d), indicating effective recovery of renal function. RTX significantly downregulated the levels of renal inflammatory cytokines IL-1β, IL-6, and TNF-α, but had no significant effect on systemic inflammatory response (Supplementary Fig. 2d–f). Taken together, B cell-derived antibodies, especially pathogenic anti-β2GPI IgG, mediate HHcy-exacerbated glomerular damage in hypertensive mice.
## HHcy induces a remodeling of phospholipid composition and fatty acid accumulation in kidneys of hypertensive mice by B cell-derived antibodies
In view of the close relationship between lipid metabolism and HHcy-aggravated hypertensive renal injury,33,34,36 we used lipidomics to evaluate and identify the lipid metabolic changes. The heatmap showed a significant accumulation of 16:0, 18:0, 16:$\frac{0}{18}$:1, 16:$\frac{0}{18}$:2, 16:$\frac{0}{20}$:4, 16:$\frac{0}{22}$:6, 18:$\frac{0}{18}$:2, 18:$\frac{0}{20}$:4, 18:$\frac{0}{22}$:6 phosphatidylethanolamine (PE) and a mild decrease in phosphatidylcholine (PC) in the kidneys of HHcy 2K1C mice compared with 2K1C mice (Fig. 2a). The increase in PE-PUFAs was the most prominent, and 18:$\frac{0}{20}$:4 PE had higher variable importance in projection (VIP) scores (Fig. 2b). We further analyzed the concentrations of different phospholipid species classified according to the number of double bonds in the fatty acid acyl tail, which were showed as saturated, MUFA (monounsaturated fatty acid), PUFA, and total respectively (Fig. 2c, d). The PE-PUFA level was markedly elevated in HHcy 2K1C mice vs. 2K1C mice (Fig. 2c). However, there was no significant difference in the levels of PE-MUFAs or LPE (lysophosphatidylethanolamine)-MUFAs among these groups (Fig. 2c, d). HHcy significantly decreased the PC/PE ratio in 2K1C mice (Fig. 2e). Free fatty acids, which are substrates for phospholipid synthesis, accumulated significantly in the kidneys of HHcy mice or 2K1C mice, and 18:2 FFAs, 20:5 FFAs, and 22:0 FFAs were further elevated in HHcy 2K1C mice compared to 2K1C mice (Fig. 2f, g). PE is mainly synthesized via the Kennedy pathway (de novo pathway), and a small fraction of PE is converted from other components, such as phosphatidylserine (PS).41 The mRNA levels of PE synthases ethanolamine kinase 2 (etnk2), ethanolamine-phosphate cytidylyltransferase 2 (pcyt2), and ethanolamine phosphotransferase 1 (ept1) in the kidneys of 2K1C mice were significantly upregulated by HHcy, but the levels of phosphatidylserine decarboxylase (pisd) were not significantly altered among these groups (Fig. 2h). The protein expressions of etnk2 and pcyt2 were consistent with mRNA changes (Fig. 2j). Given that anti-β2GPI IgG deposited in GECs, we further isolated CD31-positive GECs using laser capture microdissection in frozen sections of kidney tissue to investigate their lipid synthesis gene expression induced by B-cell-derived anti-β2GPI IgG. The results showed that HHcy induced the upregulation of etnk2, pcyt2 and lpcat3 mRNA levels in CD31-positive glomerular endothelial cells from HHcy 2K1C mice (Fig. 2k). These results may explain the accumulation of PE-PUFA in kidney and GECs in vivo. Fig. 2HHcy induces a remodeling of phospholipid composition and fatty acid accumulation in kidneys of hypertensive mice by B cell-derived antibodies. a–g HPLC-MS/MS analysis of lipid metabolites and free fatty acids (FFAs) in kidney tissues from sham or 2K1C mice with or without HHcy (1.8 g/L) in drinking water for 4 weeks. Rituximab was administered to HHcy 2K1C mice at the start of modeling (i.p. 75 μg/20 g body weights every other day for 4 weeks). $$n = 4$.$ a Heatmap illustrating the phospholipid metabolic profiles in kidney tissues. b VIP scatter plot identified by PCA showing the top 15 lipid metabolites in the different groups. c, d Phospholipids were classified according to the number of double bonds in the fatty acid acyl tails and expressed as saturated, MUFA, PUFA, and total, and different species levels of PE (c) and LPE (d) in kidney tissues were analyzed using HPLC-MS/MS. e The ratios of total PC/PE were calculated in each group. f Heatmap illustrating the free fatty acids in kidney tissues. g Different levels of free fatty acids in kidney tissues are shown in the histogram. h, i Quantitative PCR analysis of PE and PC synthesis enzymes in the kidney (PE synthesis: etnk2, pcyt2, ept1; PC synthesis: pemt, lpcat3). $$n = 6$.$ j Western blot analysis of ETNK2 and PCYT2 protein expression and quantification. β-actin was used as an internal control. $$n = 3$.$ k Quantitative PCR analysis of PE and PC synthesis enzymes in the renal CD31-positive GECs from frozen section by laser capture microdissection. $$n = 3$.$ VIP variable importance in projection, PCA principal component analysis, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid, etnk2 ethanolamine kinase 2, pcyt2 phosphate cytidylyltransferase 2, ept1 ethanolaminephosphotransferase 1, pemt phosphatidylethanolamine N-methyltransferase, lpcat3 lysophosphatidylcholine acyltransferase 3. All data are expressed as the means ± SEM. * $P \leq 0.05$, **$P \leq 0.01$ To further investigate whether HHcy-activated B cell-derived antibodies are involved in these changes, we evaluated the effect of RTX on HHcy-induced phospholipid remodeling. Administration of RTX in HHcy 2K1C mice resulted in a robust decrease in PE-PUFA and FFAs accumulations (Fig. 2a–g). Consistently, RTX-treated HHcy 2K1C mice showed downregulation of PE de novo synthase etnk2 and pcyt2 expression in kidney tissue and CD31-positive GECs (Fig. 2i–k). Therefore, HHcy provides an oxidation-prone lipid environment in the kidney of hypertensive mice by inducing B-cell activation and antibodies secretion, accompanied by increased accumulation of substrate FFAs and PE-PUFA in kidney and GECs in vivo.
## B cell-derived antibodies mediate HHcy-aggravated kidney lipid peroxidation in hypertensive mice
As the main component of cellular membranes, lipids have an indispensible role in maintaining the structural integrity of cells. Excessive oxidation of lipids alters the physical properties of cellular membranes and can cause covalent modification of proteins and nucleic acids.42 Lipid peroxidation preferentially occurs in PUFAs—long-chain fatty acids with more than one double bond.27,42 Indeed, immunohistochemical staining of 4-HNE in the kidneys, which is a marker of lipid peroxidation, showed a marked increase in HHcy and HHcy 2K1C mice (Fig. 3a). HHcy and 2K1C treatment significantly increased renal LPO and MDA, the products of lipid peroxidation, and their levels were higher in HHcy 2K1C mice compared to 2K1C mice (Fig. 3b, c). The ACSL4 (long-chain fatty acid CoA ligase 4) mRNA level and LOX15 expression, catalyzing PUFAs activation and lipid peroxidation, were elevated in kidney tissues of HHcy 2K1C mice compared to 2K1C mice (Fig. 3d, e). The mRNA and protein levels of SLC7A11, the catalytic subunit of cystine/glutamate antiporter System Xc−, were downregulated in kidney tissues of 2K1C mice compared to the sham mice and further reduced by HHcy treatment (Fig. 3d, e). HHcy significantly decreased the gene expression of GPX4 in HHcy 2K1C mice compared to 2K1C mice, with a down-regulation trend in protein levels (Fig. 3d, e). The ratios of the main cellular redox couples, GSSG/GSH and NADP+/NADPH,43 were significantly increased in 2K1C mice, which were further aggravated by HHcy (Fig. 3f, g). In addition, HHcy also induced upregulation of acsl4 and lox15 mRNA levels and downregulation of slc7a11 and gpx4 mRNA levels in CD31-positive GECs in the HHcy and 2K1C HHcy mice (Fig. 3h). These results suggest that HHcy induces the perturbation of redox equilibrium and aggravates the renal lipid peroxidation of hypertensive mice. Fig. 3B cell-derived antibodies mediate HHcy-aggravated kidney lipid peroxidation in hypertensive mice. a Immunohistochemical staining of 4-HNE (brown) from kidney sections in sham or 2K1C mice with or without Hcy (1.8 g/L) in drinking water for 4 weeks, indicative of lipid peroxidation. Rituximab was administered to HHcy 2K1C mice at the start of modeling (i.p. 75 μg/20 g body weights every other day for 4 weeks). The immunohistochemical staining was calculated as the percentage of brown signals over the total area. $$n = 3$.$ b, c Effects of 2K1C and HHcy on lipid peroxidation in the kidney were measured using ELISA for LPO (b) and MDA (c). $$n = 6$.$ d Quantitative PCR analysis of redox enzyme expression in kidney tissues, including acsl4, lox15, slc7a11 and gpx4. $$n = 6$.$ e Western blot analysis of LOX15, GPX4 and SLC7A11 protein expression and quantification. β-actin was used as an internal control. $$n = 3$.$ f, g The redox balance in the kidney was assayed for the ratio of GSSG/GSH and NADP+/NADPH using ELISA. $$n = 6$.$ h Quantitative PCR analysis of redox enzyme expression in the renal CD31-positive GECs from frozen section by laser capture microdissection, including acsl4, lox15, slc7a11 and gpx4. $$n = 3$.$ LPO lipid peroxide, MDA malondialdehyde, 4-HNE 4-hydroxynonenal, lox15 lipoxygenase 15, acsl4 acyl-CoA synthetase long chain family member 4, slc7a11 cystine/glutamate transporter, gpx4 glutathione peroxidase 4, GSH glutathione, GSSG glutathione disulfide. All data are expressed as the means ± SEM. * $P \leq 0.05$, **$P \leq 0.01$ The effect of HHcy-activated B cell-derived antibodies on lipid peroxidation was validated by depletion of B cells and showed that renal lipid peroxidation was diminished in HHcy 2K1C mice treated with RTX (Fig. 3a–c). In addition, RTX ameliorated the redox imbalance in kidneys and GECs of HHcy 2K1C mice (Fig. 3d–h). Together, these results indicate that targeting B cells is instrumental for the inhibition of HHcy-exacerbated lipid peroxidation in the kidneys and GECs of hypertensive mice.
## Targeting B cell-derived antibodies improves HHcy-aggravated iron accumulation in hypertensive mice
Lipid peroxides are not only key mediators of many pathological states, including inflammation and renal degeneration, but have recently been identified as a key downstream feature of ferroptosis, an emerging form of regulated nonapoptotic cell death.44 Iron accumulation is another important manifestation of ferroptosis, which interferes with redox homeostasis, catalyzes ROS propagation, leading to oxidative stress and tissue damage.45 To explore whether ferroptosis participates in HHcy-aggravated hypertensive renal injury, we further detected the levels of iron. Indeed, ELISA showed that HHcy promoted iron accumulation in the kidneys of HHcy 2K1C mice compared to 2K1C mice, and RTX effectively alleviated this alteration (Fig. 4a). Abnormal iron accumulation due to loss of balance between influx and efflux. Transferrin (TF) delivers large amount of iron from plasma to the bone marrow for heme biosynthesis and small amount to other tissues, which is responsible for iron influx into cells.45 The mRNA and protein levels of TF were significantly increased in the kidneys of 2K1C mice compared to sham mice, and there was a trend of further increase in HHcy 2K1C mice (Fig. 4b, e). HHcy treatment upregulated transferrin receptor (TFR) mRNA expression in the kidneys of sham and 2K1C mice, but did not affect TFR protein expression (Fig. 4c, e). However, the mRNA and protein levels of SLC40A1, which mediates iron efflux from cells,45 were decreased in HHcy 2K1C mice compared to 2K1C mice (Fig. 4d, e). After RTX treatment, the alteration of TF, TFR, and SLC40A1 in mRNA and protein levels in the kidney tissues of HHcy 2K1C mice were partial ameliorated, except for TFR protein level (Fig. 4b–e). Similarly, HHcy treatment significantly increased TFR gene expression and decreased SLC40A1 gene levels in HHcy and HHcy 2K1C renal CD31-positive GECs, and RTX treatment effectively attenuated these changes (Fig. 4f). Taken together, these results suggest that HHcy promotes renal and GEC iron deposition and might trigger ferroptosis during renal injury in 2K1C mice via B-cell-derived antibodies. Fig. 4Targeting B cell-derived antibodies to improve HHcy-aggravated iron accumulation in hypertensive mice. Iron content and metabolism were analyzed in mice treated with sham or 2K1C surgery and given drinking water with or without Hcy (1.8 g/L) for 4 weeks. Rituximab was administered to HHcy 2K1C mice (i.p. 75 μg/20 g body weights every other day for 4 weeks). a Iron concentrations in kidney tissues were measured using ELISA. $$n = 6$.$ b–d Quantitative PCR analysis of the genes associated with iron metabolism in kidney tissues, including tf (b), tfr (c) and slc40a1 (d). $$n = 6$.$ e Western blot analysis of TF, TFR, and SLC40A1 protein expression and quantification in kidney tissues. β-actin was used as an internal control. $$n = 3$.$ f Quantitative PCR analysis of the genes associated with iron metabolism in the renal CD31-positive GECs from frozen section by laser capture microdissection, including tfr and slc40a1. $$n = 3$.$ TF transferrin, TFR transferrin protein receptor, slc40a1 solute carrier family 40 member 1, HE staining hematoxylin and eosin staining, PAS staining periodic acid-Schiff staining. All data are expressed as the means ± SEM. * $P \leq 0.05$, **$P \leq 0.01$
## Ferroptosis mediates GECs dysfunction induced by anti-β2GPI antibody derived from Hcy-activated B cells in vitro
Considering the deposition of anti-β2GPI IgG on CD31-positive GECs, we examined the effects of Hcy-induced B cell-derived-anti-β2GPI IgG on GECs in vitro. We have demonstrated that the culture medium of B cells treated with 100 μM Hcy for 72 h, which contains high level anti-β2GPI antibody, and its purified IgG both induced a shift in macrophage phenotypes towards M1 polarity and evoked vascular inflammation.18 GECs were treated with con-B CM or Hcy-B CM (culture medium of B cells treated with or without 100 μM Hcy for 72 h) and with or without Ang II for 24 h to simulate the in vivo environment of renal hypertension and HHcy (Supplementary Fig. 3a). We used 1 μM Ang II as the stimulation concentration because GEC viability began to decrease in the CCK-8 assay at this concentration (Supplementary Fig. 3b). The concentration of Hcy in Hcy-B CM was about 0.4167 ± 0.2638 μM (Supplementary Fig. 3c), which had no effect on the viability of GECs (Supplementary Fig. 3d), so the potential effect of residual Hcy was excluded. The results from RNA-sequencing (RNA-seq) showed that the top 20 GO enrichment results between the differentially expressed genes in con-B CM + Ang II and Hcy-B CM + Ang II treated GECs included cell cycle, apoptotic processes, oxidative stress and hypoxia, indicating that multiple cellular damage pathways were activated in Hcy-B CM + Ang II treated GECs (Fig. 5a). Indeed, the LDH level in the Hcy-B CM + Ang II group was significantly higher than the Ang II alone group (Fig. 5b), and ferroptosis inhibitor ferrostatin-1 (Fer-1, 5 μM) rescued this change (Fig. 5b). In addition, Hcy-B CM increased the percentage of Annexin V+ GECs induced by Ang II, whereas the ferroptosis inhibitors Fer-1 and liproxstatin-1 (Lip-1, 200 nM) inhibited this effect (Supplementary Fig. 3e). We assessed the redox state of GECs under different stimulations. The mRNA levels of the lipid peroxidation-related enzymes ACSL4, LOX12, and LOX15 were increased, and the mRNA expression levels of the GPX4 and SLC7A11, antioxidant enzyme and transporter, were decreased in the Hcy-B CM + Ang II group compared to the Ang II alone group (Fig. 5c). The changes in protein expression of GPX4 and LOX15 paralleled the changes in mRNA expression, suggesting the increased oxidative and diminished antioxidant capacities (Fig. 5d). Besides, the levels of LPO and MDA in the Hcy-B CM + Ang II group were markedly increased compared to the Ang II alone group (Fig. 5e, f). Hcy-B CM treatment aggravated the accumulation of lipid peroxides in the Hcy-B CM + Ang II group vs. the Ang II alone group, as revealed by BODIPY-C11 staining in GECs (Fig. 5g and Supplementary Fig. 3f). The oxidative phospholipidomic assays provided further direct evidences of increased oxygenated phospholipids, including oxPE and oxPC in the Hcy-B CM + Ang II group of GECs (Fig. 5h).Fig. 5Ferroptosis mediates GECs dysfunction induced by anti-β2GPI antibody derived from Hcy-activated B cells in vitro. We evaluated the bioactive effects of Hcy-activated B cell-derived antibodies, particularly anti-β2GPI, on GECs. Ferrostatin-1 (Fer-1, 5 μM) was administered to Hcy-B CM + Ang II GECs to inhibit ferroptosis. a Cellular RNA was sequenced by RNA‐Seq, and the top 20 GO enrichment results between the differentially expressed genes in con-B CM + Ang II and Hcy-B CM + Ang II treated GECs were shown. b LDH release into GEC culture medium was measured using an ELISA kit. $$n = 6$.$ c Quantitative PCR analysis of redox enzyme expression in GECs, including ACSL4, LOX12, LOX15, GPX4, and SLC7A11. $$n = 6$.$ d Western blot analysis of LOX15 and GPX4 protein expression and quantification. β-actin was used as an internal control. $$n = 3$.$ e, f The intracellular LPO (e) and MDA (f) levels in GECs were measured using ELISA. $$n = 6$.$ g For flow cytometry analysis, GECs were treated with or without Ang II (1 μM) and with or without Hcy (100 μM)-activated B cell culture supernatant for 24 h, and 5 μM BODIPY-C11 dye was added during the last hour and resuspended in culture medium. The cells were washed twice with ice-cold PBS, stained with 7-AAD for 5 min, trypsinized and filtered into single-cell suspensions. Flow cytometry analysis was performed using a PE-Texas Red filter for reduced BODIPY-C11 and an FITC filter for oxidized BODIPY-C11. h HPLC-MS/MS analysis of phospholipids and oxidized phospholipids in GECs from each group. Heatmap illustrating the phospholipid metabolic profiles in GECs. $$n = 4$.$ i, j Quantitative PCR analysis of TFR (i) and SLC40A1 (j) associated with iron metabolism in GECs. $$n = 6$.$ k Western blot analysis of TFR and SLC40A1 protein expression and quantification in kidney tissues. β-actin was used as an internal control. $$n = 3$.$ l, m Intracellular iron concentrations in GECs were measured using ELISA (l, $$n = 4$$) and Phen Green SK (PGSK) staining was assessed using flow cytometry (m, $$n = 3$$). Higher Fe2+ concentrations are indicated by weaker PGSK fluorescence intensity. The reductions in PGSK fluorescence intensity were calculated. NHIgG and aPL treatment of GECs. All data are expressed as the means ± SEM. * $P \leq 0.05$, **$P \leq 0.01$ Iron metabolism was also assessed in GECs. The mRNA and protein expression of transferrin receptor TFR were increased, and the expression of iron efflux transporter SLC40A1 was decreased in the Hcy-B CM + Ang II group compared to the Ang II alone group (Fig. 5i–k). ELISA assay showed that Hcy-B CM increased the intracellular Fe2+ concentration in the Hcy-B CM + Ang II group of GECs (Fig. 5l). PGSK probe staining further validated these changes in the Fe2+ concentration using flow cytometry analysis and immunofluorescence staining, which showed a higher Fe2+ concentrations indicated by weaker fluorescence intensity (Fig. 5m and Supplementary Fig. 3g). Fer-1 treatment (5 μM, 24 h) reversed the expression of the antioxidants GPX4 and SLC7A11 and the oxidants ACSL4, LOX12, and LOX15 (Fig. 5c, d). ELISA and BODIPY-C11 staining showed that Fer-1 treatment reduced lipid peroxidation in the Hcy-B CM + Ang II group of GECs (Fig. 5e–g and Supplementary Fig. 3f), which was directly demonstrated by oxidative phospholipidomics (Fig. 5h). Fer-1 treatment also ameliorated the upregulated expression of TFR and downregulated expression of SLC40A1 (Fig. 5i–k), which contributed to the iron reduction in Fer-1-treated GECs (Fig. 5l, m and Supplementary Fig. 3g). Taken together, these results suggest that ferroptosis mediates the dysfunction of GECs caused by Hcy-activated B cell-derived antibodies, primarily pathogenic anti-β2GPI IgG, with iron-dependent oxidative phospholipid induction and lipid peroxidation.
## aPL induces ferroptosis in GECs in vitro
To further illustrate the function of B-cell-derived anti-β2GPI IgG, we used purified antiphospholipid antibodies (aPL, 100 μg/ml) and control IgG (NHIgG, 100 μg/ml) to treat GECs for 24 h. aPL significantly induces LDH release from GECs, and Fer-1 (5 μM) effectively ameliorates GECs impairment (Fig. 6a). Further analysis of redox-related enzyme expression showed that aPL significantly upregulated gene and protein expression of ACSL4 and LOX15, and decreased the expression of SLC7A11 and GPX4 (Fig. 6b, c). In this oxidative environment, GECs showed lipid peroxide accumulation induced by aPL (Fig. 6d–f). Fer-1 treatment effectively improved the abnormal expression of redox-related enzymes and reduced lipid peroxide levels in GECs (Fig. 6b–f). aPL-induced imbalance in GSSG/GSH and NADP+/NADPH ratios was also effectively alleviated by Fer-1 treatment (Fig. 6g, h). Furthermore, aPL-induced upregulation of TFR expression and downregulation of SLC40A1 in GECs, and intracellular Fe2+ levels were significantly increased in the presence of aPL (Fig. 6b–c, i). However, Fer-1 treatment significantly ameliorated these changes (Fig. 6b–c, i). Thus, these results provide evidence that antiphospholipid antibodies cause ferroptosis in GECs. Fig. 6aPL induce ferroptosis in GECs in vitro. Evaluation of purified antiphospholipid antibodies (aPL, 100 μg/ml) and control IgG (NHIgG, 100 μg/ml) on GECs. a LDH release was measured using an ELISA kit. $$n = 3$.$ b, c Quantitative PCR and western blot analysis of redox and iron metabolism enzymes expression in GECs, including ACSL4, LOX15, GPX4, SLC7A11, TFR, and SLC40A1. d, e The intracellular LPO (d) and MDA (e) levels in GECs were measured using ELISA. $$n = 3$.$ f Detection of lipid peroxides by BODOPY-C11. $$n = 3$.$ g, h Intracellular GSSG/GSH (g) and NADP+/NADPH (h) ratios in GECs were measured using ELISA. $$n = 3$.$ i Intracellular iron concentrations in GECs were measured using PGSK staining. $$n = 3$.$ LDH lactate dehydrogenase. All data are expressed as the means ± SEM. * $P \leq 0.05$, **$P \leq 0.01$
## Ferroptosis mediates HHcy-exacerbated kidney injury in vivo
Lipid peroxidation and iron accumulation are characteristic manifestations of ferroptosis.44 We further determined whether ferroptosis is involved in the pathogenesis of hypertensive renal injury exacerbated by HHcy by pharmacological methods. HHcy 2K1C mice were continuously injected with the ferroptosis inhibitor ferrostatin-1 (Fer-1, 1 mg/kg/day, i.p.) for 4 weeks (Supplementary Fig. 4). ELISA showed a significant reduction in plasma Cre, BUN and urinary microalbumin after Fer-1 treatment in HHcy 2K1C mice (Fig. 7a–c). H&E and PAS staining showed that Fer-1 treatment significantly improved the exacerbated glomerulosclerosis in HHcy 2K1C mice (Fig. 7d). Fer-1 also inhibited the renal increased LPO and MDA levels in HHcy 2K1C mice (Fig. 7e, f). These results indicate that ferroptosis mediates the pathogenesis of HHcy-exacerbated hypertensive renal injury. Fig. 7Ferroptosis mediates HHcy-exacerbated kidney injury. Fer-1, a specific ferroptosis inhibitor, was administered to HHcy 2K1C mice at the start of modeling (1 mg/kg/day, i.p. 4 weeks) to evaluate the effect of ferroptosis on kidney damage. a–c Plasma Cre (a), BUN (b), and urinary microalbumin (c) were assayed to evaluate renal function using ELISA. $$n = 6$.$ d Representative HE staining (top) and PAS staining (bottom) (scale bar, 50 μm) in kidney paraffin sections. Quantification of the glomerular matrix index was performed for each group (50 glomeruli/mice, $$n = 6$$/group). e, f Kidney tissue lysates were prepared and evaluated for LPO (e) and MDA (f) using ELISA. $$n = 6$.$ *All data* are expressed as the means ± SEM. * $P \leq 0.05$, **$P \leq 0.01$
## Discussion
CKD has become a leading cause of morbidity and mortality worldwide in the last few decades.11,46 A previous epidemiological study reveals that HHcy predicts reduced renal function and the incidence of CKD in hypertensive patients.47 Immune-associated glomerulonephritis is a major cause of CKD, and its pathogenesis is based on the interaction between bone-marrow-derived immune cells and cells intrinsic to the kidney.48 Notably, B lymphocytes and their antibodies are key players in immune activation.48,49 In the present study, we found, from an immunological perspective, that HHcy activated B cells and promoted antibody production and secretion, especially the pathogenic anti-phospholipid binding protein β2GPI IgG, which deposited in glomerular endothelial cells to induce ferroptosis (Fig. 8). Improvements in HHcy-aggravated renal dysfunction via B cell depletion using RTX further confirmed that B cells and anti-β2GPI IgG-related immune mechanisms were the main pathways in HHcy-exacerbated hypertensive kidney injury. Fig. 8Schematic diagram. HHcy aggravates hypertensive kidney damage by activating B lymphocytes to secrete pathological anti-β2GPI IgG, which promotes lipid peroxidation and ferroptosis in hypertensive glomerular endothelial cells Anti-β2GPI antibodies are a class of antiphospholipid antibodies (aPLs), which are the biomarker in the serum of patients with cardiovascular disease, mediating the development of autoimmune disease antiphospholipid syndrome (APS).50 As the major membrane-bound antigenic protein of aPL, β2GPI forms a specific immune complex with serum anti-β2GPI antibodies, which is closely related to systemic lupus erythematosus (SLE) and atherosclerosis through TLR signaling.51,52 TLRs are a major class of germ-line encoded receptors that activate the B-cell-mediated pathological process in autoimmune diseases. In our previous work, we have shown that anti-β2GPI antibodies produced by Hcy-activated B cells polarizes macrophages to M1 through TLR4 signaling pathway and contributes to AAA, and β2GPI-expressing endothelial cells may also be involved.18 *This is* consistent with the reports that anti-β2GPI antibodies mediate proinflammatory phenotype of endothelial cells and monocytes.51,53 The cationic phospholipid-binding site (located in the fifth domain of the molecule) and anionic structures, such as heparin sulfate, on the cell membrane, interact electrostatically to anchor β2GPI to endothelial cell membranes I or as a ligand for annexin A2.54 Anti-β2GPI antibody binding results in clustering of β2GPI with its potential receptors, such as Toll-like receptor (TLR) 4, annexin A2, and apolipoprotein E receptor 2, and it also triggers cell signal transduction, activating either p38 mitogen-activated protein kinase (MAPK) or nuclear factor κB (NFκB) or both.54,55 In our study, we demonstrated that ferroptosis is an important novel mechanism by which anti-β2GPI/β2GPI immune complex disrupts GECs function. Although circulating aPLs and endothelial dysfunction are necessary “first hits” for cardiovascular events in APS, an inflammatory “second hit” to upregulate the expression of β2GP1 receptors on the endothelial cell surface is needed.54 The “second hit” may be consistent with the elevated blood pressure induced by 2K1C, and it may account for the higher susceptibility of 2K1C mice to suffer an HHcy-induced decline in renal function. This hypothesis is supported by a recent report that the strength of the relationship between plasma Hcy levels and cardiovascular events is greater in patients with hypertension than patients without hypertension.56 Immune system, as the main defense mechanism of the body, can be induced rapid adaptive response of metabolic remodeling in immune cells by immunogenic stimulation, and this process is called immune metabolism.57 We have demonstrated that glucose metabolism and accumulation of phospholipids and fatty acids mediates HHcy-induced B cell activation and Ab secretion, including pathogenic anti-β2GPI antibodies.17,18 Glucose-dependent de novo lipogenesis supports proliferation and expansion of the cellular endoplasmic reticulum and Golgi endomembrane network, which favors B-cell Ab production and secretion.58 However, the activation of liver X receptor to increase cholesterol and fatty acid excretion and activation of fatty acid oxidation by the peroxisome proliferator activated receptor alpha (PPARα) agonist fenofibrate both effectively inhibit Hcy-induced B cell IgG and anti-β2GPI IgG secretion,18,59 indicating the importance of B cell lipid accumulation and metabolism for anti-β2GPI IgG production and secretion. More importantly, when endothelial cells or monocytes/macrophages are subjected to stress, membrane phospholipids are perturbed to expose β2GPI binding sites and recruit circulating anti-β2GPI antibodies to form immune complexes that mediate cell signal transduction via the potential receptor TLR4.60 In the present study, we found that the anti-β2GPI IgG binded to the glomerular endothelial cell antigen β2GPI, and depletion of B cells by RTX confirmed that this immune complex was involved in hypertensive renal injury exacerbated by HHcy. Furthermore, we demonstrated that HHcy-activated B cell-derived antibodies mediate macrophage polarization toward inflammatory M1 via F(ab′)2, suggesting an antigen-dependent mode of action.18 RNA-seq results showed that blocking TLR4 (TAK-242 pretreatment, 10 nM, 30 min) effectively inhibited fatty acid transport and synthesis, phospholipid synthesis, and iron transport into cells, providing strong evidence that TLR4 is a potential receptor for anti-β2GPI antigen-antibody complexes (data not shown).
As a major risk factor for chronic kidney disease, disorders of lipid metabolism induce structural damage and dysfunction of biological membranes and reactive phospholipid products that act as signaling molecules induce oxidative stress and inflammation.61–64 The present study reported lipid remodeling in the renal tissue of HHcy 2K1C mice, particularly polyunsaturated PE accumulation and a decreased PC/PE ratio compared to 2K1C mice. The key enzymes that catalyze PE synthesis, etnk$\frac{1}{2}$, pcyt2 and ept1, were upregulated in HHcy 2K1C mice. This indicates an increase in de novo PE synthesis, especially polyunsaturated PE. In HHcy 2K1C mice, ACSL4, which favors the substrate arachidonate, was also increased. ACSL4 catalyzes the conversion of long-chain fatty acids to their active form, acyl-CoA, for the synthesis of cellular lipids.65 These results are consistent with our results of significant increases in renal $\frac{16}{0}$–$\frac{20}{4}$ PE and $\frac{18}{0}$–$\frac{20}{4}$ PE (Fig. 2a, b). Knockdown of ACSL4 reduces the cellular PUFA phospholipid content.66 Abundant FFAs provide sufficient substrates for PE-PUFA synthesis (Fig. 2f, g). The increase in PE-PUFA enhances the sensitivity of PE to oxidative stress attack.27 LPC levels were also slightly increased in the kidneys of HHcy mice compared with sham mice. *The* generation of LPC is dependent on phospholipase A2 (PLA2) to hydrolyze the sn-2 position of PC. We have demonstrated that LPC had a pro-inflammatory effect in adipocytes.36 This effect may be one of the mechanisms for renal inflammation in renal tissue. Renal lipid metabolism disorders and FFAs accumulation were effectively ameliorated in HHcy 2K1C mice when RTX treatment was administered. However, the regulatory mechanism of the decrease in the PC/PE ratio caused by HHcy-activated B cell-derived antibodies requires further investigation.
The close association of PE-PUFA with ferroptosis indicated by KEGG signaling pathway analysis attracted our attention. Ferroptosis is a regulated form of cell death that occurs when PL-PUFAs are oxidized in an iron-dependent manner,23,25 and it is gaining attention in many kidney diseases, such as ischemic kidney injury and renal cell carcinoma.67–69 As substrates for lipid peroxidation, PL-PUFAs are susceptible to attack by oxidants, such as free radicals. The presence of aPL provides an oxidative stress environment.70,71 Initial lipid hydroperoxides (LOOHs), following reactive aldehydes, MDA, and 4-HNE are all byproducts of lipid peroxidation. We found that Hcy-treated B cell culture supernatant containing abundant anti-β2GPI antibodies contributed to lipid peroxidation in GECs in vitro. The oxidative phospholipidomics results further confirmed with a significant increase in oxidized PC and oxidized PE, such as IsoF-PC, F2-IsoP-PC, PE(18:0a/C20:4 + 3[O]), PE(18:0/C22:4 + 1[O]) PE(18:0/C22:4 + 2[O]), and PE(18:0/C22:4 + 3[O]) (Fig. 5h). After being incorporated into membrane environments, PL-PUFAs undergo a peroxidation reaction with labile iron and iron-dependent enzymes to produce PL-PUFA-OOH, which is sufficient to damage the membrane and trigger the cell death program.25,26 Initiating membrane damage during ferroptosis only requires $2\%$ oxidative damage to PUFAs.72 *Ferroptosis is* fueled by iron-dependent enzymes such as lipoxygenases and cytochrome P450 oxidoreductase (POR).72,73 We showed that 12-lipoxygenase (LOX12) and LOX15 expression were upregulated in vitro and in vivo. LOX15 has catalytic competence in the selective oxidation of membrane ETE-PE to ferroptosis signals, HpETE-PE.74 Fer-1, a well-known inhibitor of ferroptosis, exhibits anti-ferroptotic activity because it scavenges the initiating alkoxyl radicals and other rearrangement products that ferrous iron from lipid hydroperoxides produces.75 In addition, Fer-1 has been reported to effectively reduce the intracellular labile iron pool by forming a complex with Fe2+,75 which partially explains our observation that Fer-1 treatment reduces Fe2+ levels in vivo and in vitro. Further studies demonstrated that Fer-1 did not affect LOX15 alone, but it effectively inhibited HpETE-PE production by the LOX15/phosphatidylethanolamine binding protein-1 (PEBP1) complex.76 Fer-1 treatment in our study significantly attenuated the decline in renal function in vivo and oxidative phospholipids of GECs in vitro, which confirmed that ferroptosis mediated the anti-β2GPI IgG-induced peroxidation of phospholipids and renal injury.
Through the transferrin receptor 1 (TFR1), labile iron is imported and stored in ferritin. The process of ferritin degradation, known as ferritinophagy, releases labile iron and promotes the peroxidation reaction that leads to ferroptosis.77,78 The activity of iron-dependent enzymes is also dependent on iron. Lipid peroxidation is a consequence of iron accumulation and may be a trigger to regulate iron metabolism by affecting membrane fluidity and membrane protein homeostasis, such as related receptors and transporters, as evidenced in a recent report.79 Renal CD31-positive GECs obtained by laser microdissection showed upregulation of TFR and inhibition of SLC40A1, a membrane transporter that mediates iron efflux, was induced by HHcy (Fig. 4f). Similarly, we observed consistent results in Hcy-B CM-treated GECs in vitro (Fig. 5i–k). These changes together lead to iron overload in GECs.
GPX4 is one of the most important antioxidant enzymes, and it uses the cysteine-containing tripeptide glutathione (GSH) to eliminate phospholipid peroxides. The cystine/glutamate transporter system Xc− exchanges intracellular glutamate for extracellular cystine for GSH synthesis.24 GPX4 activity and stability are directly impacted by GSH depletion, which makes cells more susceptible to ferroptosis.80 HHcy induced an increase in the GSSG/GSH ratio and a downregulation of GPX expression in our study due to the downregulation of system Xc−. Intracellular cysteine also originates from the transsulfuration pathway catalyzed by CBS, which is constantly activated in some tumor cells.81 We showed that reduced renal CBS expression may further accelerate GSH depletion and weaken the antioxidant capacity in hypertensive kidneys. Iron accumulation also inhibits antioxidant capacity.
In conclusion, HHcy aggravates hypertensive kidney damage by activating B lymphocytes to secrete pathological anti-β2GPI IgG, which promotes PE-PUFA, as substrates and iron-dependent lipid peroxidation, thus ferroptosis in hypertensive glomerular endothelial cells. This study provides therapeutic guidance for nephropathy patients with HHcy, particularly in aging patients with likely CBS deficiency. The suppression of B lymphocyte hyperactivation or ferroptosis may be an effective therapeutic strategy in HHcy with CKD patients.
## Reagents and antibodies
DL-homocysteine (Hcy, H4628) was purchased from Sigma–Aldrich (St. Louis, MO, USA). Rituximab (RTX, 10 mg/ml), a monoclonal anti-human CD20 antibody, was purchased from Roche (Basel, Switzerland). Ferrostatin-1 (Fer-1, S7243) and liproxstatin-1 (lip-1, S7699) were purchased from Selleckchem (Houston, TX, USA). The following antibodies were used in this work. Anti-β2GPI (bs-1570R) was purchased from Bioss Inc. (Beijing, China). Anti-CBS (cystathionine β-synthase, 14787-1-AP) and anti-CSE (cystathionine γ-lyase, 12217-1-AP) were purchased from Proteintech (Rosemont, IL, USA). Anti-CD31 (sc-18916), anti-nephrin (sc-377246) and anti-PDGFRβ (platelet-derived growth factor receptor beta, sc-374573) were purchased from Santa Cruz Biotech (CA, USA). Anti-4-HNE (4-hydroxynonenal, MAB3249) was purchased from R&D Systems (MN, USA). Anti-xCT (catalytic subunit of cystine/glutamate antiporter System Xc-, also called SLC7A11, ab37185), anti-LOX15 (Lipoxygenase 15, ab23691), anti-GPX4 (glutathione peroxidase 4, ab125066), and anti-TFR (transferrin receptor, ab214039) were purchased from Abcam (Cambridge, MA, USA). Anti-TF (transferrin, A1448), anti-SLC40A1 (A14884), anti-β-actin (AC038), HRP-conjugated goat anti-rabbit (AS014), and HRP-conjugated goat anti-mouse (AS003) were purchased from ABclonal (Wuhan, China).
## Animal models
Male C57BL/6J mice (8 weeks old) received standard or DL-Hcy (1.8 g/L)-containing drinking water for 4 weeks to establish the HHcy model as previously described.18 A 0.12-mm silver clip was placed on the left renal artery to operate 2-kidney, 1-clip (2K1C). The method of the 2K1C model was modified from a previous report.82 Blood pressures of these mice were measured before surgery and post-operatively twice weekly using a tail-cuff blood pressure (BP) measurement system (Kent Scientific Corporation). Kidney tissue was collected 4 weeks after surgery. Plasma renin, Ang II and aldosterone levels were determined to assess RAAS activity using enzyme-linked immunosorbent assay kits (Dogesce, Beijing, China). All animal experiments were carried out in accordance with the Institute of Laboratory Animal Resources and with the approval of Peking University’s Animal Care and Use Committee.
## Renal function detection
Plasma creatinine levels were determined using a colorimetric assay kit (C011-1, Jiancheng, Nanjing, China). Plasma urea concentrations were analyzed using the QuantiChromTM Urea Assay Kit (BioAssay Systems DIUR-500). Random urine was collected, and the levels of microalbuminuria in urine were determined using a mouse microalbuminuria ELISA kit (EIA06046, Xinqidi, Wuhan, China).
## Histological analysis
Paraffin embedding and sectioning were performed on kidney specimens. Ten parts each segment/interval (5 μm) were collected at intervals of 50 μm. Periodic acid Schiff (PAS) and hematoxylin and eosin (H&E) stains were used to identify the architecture of the kidneys. Glomerulosclerosis was examined to gauge the extent of renal injury. Each kidney slide had fifty glomeruli examined to determine its glomerulosclerosis score, which ranged from 0 for normal glomeruli to, 1 for thickening of the GBM, 1.5 for glomerular thickening combined with segmental hypercellularity, 2 for mild segmental hyalinosis (<$25\%$), 2.5 for severe segmental hyalinosis (>$50\%$), 3 for glomerular hyalinosis (or “blobs” of hyaline material deposition) to 4 for diffuse glomerular sclerosis with total tuft obliteration and collapse.83 Six animals per experimental group were examined, and each animal had fifty glomeruli counted. The average of these ratings for each experimental group is used to present the data.
## Measurement of antibody levels
Utilizing mouse ELISA kits, total IgG and anti-β2GPI IgG concentrations in plasma and kidney tissue homogenate supernatants were examined (Bethyl Laboratories, Montgomery, TX, USA).
## Laser capture microdissection of frozen tissue sections
7 μm-thick frozen sections were attached to microscope slides that had been precoated with polyethylene naphthalate for immunofluorescence labeling. Using a Lecia LMD6000 system (Leica, Wetzlar, Germany) in a laminar flow biosafety cabinet, laser microdissection and laser pressure catapulting were carried out to capture CD31-positive glomerular endothelial cells in the tissue slices under fluorescence microscopy. Pure cells or tissues were removed from the slides using a 337 nm pulsed UV laser, collected in a sample tube with trizol, and then were treated to RNA extraction and reversal.
## Cell lines
Purifying magnetic microbeads with anti-CD19 antibodies (Miltenyi Biotec, Bergisch Gladbach, Germany) were used to separate splenic B cells. The RPMI-1640 medium supplemented with $10\%$ fetal bovine serum (FBS, Gibco, Grand Island, NY), and 0.1 mg/mL lipopolysaccharide (LPS, Sigma Aldrich Corporation, St. Louis, MO, USA) was used to culture the purified B cells. Prior to the following measurements, the B cells were either treated with or without 100 μM Hcy.18 Human renal glomerular endothelial cells (HGECs) (ScienCell, 4000) were cultured in endothelial cell medium (ScienCell, 1001) containing $5\%$ FBS and $1\%$ endothelial cell growth supplement (ECGS). When cell confluency reached 70–$80\%$, the cells were treated with or without Ang II (1 μM) and with or without a $50\%$ volume of culture supernatant from Hcy-activated B cells for 24 h to simulate the renal hypertension environment and measure the bioactive effects of anti-β2GPI-derived culture supernatants of Hcy-activated B cells. Stimulation concentrations and times were determined using Cell Counting Kit-8 (CCK-8) assays (Solarbio, Beijing, China).
Purified anti-phospholipid antibodies (aPL, 100 μg/ml) and control IgG (NHIgG, 100 μg/ml) from the Department of Rheumatology, Peking University People’s Hospital were utilized to treat GECs for 24 h. Healthy non-autoimmune persons provided NHIgG. Patients with APS provided aPL.84
## Ferroptosis assay
For HHcy 2K1C mice, Fer-1 (1 mg/kg/day, i.p.) was used for 4 weeks. HGECs were treated with or without Fer-1 (5 μM) or liproxstatin-1 (200 nM) for 24 h in the absence of 10−6 M Ang II and $50\%$ volumes of culture supernatant from Hcy-activated B cells. Cell death was detected by LDH release using the LDH Detection Kit (A020-2, Jiancheng, Nanjing, China) and Annexin V-FITC/PI staining (M&C Gene Technology Ltd. Beijing, China). Intracellular Fe2+ levels were measured using an Iron Assay Kit from Leagene. Malondialdehyde (MDA) and lipid peroxide (LPO) in kidney tissue homogenate samples and HGECs were assayed using ELISA kits (Jiancheng, Nanjing, China). Kidney tissue homogenate supernatants and the intracellular GSSG/GSH and NADP+/NADPH ratios were measured using glutathione and NADPH assay kits, respectively (Jiancheng, Nanjing, China).
## Metabolomics analysis
The metabolome measurements were carried out by Changzhou, China’s Zhongke Zhidian Biotechnology Co. Lipid extraction and Analyses methods based on previous reports.79
## Lipid peroxidation analysis using BODIPY-C11
HGECs were co-treated with 1 μM BODIPY-C11 and 1 μg/ml Hoechst for the final hour of the treatment. On a confocal laser scanning microscope (Leica, Germany), images of the reduced form of BODIPY-C11 and the oxidized form were captured at 563 nm and 488 nm, respectively. All images (14 images per well) were acquired using the same instrument specifications and processed using the same settings.
Adding 5 μM BODIPY-C11 dye to the culture medium during the final hour of the treatment. HGECs for flow cytometry analysis were trypsinized, stained with 7-AAD for 5 min, and then filtered into single-cell suspensions. PE-Texas Red filter was for reduced BODIPY-C11 and FITC filter for oxidized BODIPY-C11. With few alterations, the experimental procedure was carried out as it was in the prior report.85
## Phen Green SK Staining
HGECs were loaded with 10 μM Phen Green SK (PGSK, Cayman Chemical, United States) at 37 °C for 10 min after being washed with Hanks’ buffered salt solution (HBSS, pH 7.3). Hoechst 33342 was used to stain the nuclei for 10 min. The fluorescence of the images was assessed using laser scanning microscopy at an excitation wavelength of 507 nm and an emission wavelength of 532 nm. When Fe2+ is present, PGSK fluorescence is reduced, and the amount of reduction is inversely correlated with the concentration of Fe2+ in the solution.
Cell suspensions were collected and added with the PGSK probe for flow cytometry analysis. The cells were centrifuged at 300 g for 20 min after incubation, then washed and resuspended. Fluorescence activating cell sorting (FACS) was used to determine the relative PGSK level. We calculated the change in Fe2+ concentration using the decrease in PGSK fluorescence.
## Immunofluorescence staining
Frozen kidney slices (7 μm) were blocked for 1 h, and then incubated with primary antibody (1:50 dilution) overnight at 4 °C. Secondary antibody (1:500 dilution) was incubated on the sections for one hour at room temperature. Image J was used to analyze the scatter plot generated by colocalization to indicate the degree of colocalization. The closer the scatter plot is to the diagonal line, the higher the degree of colocalization is, and vice versa.
## Western blot analysis
In the presence of protease inhibitors, kidney tissue or HGECs were lysed. Equal amounts of protein were electrotransferred to polyvinylidene fluoride membranes after being separated by SDS-PAGE on running gels of $8\%$, $10\%$, or $12\%$. Treated with various antibodies (1:1000) overnight at 4 °C after being blocked with $10\%$ bovine serum albumin, and then with secondary antibodies for 1 h at room temperature. Odyssey for infrared imaging was used to detect the immunofluorescence intensity of the bands (LI-COR Biosciences, Lincoln, NE, USA). The density of the band’s pixel intensity was measured using Image J software, and it was then normalized to the equivalent loading control intensity.
## Quantitative PCR analysis of mRNA levels
Using a reverse transcription apparatus (Promega, Madison, WI, USA), two micrograms of RNA produced with Trizol reagent (Promega, Madison, WI, USA) was converted into cDNA. SYBR Green I fluorescence and a Mx3000 Multiplex Quantitative PCR System were used to perform qPCR. The Stratagene Mx3000 software was used to calculate all the results, and the relative mRNA levels were normalized to β-actin. The primer sequences used were listed as follows: mouse gpx4 (forward 5′-TGTGCATCCCGCGATGATT-3′; reverse 5′-CCCTGTACTTATCCAGGCAGA-3′); mouse slc7a11 (forward 5′-AGGGCATACTCCAGAACACG-3′; reverse 5′-GGACCAAAGACCTCCAGAATG-3′); mouse lox15 (forward 5′-GGCTCCAACAACGAGGTCTAC-3′; reverse 5′-CCCAAGGTATTCTGACACATCC-3′); mouse acsl4 (forward 5′-CCTGAGGGGCTTGAAATTCAC-3′; reverse 5′-GTTGGTCTACTTGGAGGAACG-3′); mouse tf (forward 5′-GCTGTCCCTGACAAAACGGT-3′; reverse 5′-GTCACGGAAGCTGATGCACT-3′); mouse tfr (forward 5′-GTTTCTGCCAGCCCCTTATTAT-3′; reverse 5′-GCAAGGAAAGGATATGCAGCA-3′); mouse slc40a1 (forward 5′-GCGATCACAATCCAAAGGGAC-3′; reverse 5′-TTGGTTAGCTGGTCAATCCTTC-3′); mouse etnk2 (forward 5′-CGGTGGAACAGGACGACATC-3′; reverse 5′-AGGCCAATAGCTTGTTGGTGA-3′); mouse pcyt2 (forward 5′-TGGTGCGATGGCTGCTATG-3′; reverse 5′-CCCTTATGCTTGGCAATCTCC-3′); mouse ept1 (forward 5′-CTACTCCTGACATACTTCGACCC-3′; reverse 5′-CCACGACAATCCAAACCCAG-3′); mouse pemt (forward 5′-ATCACCATTGTGTTCAACCCAC-3′; reverse 5′-CCAGGGAATAGCAGGCTAGG-3′); mouse lpcat3 (forward 5′-GACGGGGACATGGGAGAGA-3′; reverse 5′-GTAAAACAGAGCCAACGGGTAG-3′); mouse actb (forward 5′-GTGACGTTGACATCCGTAAAGA-3′; reverse 5′-GCCGGACTCATCGTACTCC-3′); human ACSL4 (forward 5′-CATCCCTGGAGCAGATACTCT-3′; reverse 5′-TCACTTAGGATTTCCCTGGTCC-3′); human LOX12 (forward 5′-ATGGCCCTCAAACGTGTTTAC-3′; reverse 5′-GCACTGGCGAACCTTCTCA-3′); human LOX15 (forward 5′-GGGCAAGGAGACAGAACTCAA-3′; reverse 5′-CAGCGGTAACAAGGGAACCT-3′); human GPX4 (forward 5′-GAGGCAAGACCGAAGTAAACTAC-3′; reverse 5′-CCGAACTGGTTACACGGGAA-3′); human SLC7A11 (forward 5′-TCTCCAAAGGAGGTTACCTGC-3′; reverse 5′-AGACTCCCCTCAGTAAAGTGAC-3′); human TFR (forward 5′-ACCATTGTCATATACCCGGTTCA-3′; reverse 5′-CAATAGCCCAAGTAGCCAATCAT-3′); human SLC40A1 (forward 5′-CTACTTGGGGAGATCGGATGT-3′; reverse 5′-CTGGGCCACTTTAAGTCTAGC-3′); and human ACTB (forward 5′-CATGTACGTTGCTATCCAGGC-3′; reverse 5′-CTCCTTAATGTCACGCACGAT-3′).
## RNA-sequencing
Con-B CM + Ang II and Hcy-B CM + Ang II treated GECs were collected, and RNA was extracted and purified. Library preparations and sequencing were conducted by BGI Gene Company (Shenzhen, China).
## Statistical analysis
GraphPad Prism 9.0 and IBM SPSS 24.0 were used to analyze the data. All results are presented as the means ± SEM with a minimum of three replicates. The Shapiro-Wilk normality test was used to determine whether the data were normally distributed. For normal distribution comparisons, Student’s t test or one-way ANOVA with Tukey’s analysis were performed. The significance of differences was set at $P \leq 0.05.$
## Supplementary information
Supplementary Materials Table 1--Lipidomics of kidney tissue Table 2--Free fatty acids of kidney tissue Table 3--Oxidative phospholipidomics of GECs The online version contains supplementary material available at 10.1038/s41392-023-01313-x.
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|
---
title: Using sleep heart rate variability to investigate the sleep quality in children
with obstructive sleep apnea
authors:
- Li-Ang Lee
- Hai-Hua Chuang
- Hui-Shan Hsieh
- Chao-Yung Wang
- Li-Pang Chuang
- Hsueh-Yu Li
- Tuan-Jen Fang
- Yu-Shu Huang
- Guo-She Lee
- Albert C. Yang
- Terry B. J. Kuo
- Cheryl C. H. Yang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10008856
doi: 10.3389/fpubh.2023.1103085
license: CC BY 4.0
---
# Using sleep heart rate variability to investigate the sleep quality in children with obstructive sleep apnea
## Abstract
### Background
Obstructive sleep apnea (OSA) is associated with impaired sleep quality and autonomic dysfunction. Adenotonsillectomy significantly improves subjective and objective sleep quality in children with OSA. However, the postoperative changes in heart rate variability (HRV) indices (indicators of cardiac autonomic function) and their importance remain inconclusive in childhood OSA. This retrospective case series aimed to investigate the association of sleep HRV indices, total OSA-18 questionnaire score (a subjective indicator of sleep quality) and polysomnographic parameters (objective indicators of sleep quality), and effects of adenotonsillectomy on HRV indices, total OSA-18 questionnaire score and polysomnographic parameters in children with OSA.
### Methods
Seventy-six children with OSA were included in baseline analysis, of whom 64 ($84\%$) completed at least 3 months follow-up examinations after adenotonsillectomy and were included in outcome analysis. Associations between baseline variables, and relationships with treatment-related changes were examined.
### Results
Multivariable linear regression models in the baseline analysis revealed independent relationships between tonsil size and obstructive apnea-hypopnea index (OAHI), adenoidal-nasopharyngeal ratio and very low frequency (VLF) power of HRV (an indicator of sympathetic activity), and normalized low frequency power (an indicator of sympathetic activity) and OAHI. The outcome analysis showed that adenotonsillectomy significantly improved standard deviation of all normal-to-normal intervals, and high frequency power, QoL (in terms of reduced total OSA-18 questionnaire score), OAHI and hypoxemia. Using a conceptual serial multiple mediation model, % change in OSA-18 questionnaire score and % change in VLF power serially mediated the relationships between change in tonsil size and % change in OAHI.
### Conclusions
The improvement in OAHI after adenotonsillectomy was serially mediated by reductions in total OSA-18 questionnaire score and VLF power. These preliminary findings are novel and provide a direction for future research to investigate the effects of VLF power-guided interventions on childhood OSA.
## 1. Introduction
Over $4\%$ of children worldwide suffer from obstructive sleep apnea (OSA) [1]. OSA, characterized by snoring and abnormal breathing during sleep, is a chronic disorder with many comorbidities, including cardiovascular sequelae [2] and cognitive/behavioral problems [3]. OSA considerably reduces sleep quality in children [4]. Furthermore, childhood OSA has been associated with hypofunction in brain autonomic control regions [5], which can influence heart rate and heart rate variability (HRV) by the interposition of cortico-subcortical pathways to the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) [6].
Unlike clinical signs and symptoms, which are often direct presentations of a disease, HRV reflects more indirect underlying pathophysiological process, either causal, mediating, or reactive, which allows measurements of the HRV to serve as a biomarker in a wide range of health conditions [7]. Time domain and frequency domain HRV analysis on electrocardiograms are useful for diagnosing different clinical and functional conditions [8]. For example, 24-h HRV indices are significantly associated with sleep disturbance and depression symptoms of medical students [9]. In children with OSA, sleep fragmentation, arousal, and hypoxemia may increase SNS activity [10]. However, sleep stage-specific HRV measurements have shown significantly downregulated PNS activity in children with sleep-disordered breathing [11]. Studies on HRV in children with OSA have reported inconsistent results (12–14), and thus further investigations on cardiac autonomic function in this population are warranted.
Hypertrophy of adenoids and tonsils is the most common cause of upper airway obstruction in children [15], and adenotonsillectomy is the first-line treatment for childhood OSA [12, 16]. Adenotonsillectomy significantly reduces the severity of OSA in terms of apnea-hypopnea index (AHI) and sympathetic activity [17] and sustainably improved quality of life [18]. However, approximately $70\%$ of children have residual OSA [19], which still threatens children's health. Further, changes in OSA-related HRV indices are not related to changes in AHI and hypoxemia [14]. Accordingly, the aims of this study were to evaluate the reproducibility of sleep HRV analysis, the associations of sleep HRV and sleep quality, and the changes in HRV indices after adenotonsillectomy in children with OSA, and understand how these changes relate to adenoid-tonsil size and improvements in polysomnographic parameters.
## 2.1. Study participants
The Institutional Review Board of Chang Gung Medical Foundation approved this retrospective case series (No. 202200882B0). The requirement for written informed consent was waived because the current study was based on a secondary analysis of existing data. This study followed the World Medical Association's Declaration of Helsinki and the Strengthening the Reporting of Cohort Studies in Surgery guidelines [20].
We included consecutive children who underwent adenotonsillectomy for OSA at Chang Gung Memorial Hospital, Linkou Main Branch (Taoyuan, Taiwan) between March 1, 2017 and September 30, 2021. The inclusion criteria were: [1] age 5–12 years, and [2] obstructive AHI (OAHI) ≥ 2.0 events/h or obstructive apnea index (OAI) ≥ 1.0 events/h [21, 22]. The exclusion criteria were [1] patients with craniofacial, neuromuscular, or chronic inflammatory disorders [23, 24], or [2] patients without available polysomnographic data. All the children underwent extracapsular tonsillectomy with tonsillar pillar suturing and adenoidectomy that aimed to improve the upper airway obstruction by the principal investigator (L-AL) in a single stage under general anesthesia [25]. Children with follow-up polysomnographic data were included in outcome analysis (Figure 1).
**Figure 1:** *Flow diagram. OSA, obstructive sleep apnea.*
## 2.2. Clinical variables
Age, sex, body mass index (BMI), tonsil size, adenoidal-nasopharyngeal ratio (ANR) and evening blood pressure (BP) [2, 26], OSA-related quality of life, and polysomnographic parameters were recorded. All the clinical measurements were performed before and at least 3 months after adenotonsillectomy.
The tonsils were graded with a size scale from 1–4 (1: tonsils within the tonsillar; 2: tonsils visible outside the anterior pillars; 3: tonsils extending three-quarters of the way to the midline; 4: tonsils meeting at the midline) [27].
The ANR (distance from the point of maximal convexity of the adenoid shadow/the distance between the posterior border of the hard palate and the anteroinferior edge of the sphenobasioccipital synchondrosis) was measured on neck lateral view [28].
## 2.3.1. Subjective measurement
All parents evaluated their children's OSA-related quality of life using the Chinese version of the OSA-18 questionnaire [29], which includes 18 items grouped into 5 domains: sleep disturbance (4 items), physical suffering (4 items), emotional distress (3 items), daytime problems (3 items), and caregiver concerns (4 items). Each item was scored using a 7-point ordinal scale. The total score was calculated as the sum of the 18 items (overall range, 18–126) and has been shown to have excellent test-retest reliability [30].
## 2.3.2. Objective measurement
All participants underwent full-night, in-laboratory polysomnography (Nicolet Biomedical Inc., Madison, WI, USA) [23]. OAHI, OAI, arousal index, mean blood oxygen saturation (SaO2), minimal SaO2, sleep stages and total sleep time were scored and manually verified by the study investigators (L-PC and Y-SH) using a standard approach of the American Academy of Sleep Medicine [31]. For example, the AHI was calculated by dividing the sum of all apneas (defined as a ≥ $90\%$ reduction in airflow for a duration of ≥ 2 consecutive breaths) and hypopneas (defined as a ≥ $30\%$ reduction in airflow in association with electroencephalographic arousal or a ≥ $3\%$ reduction in SpO2 for a duration of ≥ 2 consecutive breaths) by the hours of total sleep time.
## 2.4. Sleep heart rate variability analysis
Electrocardiographic polysomnography signals were analyzed using HRV software (profusionSLEEPTM, version 4.5, build 502, Compumedics, Abbotsford, Australia). For artifact correction, automated annotations of electrocardiographic signals, such as loose leads, motion artifacts, and broken wires [32], were manually verified by trained technicians who had been certificated by the domestic board of the Taiwan Society of Sleep Medicine and shown substantial-to-almost perfect reliabilities in the scoring of respiratory events (intraclass correlation coefficients [ICCs] ranged from 0.66 to 0.98) [33]. According to standard guidelines, time-domain indices, including standard deviation of all normal-to-normal (N-N) intervals (SDNN), number of pairs of adjacent N-N intervals differing by more than 50 ms in the entire recording divided by the total number of all N-N intervals (pNN50), and square root of the mean of the sum of the squares of differences between adjacent N-N intervals (RMSSD) were recorded. In addition, frequency-domain indices, including total power (0.0033–0.4 Hz), very low frequency (VLF) power (0.0033–0.04 Hz), low frequency (LF) power (0.04–0.15 Hz), normalized LF power (LF%), high frequency (HF) power (0.15–0.4 Hz), and LF/HF ratio were also recorded (Table 1) (34–39).
**Table 1**
| Variable | Unit | Description | Meaning |
| --- | --- | --- | --- |
| Time-domain indices | Time-domain indices | Time-domain indices | Time-domain indices |
| N-N | ms | Time interval between N-N heartbeats | |
| SDNN | ms | Standard deviation of all N-N intervals. | Total capacity of the regulation system (35) |
| pNN50 | % | Number of pairs of adjacent N-N intervals differing by more than 50 ms divided by the total number of all N-N intervals. | Increased parasympathetic activity (35) |
| RMSSD | ms | The square root of the mean of the sum of the squares of di?erences between adjacent N-N intervals. | Increased parasympathetic activity (36) |
| Frequency-domain indices | Frequency-domain indices | Frequency-domain indices | Frequency-domain indices |
| Total power | ms2 | The variance of N-N intervals over the approximately the temporal segment (approximately ≤ 0.4 Hz) | Total capacity of the regulation system (35) |
| VLF power | ms2 | Power in very low frequency range ( ≤ 0.04 Hz) | Sympathetic activity (36) |
| LF power | ms2 | Power in low frequency range (0.04–0.15 Hz) | Baroreceptor activity (37) |
| LF% | % | LF power / (Total Power–VLF power) × 100 | Sympathetic modulation (38) |
| HF power | ms2 | Power in high frequency range (0.15–0.4 Hz) | Parasympathetic modulation (39) |
| LF/HF ratio | | Ratio LF [ms2]/HF [ms2] | Sympathovagal balance (34) |
## 2.5. Reproducibility assessment
Reproducibility of the HRV measurements was assessed using ICCs (two-way random model; absolute agreement type) from data quantified from separate sleep HRV measurements performed at least 3 months apart in a sample of 12 children with stable residual OSA [defined as postoperative OAHI within (preoperative OAHI−5.6 events/h) to (preoperative OAHI + 6.8 events/h), compatible with the upper and lower limits of agreement of OAHI measured on the first and second night in children and adolescents] [40]. This sample represented children who did not undergo adenotonsillectomy. ICCs evaluated reproducibility as “poor” (< 0.001), “slight” (0.001–0.020), “fair” (0.021–0.40), “moderate” (0.41–0.60), “substantial” (0.61–0.80), and almost perfect (0.81–1.00) [41].
## 2.6. Statistical analysis
Data were analyzed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 9.0 for Windows (Graph Pad Software Inc., San Diego, CA, USA). Changes in scores were calculated as postoperative minus preoperative values. Percentage change [(change in score/preoperative value) × 100] was calculated for variables of interest. Because all the children underwent extracapsular tonsillectomy, the change in tonsil size was equal to the negative value of tonsil size and used for further statistical analysis.
Using the Shapiro-Wilk test to examine normality, descriptive statistics were expressed as mean (standard deviation) for normally distributed continuous variables, median (interquartile range [IQR]) for skewed variables, and number (proportion) for categorical variables.
For continuous variables, the independent-samples t-test or Mann-Whitney U test was used to assess between-group changes; the paired-samples t-test or Wilcoxon signed-rank test was used to assess within-group changes as appropriate. Differences in categorical variables between two subgroups were analyzed using Fisher's exact test.
To facilitate comparisons with previous studies, linear regression models, or mediation and moderation analysis, non-normally distributed data of reference studies were transformed to normal after estimation from the sample size (n), median (m), and the first (q1) and third (q3) quartiles [42, 43]. The sample standard deviation was estimated to be [(q3 – q1) / η] where η = η(n) = 2Φ−1[(0.75 × n – 0.125) / (n + 0.25)] [42]. In addition, non-normally distributed continuous variables were transformed to normal using a two-step approach: fractional rank and inverse-normal transformation [44]. For comparisons with reference values, the one-sample t-test was applied.
Relationships between variables of interest were assessed using Pearson and Point-Biserial correlation tests as appropriate. Multivariable linear regression models, including all variables, with manual selection based on a probability of F < 0.05 were used to identify independent variables. The variance inflation factor of each predictor was calculated to adjust for intervariable relationships within the model. The regression model was repeated after removing all variables with a variance inflation factor ≥ 5 to reduce multicollinearity [45].
Conditional process analysis was performed to evaluate the mediators and moderators between changes in tonsil size/ANR and % changes in polysomnographic parameters using the SPSS PROCESS macro (version 4.1) [46]. Bias-corrected $95\%$ confidence intervals (CIs) were estimated via bootstrapping (5,000 runs) to verify mediation, moderated mediation, or mediated moderation. A two-sided $P \leq 0.05$ was considered statistically significant.
## 3.1. Participants' characteristics
Seventeen ($22\%$) girls and 59 ($78\%$) boys with OSA (median OAHI, 5.5 [IQR, 2.3–12.6] events/h) were included in the baseline analysis (Figure 1), of whom 64 ($84\%$) were included in the outcome analysis and 12 ($16\%$) were not included due to no available follow-up polysomnography. All baseline variables were comparable between these two subgroups (Table 2).
**Table 2**
| Variable | Participants included in the baseline analysis | Participants included in the outcome analysis | Participants excluded in the outcome analysis | P valuea |
| --- | --- | --- | --- | --- |
| N | 76 | 64 | 12 | |
| Clinical variables | Clinical variables | Clinical variables | Clinical variables | Clinical variables |
| Age at diagnosis (years) | 7 (6–9) | 6 (5–9) | 7 (6–10) | 0.417 |
| Male sex, n (%) | 59 [78] | 47 [73] | 12 (100) | 0.058 |
| BMI (kg/m2) | 17.4 (15.3–22.8) | 17.3 (15.0–21.9) | 21.1 (15.6–26.8) | 0.133 |
| Tonsil size | 3 (3–4) | 3 (3–4) | 3 (3–4) | 0.703 |
| ANR | 0.800 (0.697–0.872) | 0.800 (0.710–0.864) | 0.691 (0.585–0.848) | 0.093 |
| Systolic BP (mmHg) | 104.2 (18.0) | 103.5 (17.2) | 107.9 (22.1) | 0.440 |
| Diastolic BP (mmHg) | 65 (59–71) | 65 (59–71) | 66 (57–76) | 0.825 |
| Subjective sleep quality (assessed by the OSA-18 questionnaire) | Subjective sleep quality (assessed by the OSA-18 questionnaire) | Subjective sleep quality (assessed by the OSA-18 questionnaire) | Subjective sleep quality (assessed by the OSA-18 questionnaire) | Subjective sleep quality (assessed by the OSA-18 questionnaire) |
| OSA-18 score | 81.3 (15.4) | 81.8 (15.7) | 78.4 (14.1) | 0.486 |
| Objective sleep quality (assessed by polysomnography) | Objective sleep quality (assessed by polysomnography) | Objective sleep quality (assessed by polysomnography) | Objective sleep quality (assessed by polysomnography) | Objective sleep quality (assessed by polysomnography) |
| OAHI (events/h) | 5.5 (2.3–12.6) | 5.8 (2.4–13.1) | 6.3 (2.4–10.4) | 0.943 |
| OAI (events/h) | 0.4 (0.1–1.5) | 0.6 (0.2–1.7) | 0.5 (0.18–1.5) | 0.908 |
| Arousal index (events/h) | 9.8 (7.2–16.5) | 9.9 (7.3–16.9) | 9.0 (6.7–15.2) | 0.397 |
| Mean SaO2 (%) | 97 (97–98) | 97 (97–98) | 97 (96–98) | 0.480 |
| Minimal SaO2 (%) | 84 (90–92) | 90 (84–92) | 90 (83–92) | 0.797 |
| N1 sleep (%) | 10 (6–15) | 10 (6–16) | 9 (7–14) | 0.569 |
| N2 sleep (%) | 39.1 (8.6) | 39.2 (9.2) | 39.0 (4.8) | 0.911 |
| N3 sleep (%) | 28 (22–35) | 27 (22–35) | 29 (23–35) | 0.711 |
| REM sleep (%) | 19.3 (5.9) | 19.1 (5.8) | 20.2 (6.3) | 0.556 |
| TST (min) | 337 (321–352) | 337 (320–353) | 329 (321–349) | 0.437 |
| Sleep heart rate variability indices | Sleep heart rate variability indices | Sleep heart rate variability indices | Sleep heart rate variability indices | Sleep heart rate variability indices |
| Heart rate (bpm) | 76 (70–82) | 76 (70–85) | 76 (70–82) | 0.770 |
| N-N interval (ms) | 791.8 (97.3) | 793.7 (95.6) | 781.6 (93.3) | 0.694 |
| SDNN (ms) | 96.6 (32.6) | 98.3 (34.1) | 87.4 (21.7) | 0.163 |
| pNN50 (%) | 36.9 (19.0) | 37.1 (19.2) | 35.8 (5.5) | 0.840 |
| RMSSD (ms) | 67 (50–105) | 63 (49–114) | 69 (53–80) | 0.680 |
| Total power (ms2) | 8688 (4614–14944) | 9454 (4499–15430) | 7560 (5233–9443) | 0.298 |
| VLF power (ms2) | 1509 (1095–2540) | 1509 (1095–2727) | 1479 (895–1833) | 0.340 |
| LF power (ms2) | 1236 (760–2150) | 1243 (734–2507) | 992 (804–1526) | 0.243 |
| LF% (%) | 37 (28–47) | 38 (29–48) | 33 (27–42) | 0.494 |
| HF power (ms2) | 2140 (1113–4228) | 1970 (1103–5155) | 2183 (1248–4412) | 0.669 |
| LF/HF ratio | 0.59 (0.40–0.90) | 0.62 (0.40–0.92) | 0.50 (0.40–0.70) | 0.459 |
## 3.2. Sleep heart rate variability
Distributions of HRV indices in baseline analysis are summarized in Table 2. For comparing with previous studies, the HRV indices in this study (full-night), normal controls (full-night) [47], children with OSA (full-night) [14], children with moderate-to-severe OSA (N3 sleep) [13, 17], and children with OSA/obesity (full-night) [48] are summarized in Table 3. Comparing with three representative full-night HRV studies [14, 47, 48], SDNN, total power and VLF power in the children with OSA were significantly higher than normal values (Figure 2).
## 3.3. Measurement reproducibility of sleep heart rate variability
To assess measurement reproducibility, we calculated ICCs using HRV indices measured at least 3 months apart in 12 patients with stable residual OSA after adenotonsillectomy (Table 4). Their variables of interest were comparable to the patients with altered OSA (Tables 5, 6). Most HRV measurements demonstrated moderate (N-N interval, SDNN, RMSSD, total power, LF%) or substantial (VLF power, LF power, LF/HF ratio) reproducibility. Further, the reproducibility of pNN50 and HF power were fair [41].
## 3.4. Associations between variables of interest at baseline
Nested data structure and significant correlations were found among the polysomnographic parameters, several clinical variables and HRV indices (Figure 3). However, total OSA-18 questionnaire score was not associated with variables of interest. Using multivariable linear regression models (Table 7), male sex, OAHI and N3 sleep were independently associated with tonsil size, and systolic BP, OAHI and VLF power were independently associated with ANR. Furthermore, tonsil size, diastolic BP and LF% were independently correlated with OAHI. Table 7 summarizes the independent associations of other polysomnographic parameters with the variables of interest.
**Figure 3:** *Associations of polysomnographic variables with clinical variables and sleep heart rate variability indices. Data are summarized as Pearson's or Point-Biserial rho, as appropriate. Blank spaces mean two-sided P ≥ 0.05.* TABLE_PLACEHOLDER:Table 7
## 3.5. Changes in the variables of interest after adenotonsillectomy
The median follow-up period was 4 (IQR, 3–6) months. In outcome analysis, mean SaO2, minimal SaO2 and rapid eye movement sleep significantly increased, and OSA-18 score, OAHI, OAI, arousal index and N1 sleep significantly reduced after adenotonsillectomy (Table 5).
Regarding HRV indices, SDNN, total power and HF power significantly reduced after adenotonsillectomy (Table 6), and they were still significantly different from normal values [47] (Figure 2).
## 3.6. Associations of percentage changes in the variables of interest
Correlations of % changes in polysomnographic parameters and % changes in clinical variables and HRV indices also revealed significant associations with nested data structure (Figure 4). Using multivariable linear regression models (Table 8), % changes in OSA-18 score, OAHI and HF power were independently associated with change in tonsil size. Age at diagnosis, male sex and % change in arousal index were independently associated with change in ANR, and change in tonsil size was independently correlated with % change in OAHI.
**Figure 4:** *Associations of % changes in polysomnographic variables, % changes in clinical variables, and % changes in sleep heart rate variability indices. Blank spaces mean two-sided P ≥ 0.05.* TABLE_PLACEHOLDER:Table 8
## 3.7. Mediation and moderation analyses
Consistent relationships between “tonsil size and OAHI” and “change in tonsil size and % change in OAHI” were observed. Mediation and moderation analyses were performed from change in tonsil size to % change in OAHI, especially with regards to HRV indices, and only a significant conceptual serial multiple mediation model was identified: change in tonsil size (independent variable), % change in OSA-18 score (first mediator), % change in VLF power (second mediator), and % change in OAHI (dependent mediator) (Figure 5). The direct paths from change in tonsil size to % change in OAHI, change in tonsil size to % change in OSA-18 score, change in tonsil size to % change in VLF power, change in OSA-18 to % change in VLF power, and % change in VLF power to % change in OAHI were significant. In contrast, the direct paths from change in OSA-18 to % change in OAHI were not significant. The serial mediation model revealed a positive total effect (β = 65.78, standard error = 16.71, $P \leq 0.001$). The direct effect of change in tonsil size on % change in OAHI (β = 44.47, standard error = 18.90, $$P \leq 0.022$$) was significant. For the indirect effects, the first path from change in tonsil size to % change in OAHI through % change in OAS-18 score (effect = 12.44, $95\%$ CI:−5.18–32.99) was not significant. The second path through % change in VLF power (effect = 13.74, $95\%$ CI: 0.01–33.36), third path through % change in OAS-18 score and % change in VLF power (effect = −4.87, $95\%$ CI:−13.69-−0.09), and indirect effect (effect = 21.32, $95\%$ CI: 0.39–44.30) were significant.
**Figure 5:** *A serial multiple mediation model of the effect of change in tonsil size. Data are summarized as β and standard errors. *P < 0.05 and ≥ 0.01; **P < 0.01 and ≥ 0.001. Solid lines indicate significant paths, while a dotted line indicates a non-significant path. OAHI, obstructive apnea-hypopnea index; OSA, obstructive sleep apnea; VLF, very low frequency.*
## 4. Discussion
This study is the first to report that OSA-related quality of life and VLF power were first and second mediators of the relationship between tonsil size and improvement in AHI using a conceptual serial multiple mediation model. Beyond providing important mechanistic insights, these results suggest that VLF power could be a new target for OSA therapy in children. For example, exercise training can decrease VLF power over time [49] and reduce AHI [50] in adults.
Our results confirmed the reproducibility of sleep HRV measurements at two time points. Most measures showed moderate or substantial reproducibility, except for pNN50 and HF power. The possible reason for this relatively lower reproducibility may be related to sleep stage and arousal index. To the best of our knowledge, no comprehensive reproducibility study has reported HRV measurements in children with OSA. Accordingly, the interpretations of sleep pNN50 and HF power should be made with caution in this population.
Using full-night HRV measurements, SDNN, total power, and VLF power in the children with OSA were significantly higher than normal values (Figure 2) [14, 47, 48]. SDNN and total power represent total capacity of the regulation system, whilst VLF power represents sympathetic activity [36] (Table 1). Although SNS and PNS activities both contribute to SDNN and total power, long-term recordings have revealed that SNS activity is more related to these indices [51]. The transition between normal and pathological respiration can enhance SNS activity rather than PNS activity in adults with OSA [52]. Additionally, the results of this and previous studies [13, 14, 53] suggest that sympathetic activity increases during sleep in children with OSA; however, SDNN in the 12 obese children with OSA in this study was comparable to normal values [47] (Table 3). This discrepancy may be explained by the patients' weight status, since childhood obesity is significantly related to low SDNN [54]. Furthermore, this study and Isaiah's study [14] found that % changes in SDNN and total power were not related to % change in OAHI. Therefore, these changes could not be simply due to improvements in OAHI after adenotonsillectomy.
The baseline values and % changes in tonsil size and ANR were not consistently associated with most HRV indices. Despite increased ANR being related to decreased VLF power in children with OSA, the causal relationship between adenoid hypertrophy and reduced sympathetic activity could not be supported by the post-operative changes. Nevertheless, our findings suggested a positive relationship between change in tonsil size and % change in HF power of HRV (parasympathetic modulation). We hypothesize that tonsillectomy may directly injure or cause scar formation, thereby reducing function of the lingual branch of the hypoglossal nerve, interrupting baroreceptor signaling at the carotid sinus, influencing vagus nerve function, eventually resulting in decreased parasympathetic modulation and increased sympathetic activity of cardiac autonomic function during sleep. This condition may further interfere with the relationships between % change in SDNN or total power and % change in OAHI.
Our results demonstrated significant relationships between the change in tonsil size and % change in OAHI as well as relationships between the change in tonsil size and % change in OSA-18 score as previous studies [29, 55]. Tonsil size has been significantly associated with the change in OSA-18 score after tonsillectomy in children with sleep-disordered breathing [56]. Although a change in AHI has been associated with a change in OSA-18 score [23], we found that this association was not independent in this study. In simple mediation and moderation models, % change in OSA-18 score neither mediated nor moderated the relationship between the change in tonsil size and % change in OAHI. However, in serial mediation analysis, the relationship between the change in tonsil size and % change in OAHI was mediated by % change in OSA-18 score and % change in VLF power in serial analysis, and also by % change in VLF power alone (Figure 5).
VLF rhythm is a cardiac intrinsic rhythm which is essential for health and happiness [36]. Even though there is currently no agreement on the physiological mechanisms responsible for activity within the VLF band, low VLF power has been associated with adverse outcomes and all-cause mortality [57, 58]. This band is generated by the stimulation of afferent sensory neurons in the heart [59]. In animal models, stressful stimulation [60] and paradoxical sleep deprivation [61] have been shown to significantly reduce VLF power [60]. In this study, the inverse relationship between % change in OSA-18 and % change in VLF power suggested that reduced OSA-specific stress and sleep disturbance may increase sleep VLF power.
However, VLF power is an independent predictor of AHI in humans [62]. VLF power is significantly elevated during pathological respiration compared with normal respiration in OSA patients [52]. Therefore, it is reasonable that reduced AHI may contribute to a decrease in VLF power after adenotonsillectomy. Our mediation model also highlighted the possibility that changes in VLF power may influence changes in AHI in children with OSA. Sympathetic abnormalities were shown to precede the development of mild OSA in a cohort of adults with no known diagnosis of OSA [63]. Although further direct evidence is warranted, these studies suggest that a reduction in VLF power may help to alleviate the AHI in children with OSA.
Therefore, the mediation role of VLF power on the relationship between change in tonsil size and % change in AHI is of interest. Increasing exercise intensity can reduce awake VLF power [49], and morning exercise can increase sleep VLF power [64] in adults. Besides, exercise training [65] or aerobic exercise combined with resistance training can reduce AHI in adults. Therefore, VLF power is modifiable and may be a marker of therapeutic efficacy and a potential therapeutic target for OSA [66]. However, in children with adenotonsillar hypertrophy, it may be unlikely that addressing the HRV independently will improve AHI unless there is a clear demonstration the neuromuscular tone is improved to the point that the tonsils do not medialize during sleep. Accordingly, future studies should focus on VLF power-lowering interventions and their effects on the severity of childhood OSA.
## 4.1. Strengths and limitations
Compared with previous studies [13, 14, 17, 48], the greatest strengths of this investigation were the inclusion of a sample of representative and well-characterized pediatric OSA patients. Our results provided a preliminary yet comprehensive documentation of the relationships of HRV indices with clinical variables and polysomnographic parameters before and after adenotonsillectomy, which showed some novel and interesting findings. However, limitations should be addressed. First, the HRV results may have been affected by certain psychophysiological changes (e.g., anthropometrics, lifestyle factors, acute or chronic diseases) other than adenotonsillectomy. However, the use of standardized, full-night, in-laboratory protocols with moderate-to-substantial reproducibility in most HRV indices among OSA patients with stable severity reduces this concern. Second, approximately half of our subjects received both adenotonsillectomy and medical treatment, and the heterogeneity of care may have had a confounding effect. Nevertheless, these interdisciplinary treatments are closer to real-world care for OSA, and a greater variability in AHI changes may contribute to better generalizability of this study. Third, 3 months may not be long enough to show cardiovascular changes, and studies with a longer follow-up period are warranted for this young population. Finally, in this study, there was no evidence of direct mediations of HRV on the relationship between adenotonsillectomy and AHI or AHI on the relationship between adenotonsillectomy and HRV indices [14], and this may be due to difficulties in measuring HRV across various sleep stages. Among school-age children, excessive body movements and parasomnia [67] make the researchers need 2-min epochs to analyze HRV and choose sleep periods free of respiratory events and movement artifacts [17]. However, frequency-domain measurements, such as VLF power and LF/HF ratio, often require recording periods of at least 5 min [34]. Therefore, measuring HRV across various stages in our study population is challenging. Nevertheless, averages may not be sensitive enough to detect sleep stage-specific mediating effects. In future studies, ultra-short-term HRV measurements during different sleep stages should be conducted to accurately assess the impact of nocturnal HRV changes on the management of OSA.
## 5. Conclusion
In conclusion, we confirmed that analysis of electrocardiographic polysomnography signals is a reliable method to measure HRV over 3 months in children with OSA. Adenotonsillectomy either reduced AHI or sympathetic activity during sleep. Improved OSA-specific quality of life and reduced sleep VLF power serially mediated the relationship between the change in tonsil size and % change in AHI. These findings suggest that HRV measurement may help monitor the sleep quality status and the disease burden of childhood OSA and many other venues. Our preliminary results also support applications of wireless HRV measurements with high-fidelity psychophysiology acquisition using edge computing in the patient's natural sleeping environment to overcome the highly obtrusive effects of visiting the sleep laboratory [68]. This technology can potentially be a “platinum standard” of sleep studies instead of the traditional “gold standard” of in-laboratory polysomnography.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by Institutional Review Board of Chang Gung Medical Foundation, Taoyuan, Taiwan. Written informed consent for participation was not provided by the participants' legal guardians/next of kin because: the current study was based on a secondary analysis of existing data. We provided a copy of the approval by the Institutional Review Board of Chang Gung Medical Foundation (No. 202200882B0), which approved the waiver of the participants' consent.
## Author contributions
L-AL, H-HC, TK, and CY conceptualized and designed the research project. L-AL, H-HC, H-SH, C-YW, TK, and CY interpreted the data. L-AL, H-HC, and H-SH collected the data and wrote the initial manuscript. C-YW, L-PC, H-YL, T-JF, Y-SH, G-SL, AY, TK, and CY contributed to writing and editing the manuscript. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'The association between acylcarnitine and amino acids profile and metabolic
syndrome and its components in Iranian adults: Data from STEPs 2016'
authors:
- Hananeh Taghizadeh
- Solaleh Emamgholipour
- Shaghayegh Hosseinkhani
- Babak Arjmand
- Negar Rezaei
- Arezou Dilmaghani-Marand
- Erfan Ghasemi
- Nekoo Panahi
- Hojat Dehghanbanadaki
- Robabeh Ghodssi-Ghassemabadi
- Niloufar Najjar
- Mojgan Asadi
- Mohsen khoshniat
- Bagher Larijani
- Farideh Razi
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10008865
doi: 10.3389/fendo.2023.1058952
license: CC BY 4.0
---
# The association between acylcarnitine and amino acids profile and metabolic syndrome and its components in Iranian adults: Data from STEPs 2016
## Abstract
### Background
Evidence, albeit with conflicting results, has suggested that cardiometabolic risk factors, including obesity, type 2 diabetes (T2D), dyslipidemia, and hypertension, are highly associated with changes in metabolic signature, especially plasma amino acids and acylcarnitines levels. Here, we aimed to evaluate the association of circulating levels of amino acids and acylcarnitines with metabolic syndrome (MetS) and its components in Iranian adults.
### Methods
This cross-sectional study was performed on 1192 participants from the large–scale cross-sectional study of Surveillance of Risk Factors of non-communicable diseases (NCDs) in Iran (STEP 2016). The circulating levels of amino acids and acylcarnitines were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in individuals with MetS ($$n = 529$$) and without MetS ($$n = 663$$).
### Results
The higher plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asp to Asn were significantly associated with MetS, whereas lower circulating levels of Gly, Ser, His, Asn, and citrulline were significantly associated with MetS. As for plasma levels of free carnitine and acylcarnitines, higher levels of short-chain acylcarnitines (C2, C3, C4DC), free carnitine (C0), and long-chain acylcarnitines (C16, C18OH) were significantly associated with MetS. Principal component analysis (PCA) showed that factor 3 (Tyr, Leu, Val, Met, Trp, Phe, Thr) [OR:1.165, $95\%$ CI: 1.121-1.210, $P \leq 0.001$], factor 7 (C0, C3, C4) [OR:1.257, $95\%$ CI: 1.150-1.374, $P \leq 0.001$], factor 8 (Gly, Ser) [OR:0.718, $95\%$ CI: 0.651-0.793, $P \leq 0.001$], factor 9 (Ala, Pro, C4DC) [OR:1.883, $95\%$ CI: 1.669-2.124, $P \leq 0.001$], factor 10 (Glu, Asp, C18:2OH) [OR:1.132, $95\%$ CI: 1.032-1.242, $$P \leq 0.009$$], factor 11 (citrulline, ornithine) [OR:0.862, $95\%$ CI: 0.778-0.955, $$P \leq 0.004$$] and 13 (C18OH, C18:1 OH) [OR: 1.242, $95\%$ CI: 1.042-1.480, $$P \leq 0.016$$] were independently correlated with metabolic syndrome.
### Conclusion
Change in amino acid, and acylcarnitines profiles were seen in patients with MetS. Moreover, the alteration in the circulating levels of amino acids and acylcarnitines is along with an increase in MetS component number. It also seems that amino acid and acylcarnitines profiles can provide valuable information on evaluating and monitoring MetS risk. However, further studies are needed to establish this concept.
## Introduction
Metabolic syndrome (MetS) is a collection of interrelated cardiometabolic abnormalities, including central adiposity, hyperglycemia, hypertension, hypertriglyceridemia, and low level of high-density lipoprotein cholesterol (HDL-C). These features are strongly linked to the development of type 2 diabetes (T2D) and cardiovascular disease and increased mortality [1]. This condition is a global epidemic disorder affecting about 20-$25\%$ of adults worldwide [2]. When compared across regions, it was estimated that 32-$47.6\%$ of the Iranian population was afflicted with MetS [3].
Recently, tremendous efforts have been devoted to addressing the MetS pathogenesis; however, there is considerable uncertainty in this regard. Additionally, there is no optimal screening tool and treatment for this disease, necessitating the identification of novel target-based approaches for better diagnostic and treatment modalities [1]. Currently, the clinical relevance of metabolomics to identify a collection of biomarkers for the detection, prediction, and monitoring of MetS and its associated metabolic abnormalities comes into the center of interest (4–6).
Various studies albeit with conflicting results have suggested that cardiometabolic risk factors, including obesity, T2D, dyslipidemia, and, hypertension are highly associated with changes in metabolic signature, especially plasma amino acids and acylcarnitines levels (7–13).
The amino acid availability has been implicated in regulating intracellular signaling, hormonal secretion, and energy homeostasis. For example, the branched-chain amino acids (BCAAs; leucine, isoleucine, valine) play important roles in the regulation of insulin secretion, glucose, lipid metabolism, central nervous system control of food intake, and energy balance [14, 15].
Current evidence reports elevations in the profile of branched-chain amino acids (BCAAs; leucine, isoleucine, valine) and aromatic amino acids (AAAs; phenylalanine, tyrosine) in individuals with type 2 diabetes(T2D), insulin resistance, and obesity [4]. Moreover, BCAA, Phenylalanine (Phe), and tyrosine (Tyr) could predict the risk of T2D, MetS, and cardiovascular disorders before disease manifestation (16–18).
The change in acylcarnitine profile could reflect dysregulation of fatty acid metabolism and mitochondrial function and point toward the presence of fatty acid oxidation and organic acid metabolism disorders [10, 19]. Change in circulating levels of acylcarnitines has also been reported in patients with MetS [7, 20]. Additionally, alteration of acylcarnitine plasma levels, including tetradecenoylcarnitine (C14:1), tetradecadienylcarnitine (C14:2), octadecenoylcarnitine (C18:1), and malonylcarnitine/hydroxy butyryl carnitine (C3DC+C4OH) are linked to both T2D and prediabetes conditions [9].
There is growing attention to the analysis of metabolite profiles in the context of metabolic disorders [4, 21]. However, several studies, albeit with conflicting results, showed a change in the pattern of amino acids and acylcarnitine profile in patients with MetS. Moreover, the criteria used for MetS definitions and studied populations vary. Despite studies in other countries and ethnicities, no data has been reported to evaluate a correlation of amino acid and acylcarnitine profile with MetS in Iranian adults. Hence, we sought to assess the association of circulating levels of amino acids and acylcarnitines with MetS and its components in an attempt to identify candidate biomarkers for the risk of MetS.
## Study design and participants
This study was performed on participants ($$n = 1192$$) randomly selected from the large–scale cross-sectional study of Surveillance of Risk Factors of non-communicable diseases (NCDs) in Iran (STEP 2016), which has been well discussed elsewhere [22]. In brief, through a systematic cluster random sampling, proportional to the adult population of each province, STEP16 was planned to collect data on 31,050 Iranian subjects (3,105 clusters) aged ≥18 years living in urban and rural areas of 31 provinces of Iran in 2016.
All participants underwent measurement of anthropometric indices including height, weight, Body Mass Index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) by standard protocols which are consistent with WHO protocols. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured three times, and the average value of the second and third readings was used for analysis. After overnight fasting, blood samples were collected, and plasma was separated by centrifugation at 4°C. A portion of plasma was used for the measurement of clinical parameters including fasting plasma glucose (FPG), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), and total cholesterol, and another portion was stored at -70°ϲ until metabolomics analysis. Plasma levels of FPG, TG, and HDL-C were measured based on the standard procedure using the auto analyzer (Cobas C311, Roche Diagnostics).
According to the National Cholesterol Education Programme Adult Treatment Panel III (NCEP ATP III) definition [23], participants were classified into two groups; the MetS group and the non-MetS group. Participants were defined as having MetS if they had at least three of the following five items: 1) Waist circumference ≥ 102 cm in men and 88 cm in women; 2) FPG ≥100 mg/dl (or diagnosed diabetes); 3) TGs ≥150 mg/dl; 4) HDL-C <40 mg/dl in men and <50 mg/dl in women; and 5) SBP ≥130 mmHg and DBP ≥85 mmHg.
Ethical approval for the current study was obtained from the ethics committee of the Endocrine and metabolism research institute (IR.TUMS.EMRI.REC. 1395.00141). It should be noted that the objectives and study protocol were described for all eligible individuals and written informed consent was obtained from each participant before the study.
## Metabolomics analysis
Metabolites including 20 amino acids and 30 acylcarnitines were analyzed in the fasting plasma of participants by targeted approach. The full details of method development, validation, and preparation protocol have been completely described elsewhere (24–26). Briefly, samples were mixed with isotope-labeled internal standards and derivatized with butanoic-HCL. Instrumental analysis was conducted on Thermo Scientific Ultimate 3000 liquid chromatography system coupled with an AB SCIEX API 3200 Triple Quadrupole mass spectrometer with ESI positive ion mode. Finally, the data was processed using Multiquant 3.0.2 software.
## Statistical analysis
For comparison of the metabolite concentrations between the MetS group and the control one. Student’s t-test and Mann-Whitney were used as appropriate.
For the identification of metabolites statistically associated with MetS, binary logistics regression was used, where fitness for the model was checked by the Hosmer–Lemeshow goodness of fit model. A $95\%$ confidence interval and a value of less than 0.05 were used as statistically significant levels.
Factor (principal component) analysis was performed to determine the potential pattern of metabolites associated with MetS. Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests were used to evaluate the suitability for factor analysis. Varimax rotation was applied to facilitate their interpretation. According to the scree plot, only the components with eigenvalues greater than 1 were kept. Metabolites with a factor loading ≥0.4 were considered important for further analysis.
Statistical analysis was conducted using SPSS 26 (IBM Corp., Armonk, NY, USA) and Graph Pad Prism 9 (GraphPad Software, San. Diego, CA). There was no outlier deletion. Data were z-transformed and analyzed. The Benjamini-Hochberg false discovery rate (FDR) was calculated to adjust the P-value for multiple comparisons. A P-value less than 0.05 was considered statistically significant.
## Baseline characteristics of study population
A total of 1192 participants were enrolled in this study: 529 patients with MetS (202 men and 327 women) and 663 ones without MetS (368 men and 295 women). The demographic, anthropometric, and biochemical characteristics of all participants are summarized in Table 1. The mean age for MetS and non-MetS groups was 56.75 ± 11.48 and 55.69 ± 12.45 years, respectively. There was not any significant difference between patients and controls in terms of years of education. As expected, individuals with MetS had higher BMI, waist circumference, hip circumference, and a worse metabolic profile reflected by higher levels of FPG, TG, and total cholesterol, while lower levels of HDL-C were in comparison with the subjects without MetS.
**Table 1**
| N | Non-MetS group | MetS group | P-value |
| --- | --- | --- | --- |
| N | 663 | 529 | |
| Gender, n (%) | | | 0.000 |
| Women | 295 (44.49) | 327 (61.81) | |
| Men | 368 (55.51) | 202 (38.19) | |
| Education (year) | | | 0.281 |
| <1 year | 168 (25.34) | 147 (27.79) | |
| 1-7 year | 221 (33.33) | 189 (35.73) | |
| 8-12 year | 186 (28.05) | 139 (26.28) | |
| >12 year | 88 (13.27) | 54 (10.21) | |
| Age (year) | 55.69 ± 12.45 | 56.75 ± 11.48 | 0.133 |
| BMI (kg/m²) | 25.75 ± 4.61 | 30.16 ± 4.75 | <0.001 |
| SBP (mmHg) | 126.73 ± 19.78 | 138.65 ± 20.27 | <0.001 |
| DBP (mmHg) | 77.67 ± 10.88 | 83.88 ± 12.14 | <0.001 |
| FPG (mg/dL) | 93.97 ± 27.29 | 115.18 ± 44.30 | <0.001 |
| TG (mg/dL) | 104.34 ± 57.23 | 176.82 ± 121.96 | <0.001 |
| HDL-C (mg/dL) | 45.38 ± 11.76 | 36.02 ± 8.94 | <0.001 |
| Cholesterol (mg/dL) | 164.38 ± 34.67 | 172.05 ± 37.38 | <0.001 |
| WC (cm) | 89.79 ± 13.05 | 101.27 ± 10.42 | <0.001 |
| HC (cm) | 98.83 ± 11.17 | 106.78 ± 9.79 | <0.001 |
| Smoking | 125 (18.85) | 47(8.88) | <0.001 |
## Altered plasma amino acid and acylcarnitine levels in MetS
Binary logistic regression analysis of 50 metabolites; 20 amino acids and 30 acylcarnitines has been demonstrated in Supplementary Table 1. The higher plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asp to Asn were significantly associated with MetS, whereas lower circulating levels of Gly, Ser, His, Asn, and citrulline were significantly associated with MetS.
After age and sex adjustment, all the above-mentioned amino acids plus Met, Trp, and the ratio of Asp to Asn showed a statistically significant association with MetS.
As for plasma levels of free carnitine (C0) and acylcarnitines, higher levels of C0, short-chain acylcarnitines (C2, C3, C4DC), and long-chain acylcarnitines (C16, C18OH) were significantly associated with MetS. However, the lower plasma level of C4 was significantly lower in the MetS group than in the non-MetS group. Multivariate analysis showed that circulating levels of C0 and acylcarnitines including C3, C4DC, C5, C16, and C18OH were associated with MetS independent of age and sex as covariates.
The univariate analysis of 50 metabolites; 20 amino acids and 30 acylcarnitines between the MetS group and non-MetS group has been demonstrated in Supplementary Table 2. Our data showed that plasma levels of C0, short-chain acylcarnitines (C0, C2, C3, C4DC), and long-chain acylcarnitines (C16, C18OH) were significantly higher in patients with MetS in comparison to those in the non-MetS group. However, the plasma level of C4 was significantly lower in the MetS group than in the non-MetS group.
The Plasma levels of branched-chain amino acids (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, and the ratio of Asn to Asp were all significantly higher in the MetS group compared to those in the non-MetS group. However, circulating levels of Gly, Ser, His, Asn, and citrulline were significantly lower in the MetS group compared to those in the non-MetS group.
Given the statistically significant difference in the number of men and women in the two study groups, possible changes in the plasma level of amino acids and acylcarnitines were also calculated depending on gender (Supplementary Table 3).
According to sex differences, the plasma level of C0, short-chain acylcarnitines (C3, C3DC, C4DC, C5, C5OH, and C5DC), medium-chain acylcarnitines (C8, C10, C10:1, and C12) and long-chain acylcarnitines (C14, C14:2, C14 OH, C16, C16 OH, C16:1OH, and C18) were significantly higher in men in comparison to those in women in MetS group. A similar pattern was also found when comparing men and women in the non-MetS group.
As for amino acids, except for Ala, Arg, Thr, Ser, His, and Lys all amino acids were significantly higher in men in comparison to those in women in both groups. However, *Arg plasma* level was significantly higher in men in comparison to those in women in the MetS group.
## Altered circulating levels of amino acid and acylcarnitine profile with MetS components
The association of 19 amino acids and acylcarnitines (that were shown to be significantly different in MetS and non- MetS groups) with MetS components was shown in Table 2. All participants were categorized into four groups based on the number of MetS components (increased waist circumference, hypertension, hyperglycemia, hypertriglyceridemia, and decreased HDL-C concentration). These groups include ones who didn’t have any MetS component (0-component group), ones having only one MetS component (1-component group), ones with two MetS components (2-component group), and ones who had between 3 and 5 MetS components (3-5-component group).
**Table 2**
| Metabolites (µmol/L) | 0 component | 1 component | 2 components | 3-5 components |
| --- | --- | --- | --- | --- |
| Metabolites (µmol/L) | n= 152 | n= 228 | n= 283 | n= 529 |
| C0 | 52.742 ± 1.019 | 55.975 ± 0.824** | 55.711 ± 0.762** | 58.121 ± 0.577*** |
| C2 | 14.585 ± 0.432 | 14.552 ± 0.349 | 13.999 ± 0.232 | 14.872 ± 0.187 |
| C3 | 0.732 ± 0.021 | 0.845 ± 0.022*** | 0.875 ± 0.022*** | 0.940 ± 0.018*** |
| C4 | 0.488 ± 0.041 | 0.543 ± 0.037 | 0.504 ± 0.033 | 0.510 ± 0.019** |
| C4DC | 0.064 ± 0.002 | 0.065 ± 0.002 | 0.071 ± 0.002 | 0.080 ± 0.002*** |
| C16 | 0.184 ± 0.005 | 0.180 ± 0.003 | 0.183 ± 0.004 | 0.190 ± 0.002 |
| C18OH | 0.009 ± 0.000 | 0.009 ± 0.000 | 0.008 ± 0.000 | 0.010 ± 0.000 |
| Alanine | 360.929 ± 6.661 | 385.890 ± 6.327** | 404.722 ± 5.154*** | 452.150 ± 4.135*** |
| Glutamic Acid | 66.334 ± 1.803 | 65.979 ± 0.827 | 67.902 ± 0.822* | 69.274 ± 0.584*** |
| Leucine | 116.537 ± 1.790 | 117.724 ± 1.625 | 125.256 ± 1.676** | 131.710 ± 1.169*** |
| Phenylalanine | 61.281 ± 0.911 | 63.150 ± 0.803 | 64.408 ± 0.667** | 66.369 ± 0.643*** |
| Tyrosine | 65.275 ± 1.034 | 68.478 ± 0.863** | 70.986 ± 0.875*** | 74.753 ± 0.653*** |
| Valine | 237.840 ± 3.288 | 242.690 ± 3.053 | 262.019 ± 3.171*** | 277.448 ± 2.321*** |
| Citrulline | 41.686 ± 0.818 | 40.265 ± 0.688 | 38.520 ± 0.632** | 36.930 ± 0.437*** |
| Glycine | 278.421 ± 5.993 | 289.125 ± 5.625 | 262.474 ± 4.836** | 257.256 ± 3.335*** |
| Proline | 233.227 ± 5.735 | 241.445 ± 5.529 | 256.642 ± 5.167** | 264.772 ± 3.665*** |
| Serine | 111.587 ± 2.328 | 110.675 ± 2.047 | 103.309 ± 1.660** | 97.003 ± 1.196*** |
| Histidine | 84.677 ± 1.227 | 84.979 ± 1.121 | 84.378 ± 0.959 | 81.901 ± 0.777 |
| Asparagine | 52.099 ± 1.627 | 50.258 ± 1.322 | 47.591 ± 1.208* | 44.490 ± 0.790*** |
We found an increasing trend regarding plasma levels of amino acids; Ala, Tyr, free carnitine, and acylcarnitines; C3 as the number of components increased. The 2-component group and 3-5- component group had a higher level of Glu, Leu, Phe, Val, Citrulline, Gly, Pro, Ser, and Asn in comparison with the 0-component group. The other acylcarnitines and amino acids were not associated with MetS components.
## Amino acid profiles extracted by PCA were associated with MetS and its components
Spearman’s correlation coefficient analysis was done to evaluate the correlation of the 12 amino acids and 7 acylcarnitines with the metabolic characteristics linked to MetS (Figure 1). As shown in Figure 1, plasma levels of Leu, Val, and Phe have a moderate but significant correlation with FPG and TG levels. Moreover, C4DC was significantly associated with FPG levels.
**Figure 1:** *Pearson’s correlation coefficients were calculated for the 12 amino acids and 7 acylcarnitines with metabolic-related variables. BMI, body mass index; WC, waist circumference; HC, hip circumference, FPG, fasting plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL-c, high-density lipoprotein-cholesterol; Leu, leucine; Val, valine; Tyr, tyrosine; Phe, phenylalanine; Glu, glutamic acid; Ala, alanine; Gly, glycine; Cit, citrulline, His, histidine; Asn, asparagine; Pro, proline. All statistically significant associations were marked with a multiplication sign. Statistical differences are shown as ×P < 0.05.*
Kaiser-Meyer-Olkin (KMO) test was used to assess the suitability of our data for Factor Analysis. Here, the KMO value was 0.858 which indicates the sampling is adequate. Moreover, Bartlett’s test of Sphericity was significant (P-value < 0.001). Factors 1-13 had eigenvalues of more than 1 and explained $73.38\%$ of the cumulative variance that has been depicted by using a scree plot (Supplementary Figure 1). So, we extracted 13 factors through Principal Component Analysis (PCA) and the loadings after varimax rotation and Kaiser Normalization were listed in Supplementary Table 4. Only metabolites with loading ≥ 0.4 are included in the factors (Table 3).
**Table 3**
| PC1 | Loading | PC3 | Loading.1 | PC6 | Loading.2 | PC11 | Loading.3 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| C16:1OH | 0.825 | Tyr | 0.799 | C4OH | 0.694 | Cit | 0.787 |
| C14 | 0.819 | Leu | 0.776 | C8:1 | 0.688 | Orn | 0.659 |
| C16 | 0.774 | Val | 0.758 | C2 | 0.641 | PC12 | Loading |
| C18:1 | 0.751 | Met | 0.740 | PC7 | Loading | Arg | 0.709 |
| C18 | 0.733 | Trp | 0.712 | C0 | 0.698 | PC13 | Loading |
| C14OH | 0.708 | Phe | 0.616 | C3 | 0.675 | C18:1OH | 0.827 |
| C16OH | 0.680 | Thr | 0.429 | C4 | 0.527 | C18OH | 0.599 |
| C12 | 0.591 | PC4 | Loading | PC8 | Loading | | |
| C16:1 | 0.679 | Lys | 0.906 | Gly | 0.815 | | |
| C14:1 | 0.653 | Glu | 0.894 | Ser | 0.667 | | |
| C14:2 | 0.424 | Asp | 0.711 | PC9 | Loading | | |
| PC2 | Loading | His | 0.683 | Ala | 0.624 | | |
| C8 | 0.946 | PC5 | Loading | Pro | 0.565 | | |
| C10 | 0.939 | C5DC | 0.450 | C4DC | 0.468 | | |
| C10:1 | 0.919 | C5:1 | 0.873 | PC10 | Loading | | |
| C6 | 0.801 | C5OH | 0.762 | Glu | 0.726 | | |
| | | C3DC | 0.577 | C18:2OH | 0.555 | | |
| | | C5 | 0.559 | Asp | 0.549 | | |
The results of age and sex-adjusted odds ratio (OR) for the association between metabolites with VIP score values above 1.0 and metabolic syndrome (MetS) were shown in Table 4. After adjustment for age and sex, factor 3 (Tyr, Leu, Val, Met, Trp, Phe, Thr), factor 7 (C0, C3, C4), factor 8 (Gly, Ser), factor 9 (Ala, Pro, C4DC), factor 10 (Glu, Asp, C18:2OH), factor 11 (citrulline, ornithine) and 13 (C18OH, C18:1 OH) were independently correlated with metabolic syndrome. Factor 3 (OR:1.165, $95\%$ CI: 1.121-1.210, $P \leq 0.001$), 7 (OR:1.257, $95\%$ CI: 1.150-1.374, $P \leq 0.001$), 9 (OR:1.883, $95\%$ CI: 1.669-2.124, $P \leq 0.001$), 10 (OR:1.132, $95\%$ CI: 1.032-1.242, $$P \leq 0.009$$) and 13 (OR: 1.242, $95\%$ CI: 1.042-1.480, $$P \leq 0.016$$) positively and factors 8 (OR:0.718, $95\%$ CI: 0.651-0.793, $P \leq 0.001$) and 11 (OR:0.862, $95\%$ CI: 0.778-0.955, $$P \leq 0.004$$) were negatively associated with MetS.
**Table 4**
| Unnamed: 0 | Crude model | Crude model.1 | Crude model.2 | Adjusted for age and sex | Adjusted for age and sex.1 | Adjusted for age and sex.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Factors | OR | 95% CI | P-value | OR | 95% CI | P-value |
| 1 | 1.002 | (0.983-1.021) | 0.866 | 1.004 | (0.984-1.025) | 0.686 |
| 2 | 1.000 | (0.967-1.034) | 0.993 | 1.010 | (0.976-1.045) | 0.583 |
| 3 | 1.103 | (1.067-1.141) | 0.000 | 1.165 | (1.121-1.210) | 0.000 |
| 4 | 0.945 | (0.905-0.986) | 0.010 | 0.959 | (0.918-1.002) | 0.059 |
| 5 | 1.006 | (0.961-1.053) | 0.806 | 1.035 | (0.986-1.086) | 0.166 |
| 6 | 1.076 | (1.004-1.154) | 0.039 | 1.058 | (0.984-1.136) | 0.126 |
| 7 | 1.201 | (1.103-1.306) | 0.000 | 1.257 | (1.150-1.374) | 0.000 |
| 8 | 0.757 | (0.688-0.833) | 0.000 | 0.718 | (0.651-0.793) | 0.000 |
| 9 | 1.750 | (1.562-1.960) | 0.000 | 1.883 | (1.669-2.124) | 0.000 |
| 10 | 1.065 | (0.976-1.162) | 0.158 | 1.132 | (1.032-1.242) | 0.009 |
| 11 | 0.832 | (0.756-0.915) | 0.000 | 0.862 | (0.778-0.955) | 0.004 |
| 12 | 1.009 | (0.859-1.186) | 0.911 | 1.070 | (0.907-1.263) | 0.422 |
| 13 | 1.230 | (1.042-1.451) | 0.014 | 1.242 | (1.042-1.480) | 0.016 |
## Discussion
Recently, metabolomics studies have opened new insights into the identification of biomarkers for the diagnosis, monitoring, and risk prediction of metabolic disorders [7, 20, 27, 28]. However, available evidence on the alteration of metabolites especially amino acids, and acylcarnitines in the context of MetS is little and conflicting [13, 20, 29], which warrants further exploration.
As revealed in the current study, two distinct amino acid patterns albeit with an opposing direction are significantly associated with MetS. The first one comprises elevated BCAAs (Val, Leu), aromatic amino acids (Phe, Tyr), Pro, Ala, Glu, Trp, Met, and the ratio of Asp to Asn, while the second one consists of decreased circulating levels of Gly, Ser, His, Asn, and citrulline.
Each of the two distinct amino acid patterns is mostly comprised of chemically and functionally correlated metabolites. The first pattern is composed of amino acids that their high levels are linked to impaired energy metabolism, dysregulation of insulin signaling, and the development of adverse cardiometabolic outcomes. To support this concept, data from in vitro studies and animal models revealed that Leu acts as a potential nutritional signal and shares metabolic regulatory function with insulin. Specifically, studies showed that leucine infusion inhibits insulin-stimulated glucose uptake, leading to desensitization of insulin signaling. Moreover, amino acids such as Ala, Val, or glutamine can augment glucose production and consequently induce hyperglycemia. Val also increased the free fatty acid uptake in serum, leading to the accumulation of neutral lipids and ATP production in the early stages of liver regeneration following resection [14, 15, 30]. Additionally, dietary supplementation with aromatic amino acids has the potential to ameliorate hepatic steatosis by stimulating bile acid synthesis in mice [31]. Accumulating evidence showed that methionine restriction has favorable effects on health since it leads to improvement of insulin signaling, elevated energy expenditure, and decreased oxidative damage and inflammation in the context of metabolic disorders. Moreover, hyperprolinemia is linked to impaired insulin secretion and dysregulated glucose homeostasis (32–34).
The second pattern is composed of amino acids that their decline is involved in the pathogenesis of metabolic disorders, particularly glucose intolerance, insulin resistance, and T2DM, albeit with conflicting findings. For instance, metabolic benefits mediated by glycine and histidine include the inhibition of oxidative stress and inflammation, and an inhibitory effect on gluconeogenesis and food intake Glycine also exerts positive effects on mitochondrial activity, detoxification processes, and regulation of hormones involved in glucose homeostasis [35, 36].
In agreement with the above-mentioned information, plasma levels of Leu, Val, and Phe have a moderate but significant correlation with FPG and TG levels; both are highly linked to the development of adverse cardiometabolic outcomes.
Although the current study cannot address the underlying mechanism linking altered amino acid profile to MetS components, our findings regarding the positive correlation of Leu, Val, and Phe with hypertriglyceridemia and hyperglycemia can be attributed to the functions of branched-chain amino acids as biological regulators of lipid and glucose metabolism and insulin signaling [14, 15].
In line with our data, Sun et al. showed increased levels of BCAAs, aromatic amino acids, Pro, Ala, Met, and Glu were linked to an elevated risk of MetS and its components in a Chinese Han population. They showed that Leu, Val, and Phe were positively associated with the concentration of TG and 2-h postprandial glucose [29]. Several cross-sectional and prospective cohort studies also point toward the strong association between plasma levels of BCAA and aromatic amino acids and central obesity, T2D, and insulin resistance (11, 18, 37–39).
Okekunle et al. found a positive correlation between TG concentration and Leu, Val, and Phe in patients with obesity, T2D, and MetS. Compared to healthy individuals, a similar pattern was found in patients with multiple metabolic disorders; Val, Glu, Pro, and Ile were concomitantly elevated, while Gly was significantly decreased in multiple metabolic disorders [12].
An increased concentration of amino acids related to glutamate, alanine, and aromatic amino acid metabolism, but a lower level of glycine-serine-threonine metabolism-related amino acids in patients with Mets following a lifestyle modification program are other examples supporting altered amino acid profile in the context of MetS [40].
As for circulating levels of free carnitine and acylcarnitines, we found that only one distinct pattern including elevated free carnitine (C0), short-chain acylcarnitines (C3, C4DC, C5), and long-chain acylcarnitines (C16, C18OH) is associated with MetS. Among the above-mentioned acylcarnitines, C4DC was significantly associated with FPG levels.
In parallel, Libert et al. assigned adults ($$n = 90$$) to a spectrum of metabolic wellness groups based on BMI and ATP III criteria for MetS: (i) lean metabolically well; (ii) obese metabolically well; (iii) obese metabolically unwell; and (iv) obese metabolically unwell with T2D. The results showed that patients who completely meet the ATP III criteria for MetS showed a higher level of both C3 carnitine and the ratio of C3 and C5 to total acylcarnitines in comparison with ones with a metabolically healthy status [20].
C3 acylcarnitine is formed from Val and Ile after interaction with branched-chain α-keto acid dehydrogenase (BCKD). However, C5 acylcarnitine can be produced from the breakdown of Iso and Leu before metabolism by BCKD [21]. Hence, C5 and C3 acylcarnitine levels may reflect BCKD activity in the context of MetS. Specifically, the possible impairment of BCKD in MetS in a mechanism dependent on pathway rerouting can lead to the elevation of C5 concentration, while it can reduce C3 levels through upstream suppression [4]. However, Libert’s study suggests that acylcarnitine can be formed both upstream and downstream of BCKD [20]. In another study, Bene et al. observed higher levels of C3 and C4 acylcarnitines but lower levels of most of the medium-chain and long-chain acylcarnitine levels in patients with MetS and the patients with diabetes mellitus as compared to controls [7]. It is well-established that BCAAs are generally increased in MetS, hence, it is tempting to speculate that an increased level of C3 acylcarnitine observed here is linked to elevated BCAA levels in the patients with Mets [4]. However, the role of other pathways leading to C3 acylcarnitine formation cannot be ignored here.
Wolf et al. showed an elevated level of medium-chain acylcarnitines in individuals with high fasting respiratory quotient (RQ) as a risk factor for metabolic syndrome [41]. RQ reflects the mix of fat and carbohydrates being oxidized [42]. Hence, our data regarding an elevated level of medium-chain acylcarnitines indicates a reduced fatty acid oxidation capacity and a higher rate of incomplete fatty acid oxidation in patients with MetS.
Free carnitine facilitates the transport of activated fatty acids across the mitochondrial membrane and delivers them for β-oxidation to produce energy. In addition, it enhances acetyl and acyl group efflux out of the mitochondria into the cytosol as acylcarnitines. In line with our data, others reported higher free carnitine in patients with newly diagnosed T2D patients and individuals with obesity in comparison with controls [43, 44].
The finding of higher free carnitine in MetS is not unexpected and several possibilities derived from the pieces of literature can justify this finding. First, insulin resistance and metabolic disorders such as obesity and T2D are characterized by incomplete fatty acid oxidation that elevates acylcarnitine concentrations [44]. Hence, it can be speculated that an increased concentration of free carnitine in the context of MetS is an endogenous effort to facilitate the transesterification of activated long-chain acyl-CoA to its acylcarnitine before entry into mitochondria. Another explanation may lie in the fact that free carnitine can induce oxidation of the branched-chain amino acids in several tissues and regulate ketogenesis by interaction with branched-chain acyl-CoA esters and pyruvate [45]. Due to the increased plasma level of BCAA in our study, we can speculate that higher levels of free carnitine in patients with MetS might be a mechanism that facilitates BCAA oxidation in MetS.
However, several studies reported inconsistent data. For instance, Bene et al. reported a significantly lower level of free carnitine in T1D patients while no differences were found in the free carnitine concentrations between T2D and MetS patients and the controls [7]. In another study, there was a trend of free carnitine levels being lower in patients with already-diagnosed diabetes mellitus as compared with controls [20].
This discrepancy stems from the complicated nature of the pathogenesis of metabolic disorders [1] as well as different degrees of involvement of impaired mitochondrial function and incomplete long-chain fatty acid oxidation pathways in the MetS [43].
According to sex differences, the plasma level of C0, some short-chain acylcarnitines, medium-chain acylcarnitines, and long-chain acylcarnitines were significantly higher in men in comparison with those in women in both study groups. As for amino acids, nearly all amino acids were significantly higher in men in comparison to those in women in both groups. These findings were in line with several studies conducted on other metabolic disorders [21, 46, 47]. Specifically, it has been suggested that amino acids especially BCAA may be regulated differently by sex. Moreover, a higher concentration of some metabolites in men rather than in women can be attributed to larger muscle mass in men. However, the important role of sex steroids and their precursors in modulating carnitine turnover cannot be ignored when we interpret data on acylcarnitine levels (45, 48–50). Along with other studies, the aforementioned data can strengthen this concept that there is a sex-specific difference in the association between metabolites and MetS. However, more studies are needed to confirm this issue.
The main strength of our study is that metabolomics analysis was conducted on a large number of study participants who were randomly selected from a multi-regional cohort in Iran.
Another strength worth pointing out is that all anthropometric data and biochemical parameters were measured using standardized methods and rigorous statistical methodology was applied for data analysis. This ensures adequate statistical power for the generalization of the above findings at least in Iran.
To put these findings together, our data along with others can open new avenues for exploring potential biomarkers for clinical screening, diagnosis, and treatment. However, the present study has several limitations that merit consideration. First of all, changes in levels of several amino acids and acylcarnitines were found in patients with MetS in a study with a cross-sectional design which limits us to make conclusions about the contributory nature of metabolic changes in the progression of these conditions. Therefore, prospective cohort studies are warranted to replicate our findings in the future. More importantly, while we analyzed a large number of metabolites, the targeted nature of the metabolomics analysis mainly hinders the discovery of analytes that may be of importance for risk assessment while those were not analyzed.
In conclusion, our study showed that elevated plasma levels of some acylcarnitine and amino acid metabolites including Tyr, Leu, Val, Met, Trp, Phe, Thr, C0, C3, C4, Gly, Ser, Ala, Pro, C4DC, Glu, Asp, C18:2OH, citrulline, ornithine, C18OH, C18:1 OH were associated with MetS risk in Iranian adults with MetS. The present study further strengthens the existence of various mechanisms being responsible for MetS derangements. However, additional population cohorts should be undertaken to replicate our conclusion.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
Ethical approval for the current study was obtained from the ethics committee of the Endocrine and metabolism research institute (IR.TUMS.EMRI.REC. 1395.00141). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
BL, FR, SE, and MK contributed to the study conception and design. BA EG, and HT provided study patients and monitored data and specimen collection. NN, AD-M, and SH performed the experiments. RG-G, SE, and SH analyzed the data. HT and HD wrote the manuscript. SE, MA, and NR edited the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1058952/full#supplementary-material
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|
---
title: '“I was bullied for being fat in every situation, in every outfit, at every
celebration”: A qualitative exploratory study on experiences of weight-based oppression
in Qatar'
authors:
- Lily O'Hara
- Bayan Alajaimi
- Bayan Alshowaikh
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10008867
doi: 10.3389/fpubh.2023.1015181
license: CC BY 4.0
---
# “I was bullied for being fat in every situation, in every outfit, at every celebration”: A qualitative exploratory study on experiences of weight-based oppression in Qatar
## Abstract
### Introduction
Weight-based oppression (WBO) has been documented as a widespread phenomenon in Western countries and is associated with a range of psychological, physiological, and behavioral harms. Research on weight-based oppression is largely absent from the Arab region.
### Methods
We conducted a qualitative exploratory study using semi-structured in-depth interviews to examine the internalized attitudes, values, and beliefs related to body weight, and experiences of external weight-based oppression of 29 staff, faculty, and students at Qatar University.
### Results
Thematic analysis revealed six major themes on the characteristics of internalized WBO, and the nature, timing, source, extent, and impact of external WBO. WBO was regarded as so common in the Arab culture as to be normative, with damaging exposure to WBO beginning in early childhood.
### Conclusion
WBO in the Arab region is an important and unrecognized public health issue. Programs to reduce WBO should be developed in all sectors.
## 1. Introduction
Weight-based oppression (WBO), including negative beliefs, teasing, harassment, stigma, prejudice, and discrimination based on body weight, is a widespread phenomenon that leads to considerable distress, health harming behaviors and poor health outcomes [1]. WBO arises from both external and internal sources. External sources of WBO include exposure to stigmatizing or exclusionary social, cultural, economic, political, and built environments, weight bias and discrimination, and weight-based bullying and violence. In Western countries, the prevalence of weight stigma is high, and appears to be increasing [2]. Up to a half of young people in the USA have been subjected to weight-based harassment, the highest rate of any type of harassment, and similar to or greater than rates of ethnicity-based harassment [3, 4]. Up to one third of young people have been subjected to weight based discrimination [4]. Weight-based harassment is prevalent across genders, with up to $65\%$ of non-binary and transgender youth experiencing weight-based victimization [4]. Those with higher weights, women, transgender, non-binary, queer, and younger people are subjected to the highest levels of weight-based discrimination (4–6). Weight bias has been demonstrated to exist across all aspects of society including in health care practitioners, educators, employers, landlords, and the general public [7].
WBO from external sources is linked with decreased body satisfaction, lower self-esteem, greater weight concerns, more loneliness, higher depressive symptoms, suicidal thoughts and attempts, higher preference for sedentary activities or activities performed alone, and bulimic behavior, regardless of actual body weight [8]. Mental health issues such as depression are associated with being exposed to weight-based teasing, bullying and stigmatization. These associations are apparent across gender, ethnic, racial, and weight groups [9]. The more sources of teasing that people are exposed to, the greater the prevalence of emotional health issues. In one study, participants that were exposed to teasing from their peers had considerably higher levels of depression and were five times more likely to adopt health harming weight manipulation behaviors compared to those who were not exposed to teasing [10]. The frequency of teasing and the number of teasing sources significantly increased the risk of depression. Weight-based teasing strongly predicts binge eating and intensive weight manipulation over 5 years [11]. Negative comments from parents about weight or shape and eating are associated with psychological distress and eating disorder cognitions in adolescents [12]. A systematic review found an association between weight teasing by parents and problematic eating behaviors in adolescents [13].
Internalized WBO is the negative attitudes, values, and beliefs people hold about one's own weight [14], which have negative consequences for health and wellbeing [15]. Internalized negative attitudes about body weight are so strong that being fat is considered worse than having breast cancer [16], and a proportion of people would rather lose a limb, be blind, alcoholic, severely depressed, unable to have children, or lose 10 years of life or more than be fat [17]. Internalized WBO is linked to low self-esteem, anxiety, depression, avoidance of physical activity, body image disturbance, decreased use of preventive health services, increased calorie consumption, disordered eating, and weight gain (18–21). Evidence is mounting of the psychological (22–27), behavioral (21, 25, 28–32) and physiological effects of WBO. Physiological effects include higher blood pressure [33, 34], type 2 diabetes mellitus [35], metabolic syndrome [36, 37], allostatic load (lipid/metabolic dysregulation, glucose metabolism and inflammation) [38], cortisol reactivity [39], and oxidative stress [40].
Research on external WBO is largely absent from the Arab world, including Qatar. The only study to address any aspect of external WBO found that $44\%$ of female Emirati students reported being frequently teased about their weight, and that eating disorder symptomatology was positively correlated with being bothered by weight-based teasing and internalized weight stigma [14]. Although there have been numerous studies in the Arab region on internalized WBO, they have tended to focus exclusively on body dissatisfaction and disordered eating attitudes [for recent examples see (41–45)]. A recent review of 22 studies involving over 10,000 adolescents from nine Arab countries found that the overall prevalence of disordered eating attitudes was $26.94\%$, a higher prevalence than in the USA or sub-Saharan Africa [46].
The “global culture of modernity” [47] has come to characterize many rapidly urbanizing parts of the world, including the Arab region, and is viewed as eliciting a rise in average body weight, weight consciousness, and disordered eating. Gordon [48] reviewed epidemiological data for nations where eating disorders first began being reported in the 1990s. He identified four pivotal characteristics that these nations had in common: [1] rising average body weights, [2] highly developed economies or rapid economic change, [3] changing and conflicting gender roles for women, and [4] a global consumer culture with an emphasis on slenderness as a female body ideal. All four of Gordon's factors resonate strongly with Qatar's rapid socio-economic and epidemiological transition. Rates of WBO may therefore be rising concomitantly, with the attendant poor physical, mental, and social health outcomes, including chronic non-communicable diseases such as cardiovascular disease and diabetes. However, no studies have examined any of the concepts related to internalized or external WBO in Qatar. This study aims to examine experiences of external WBO, including teasing, bullying, stigmatization, and discrimination, and the internalized attitudes, values, and beliefs related to body weight in a sample of people in Qatar.
Given the significant body of evidence demonstrating the relationship between population changes in body weight, WBO, and negative health outcomes, it is imperative that research studies begin to explore the full scope of WBO in Qatar and the Arab region. This qualitative study is the first to do so and will provide the foundation for future quantitative studies to examine the extent and impact of these issues in the population more broadly.
## 2.1. Research questions
The research questions we explored were 1. What are the internalized WBO related attitudes, values, and beliefs of people in Qatar? and 2. What are the external WBO experiences of people in Qatar?
## 2.2. Methodology
Constructivist epistemology guided this research study, based on the belief that “reality” is socially constructed [49]. The consolidated criteria for reporting qualitative research (COREQ) guidelines are used to report the study design and findings [50]. This qualitative study used interview methodology [51], which was most appropriate to explore the range of experiences of participants on this issue. Interview methodology provides a deeper and richer understanding of public health issues than purely quantitative methods and is most appropriate where little is already known about the issue, where the issue is sensitive, or where detailed insights are required from individual participants. All these conditions were applicable to exploring WBO in Qatar.
## 2.3. Theoretical framework
The Red Lotus Critical Health Promotion Model (RLCHPM) was used as a theoretical framework for this study [52]. The RLCHPM is a modern, holistic, socio-ecological model, that differs from other health promotion models in terms of incorporating a system of values and principles, and applying them in all stages of critical health promotion including community assessment, planning, and implementation, and evaluation (52–54). In the RLCHPM, the pod of the lotus flower represents the holistic health and wellbeing status of people. The stamens of the lotus flower that surround the pod represent the determinants of health and wellbeing related to people's characteristics, including biological, cognitive, affective, and socioeconomic factors and behaviors. The first layer of petals of the lotus flower represents the environmental determinants of health including social, cultural, political, economic, commercial, natural, and built environments. The second layer of lotus flower petals represents the components of the community assessment process. The third, fourth and fifth layers of lotus flower petals represent the components of health promotion program planning, program implementation and program evaluation processes, respectively. The leaves of the lotus plant represent a focus on sustainability. The stem of the plant represents the process of critical reflection. The tuber and roots are the foundation of the plant and represent the values and principles of critical health promotion, including social justice and equity, holistic, salutogenic, and ecosystems conceptions of health and scientific endeavor, allyship and empowerment, beneficence and non-maleficence, and evidence-informed and theory-based practice that are applied across all components of the model. The lotus plant is a dynamic, living organism that exists within a complex ecosystem. All parts of the plant and its environment are interconnected and influence each other (52–54).
This study aimed to investigate WBO as a specific health and wellbeing issue. Understanding the nature and extent of health and wellbeing issues is part of the community assessment phase of the health promotion practice cycle. Using the RLCHPM as the theoretical foundation meant that the study prioritized the participation of people with higher weight (value: priority populations determined by structural inequality), and considered WBO holistically, which meant we were open to the possibility that WBO may have had physical, mental, spiritual, and social health consequences (value: holistic health paradigm), though we did not specifically probe for each aspect. Using the RLCHPM also meant that the study investigated the characteristics of people (stamens) and environments (first petal layer) that contribute to WBO, and how these factors interact and operate at multiple levels from the individual level to the family, community, organization, and society levels (value: systems science).
## 2.4. Research team and reflexivity
The study team (LOH, BAA, BAS) engaged in reflexive practice [55] at weekly meetings, focusing on issues such as adherence to critical health promotion values and principles, as well as quality considerations. This allowed us to examine our own beliefs and assumptions, and think carefully about how these may be influencing our research process, including the risk of prioritizing our own views or opinions. The epistemological position of constructivism meant that the design of the study, data collection and analysis, and interpretation of the findings were all constructed by the researchers and influenced by our personal and professional experiences. LOH is a health promotion academic and practitioner and fat liberation advocate who has been involved in critical fat studies and fat activism in schools, universities, and the community for over 20 years. LOH and BAA have lived experience of external and internalized weight-based oppression, while BAS has witnessed such experiences with family and friends. BAA and BAS were senior undergraduate public health students at the time of the study and became interested in weight-based oppression through interaction and classes with LOH.
## 2.5. Study participants
Participants were recruited from the staff, faculty, and students at Qatar University (QU). Participation was limited to the QU community as this was an exploratory study. The researchers had no significant relationships with most of the participants prior to the study. In the first recruitment phase, participants were known to the researchers as colleagues in other departments or fellow students from other programs. One participant recruited in the first phase was well known to the researchers as a former student. This participant was eager to participate in the study, despite this existing relationship. In the second recruitment phase, the researchers had no relationships with any of the participants prior to the study.
## 2.6. Recruitment and sampling methods
A combination of homogenous and heterogeneous purposive sampling methods were used. All participants shared the common feature of having been exposed to WBO. Following this initial inclusion criterion, heterogeneous sampling was used to maximize variability within the participants. Purposive sampling is one of the most cost-effective and time-effective sampling methods available and is appropriate for studies such as this where the discovery of meaning will benefit from an intuitive approach. In the first phase of recruitment, potential participants with larger bodies were identified through personal knowledge of the researchers. They were informed about the study, and shown pictures illustrating types of WBO including teasing, and discrimination. We asked if they had experienced anything similar, and if so, would they be interested in participating in the study. This method resulted in nine interviews that were conducted in-person. We had not reached data saturation at that point, as new information was emerging with each interview [56], and so we decided to try a new recruitment strategy. Email and QU social media were used to disseminate a poster calling for QU students, staff, and faculty to participate in the study. The poster had the title “Ever been treated badly because of your weight?” and the text read “Teasing, harassment, stigma, and discrimination based on body weight are widespread and cause considerable distress. We are conducting a study exploring how people with a higher body weight are treated by their families, friends, teachers, healthcare providers, the media, and society in general. The study is the first study in the Arab region to explore these issues.” The poster included a cartoon image of a child with a larger body being teased by a group of children. After screening respondents, those that added the most heterogeneity to the sample were contacted and recruited to participate. This recruitment and sampling method resulted in a further 20 completed interviews, at which point data saturation was deemed to have occurred, as no new concepts were appearing in the data [56, 57]. Four people initially volunteered to participate in the second recruitment phase but did not respond to requests from the researchers to schedule an interview. The total sample size was therefore 29 participants. This is significantly larger than the range of nine to 17 participants generally required to reach data saturation in studies using qualitative interviews [56], due to the intentional heterogeneity of our sample.
## 2.7. Data collection method
Individual in-depth semi-structured interviews were used to collect the data via a set of predetermined but loosely structured questions. Semi-structured interviews enable the comparison of data across participants, but also provide the flexibility to probe or dig deeper on specific issues as appropriate in each individual interview. The first interview was conducted by LOH (MPH, PhD), a female Associate Professor of Public Health with experience in qualitative studies, with BAA and BAS as observers. All subsequent interviews were conducted by BAA and BAS, female senior undergraduate students majoring in public health and trained in research methods and interviewing techniques. The first nine interviews were conducted in person between January and early March 2020. With the participants' permission, interviews were audio-recorded, and field notes were made during and immediately after each interview, including the observations, thoughts, and ideas about the interview. Just after the second recruitment phase was implemented, the COVID-19 pandemic hit Qatar and all on-campus activities were suspended. As such, our interviews moved online and were conducted using WhatsApp, an end-to-end encrypted communication app that is used by almost all residents of Qatar. Depending on the choice of the participant, interviews were conducted digitally via WhatsApp video call, audio call, texting or voice notes, or a combination of texting and voice notes, and took place in March and April 2020. All in-person and digital interviews ranged from 30 to 90 min and were conducted in English and/or Arabic according to the participant's preference. Interviews records were transcribed immediately after the completion of the interview and the transcript was provided to the participant to allow for corrections or additions. Transcripts in Arabic were translated into English for data analysis by BAA and BAS, who are native Arabic speakers.
## 2.8. Data collection instrument
The interview protocol (list of questions) was developed in both English and Arabic, and pilot tested with several respondents to establish if the questions were clear and understandable, and to assess if respondents were willing to answer the questions openly and honestly. Changes were made to the interview protocol after pilot testing before use in the study (Supplementary material 1). The translation from English to Arabic was undertaken independently by BAA and BAS and then compared and amended to develop a consensus. Prompts were used by the interviewers to obtain more detailed information.
## 2.9. Data analysis method
The four-step method of analyzing qualitative data was used. This involves preparation of data, data reduction, displaying data, and verifying data [58]. Data preparation involved uploading the transcripts to the NVIVO 12 software program (QSR International). Data reduction in NVIVO included line by line coding, looking for similar concepts, grouping concepts into categories, and grouping categories into larger themes. Two researchers (BAA and BAS) independently familiarized themselves with the interview transcripts, recorded initial observations of the data, and identified codes. The analysis used both etic codes, developed based on a priori concepts from the interview guide, and emic codes generated from the words of the participants. In the displaying data phase, codes were grouped into categories and categories were grouped into themes. Codes, categories, and themes were discussed by all three researchers (LOH, BAA and BAS) and disagreements were resolved via consensus. Data verification was ensured by cross-checking the results with the original transcripts.
## 2.10. Rigor and trustworthiness
To ensure the rigor and trustworthiness of our results, the research design included using a research team and member-checking. In the weekly meetings of the research team held throughout the study, we engaged in researcher reflexivity, with attention to issues related to the quality of data collection, analysis, and interpretation. Working as a research team enabled us to reflect on our preconceived ideas and prevent the imposition of individual ideas or beliefs over those of participants. Member-checking was used to seek the confirmation of study participants regarding their transcripts. Participants were provided with full transcripts from their interview and offered the opportunity to make any amendments they wished, including adding or deleting text. In the results, we used direct quotations from participants to represent their experiences. All strategies added to the trustworthiness and rigor of the research process.
## 2.11. Ethical considerations
This study and its amendments had ethics approval from QU Institutional Review Board (QU-IRB 1070-EA/19). Several ethical issues were carefully considered in this study, including privacy, confidentiality, respect, and non-maleficence. To ensure participants' privacy, in person interviews were conducted in a private venue at Qatar University, and participants were reassured that their personal information would not be made public. Anonymity and confidentiality were ensured by using a pseudonym selected by the participant, and data were secured in a password-protected file on a password-protected computer accessible only by the researchers. Respect was also ensured through informed consent given by the participants prior to conducting interviews, and any ambiguity about the study was explained and clarified. Participants were assured that participation in the study was voluntary and that they had the right to withdraw at any time.
Finally, it is an ethical responsibility for researchers to do no (additional) harm [59]. As a research team we carefully considered the issue of non-maleficence, recognizing that participants may have already experienced significant harm because of WBO, and wanting to avoid inadvertently causing any further harm in the recruitment, data collection, data analysis, or reporting processes. Of specific consideration in this study was the possibility for harm resulting from the framing of body weight. Several studies have found that using pathologizing language about body weight is stigmatizing and leads to poor health outcomes (22, 60–63). As such, in this paper, we present these terms in a censored form (ob*sity and overw*ight) to minimize harm, in accordance with the position taken by researchers, health professionals, and social justice advocates that these terms are slurs (62, 64–66).
We were therefore very deliberate about our decision to not use terms such as “ob*se” or “overw*ight”. We also chose to not use the term “fat”. Although the word has been reclaimed by fat liberation activists addressing WBO [62], we believe that the term is predominantly regarded as pejorative in the Arab region. As such, in all study materials (including the recruitment email and social media posts, project information sheet, consent form, and interview protocol) and throughout the interviews we adopted the weight-inclusive approach [67] and referred to people as having larger bodies or being at a higher weight [62, 67].
## 3. Results
There were 29 participants from QU faculty, staff, and students (25 females, 4 males), mostly Arabic and born in Qatar, and aged 18–53 years (Table 1). All participants experienced both internalized and external WBO. The study revealed six major themes: characteristics of internalized WBO; nature of external WBO; timing of external WBO; sources of external WBO; extent of external WBO; and the impact of WBO. Each theme included a number of thematic categories. Table 2 summarizes the major themes, categories, and number of participants who experienced each thematic category. Although it was not the aim of the study to quantify the experiences of WBO, the number of participants that spoke about each thematic category is included to provide a sense of the extent of these experiences among the participants.
In reporting on the themes and thematic categories, representative quotes from the participants are included to illustrate the study's findings. The quotes are provided verbatim, with no amendments to correct for grammar or inclusion of the word “sic” to indicate perceived errors. This is consistent with the recommendation from the Associated Press as described in the Columbia Journalism Review, which states the use of “sic” can be interpreted as “snarky” and giving a sense of “we know better”, at the expense of the quoted source [68]. This is particularly important given that *English is* not the first language of any of the participants in our study. Respecting the words used by participants acknowledges that language is socially constructed, and this approach is therefore consistent with the constructionist epistemology that informs the study. It is also consistent with the health promotion value in the Red Lotus Critical Health Promotion Model of working with people transparently as a culturally and socially sensitive and reflexive ally respectful of all aspects of diversity, as opposed to the selective health promotion practice of working on people as an outside expert without explicit attention to the relevant cultural and social context or all aspects of diversity [52, 54].
## 3.1. Theme 1: Internalized WBO
Research question one for the study was what are the internalized WBO related attitudes, values, and beliefs of people in Qatar? All participants expressed a combination of internalized WBO related attitudes, values, and beliefs including feeling dissatisfied, sad, embarrassed, ashamed, worthless, or frustrated about their larger bodies. Some participants used pathologizing and/or derogatory terms to describe their bodies, indicating internalized WBO related beliefs about the acceptability of using those terms to describe their bodies.
## 3.1.1. Dissatisfied
Body dissatisfaction was the most common internalized WBO attitude, and the dissatisfaction was long standing. The desire for weight loss and a slimmer body was very strong among participants, leading to a lack of appreciation for their body. Sara 2 expressed the long-term sense of dissatisfaction, saying, “I've just grown up with this idea that I don't look fine and that I weigh too much, and I should have a thinner waist and thinner legs, and all these things mean I don't really appreciate my appearance.” Dani echoed the ongoing sense of dissatisfaction saying, “Every time I look at myself in the mirror, all I see is things I want to change.”
## 3.1.2. Sad
Participants spoke frequently of how sad they felt about their bodies, and their perceived lack of opportunities as a result of their body size. These opportunities included feeling attractive and feminine, and fitting in with peers. Dana recalled of her adolescent years, “It was horrible, I knew I was overw*ight, I knew I looked fat, I couldn't wear stuff that makes me look good, and being a female, I wanted to wear what other girls are wearing.” Participants also spoke about feeling sad but covering it up so that others were not aware of how they were feeling. As Butterfly said, “This doesn't make me feel good, I feel ugly and sad from the inside, but I pretend to be okay.” The tone of participants' voices as they recounted these experiences reflected their words. Participants sounded miserable when they remembered such situations from the past or present and talked about their body in a negative way.
## 3.1.3. Embarrassed and ashamed
Exposure to external WBO evoked deep feelings of internalized shame and embarrassment for participants. These feelings were still present for many, irrespective of how long ago the WBO occurred. Situations in which shame and embarrassment occurred included being subjected to negative comments by teachers in school, eating meals with family or friends, and eating in public. Aysha said, “I used to feel embarrassed to eat even if I'm hungry, especially in front of people.” Not finding appropriate clothing sizes in stores also led to participants feeling ashamed and embarrassed about themselves, rather than angry at the lack of options available to them. This was compounded by being treated badly by sales assistants, as Meem described, “When it comes to clothes, I feel ashamed and embarrassed when I don't find my size and some of the assistants there laugh at me because of this.”
## 3.1.4. Worthless
Beyond feeling dissatisfied, sad, embarrassed, and ashamed, many participants expressed feelings of worthlessness. Being around people with smaller bodies increased these feelings due to participants constantly comparing themselves with others, and ascribing judgment about their own relative worth based on their body size. Dani expressed this feeling saying, “I always felt lesser than the people around, less worthy, or less important. I don't think I could ever see myself as a normal, worthy person.”
## 3.1.5. Frustrated
Many participants expressed feeling frustrated with their bodies and their inability to make themselves smaller or more acceptable. They felt as though they should be able to control their body, lose weight if they tried, but were failing to do so. These feelings of frustration were internalized, and not necessarily shared with others. They also had a significant impact on the wellbeing of participants. Arif explained, “I feel frustrated and angry, but I keep it for myself, and it affects my whole day and my sleep”.
## 3.1.6. Pathologizing and derogatory self-labeling
About half of the participants used pathologizing terms such as “overw*ight” or “ob*se” when referring to their own body. In addition, some participants used terms such as “fat” in a negative manner, and other terms that they regarded as derogatory. The use of these labels to describe themselves resulted in more negative feelings and internalized WBO. Meem said, “I look at the mirror and call myself names—fatty, bear, seal—and this makes me feel awful.”
## 3.2. Theme 2: Nature of external WBO
Research question two for the study was what are the external WBO experiences of people in Qatar? External experiences are categorized under the themes of nature, timing, sources, extent, and impact of external WBO. This section addresses the nature of external WBO. All participants were bullied or teased, experienced discrimination, or were treated badly by others because of their larger body size.
## 3.2.1. Bullying and teasing
Weight-based bullying occurred in different settings and from various people. In addition, bullying and teasing had several forms such as being called names, verbal bullying, and hurtful comments from even the people closest to participants. When recalling such situations, participants were visibly upset or annoyed. Malak vividly recalled one such experience from her time at school, “One time, our teacher brought chocolates to the class, and some girls were taking more than one piece, so I wasn't left with anything. And when the teacher asked why I didn't get chocolate, the girls started saying that ‘she doesn't need chocolate, that is better for her, so she can lose weight' and the other girls started laughing. But I did not do anything and said that I did not take one because I was fasting, although I was not fasting that day.”
## 3.2.2. Discrimination
Being discriminated against and having limited opportunities when it comes to certain jobs or marriage were very common concerns among participants. Having a larger body was seen as an obstacle between the person and their goals or desires across all aspects of their lives including personal, social, and professional aspects. Sara 2 explained, “In the beginning of my academic life I wanted to get into the psychology major, and I went to speak to the head of department of psychology, so he can tell me if I have a chance. I was speaking to him academically in terms of GPA, courses I've finished, and he said literally “yeah but this is not gonna work, you need to lose weight”. He was like, “we don't have unhealthy people in psychology”. Mustafa described a similar experience of being denied entry to his profession of choice saying, “When it comes to applying for the military medical services, there is a specific weight; if you are higher, they don't accept you.” Discrimination during the school years was equally painful for participants. Malak recalled, “I remember at sports classes when it comes to choosing teams, no one picks me because I am fat and say that I will slow them down.” When sharing these experiences of being discriminated against, participants were very downcast and seemed to feel defeated, especially when talking about losing the opportunity to pursue their dream jobs.
## 3.2.3. Treated badly
In addition to teasing, bullying, and discrimination, participants were treated badly in other situations because of their larger body size, including at clothing stores, on public transport, at home, and in other public places. Zayed said, “I used to volunteer in animal shelter for dog walks, pet owners were having mean talks with me regarding how will I walk the dog if I cannot walk myself.” Arif felt as though he is treated badly everywhere he goes, explaining, “When I use public transport, and any outside places, everywhere, I feel that I am treated differently compared to others. Like, when I was using the bus, and it was crowded, one man was pushing me and saying that it's okay nothing will happen to me because I have a large body, and that we are not fitting in the bus because of fat people.” Stereotypes about people with larger bodies also resulted in being treated badly. For example, Malak shared an example from her school years, saying “At class when someone smells a bad smell, the girls try to put it on me and give hints that I am the one that smells bad because I am fat and I need to shower a lot, and I don't know where they get this idea from that if you are fat then you smell bad.”
## 3.3. Theme 3: Timing of external WBO
Participants spoke about experiencing WBO at all ages, including in their current lives. However, the experiences that hurt the participants most were those that occurred during childhood and adolescence. Many participants recalled stories from their childhood and school years with great clarity, including the pain felt at the time, and the ongoing shame and embarrassment resulting from the experiences. Participants also shared experiences of WBO in adulthood, and after marriage.
## 3.3.1. Childhood and teenage years
Most participants were bullied or teased because of their weight as children, and this escalated in teenage years, especially at school. Bullying was experienced from friends, classmates, other students, teachers, and in other settings such as at home, the gym, and other places. Dani vividly recalled her experiences, saying, “I was bullied a lot as a child, especially at school. People I don't know would come up to me and call me names like fatty or bear. They would literally point fingers at me in recess as I walked by them. I remember once being punched by boys because ‘It felt like beating a pillow' they said, and this whole year at middle school they'd call me ‘la vache qui rit' (translation: the laughing cow).” Likewise, Malak described the significant impact of early exposure to WBO, saying, “This affected me since childhood, I always put this idea in my mind that I am fat and this means that I am not like the other girls, and I can't be as beautiful as they are. The effect got even greater when I turned to a teenager. I entered the hospital several times, because of being obsessed with losing weight and looking as good as the other girls without caring about my health or anything else.”
## 3.3.2. Adulthood
Exposure to external WBO continued into adulthood for most participants. Ongoing exposure was exasperating for some participants, indicating that they may have felt it would decline once they left their childhood years. As Arif said, “This weight bullying started from a young age, and it continued till this time. I am 32 years old, and I am still getting bullied and harassed in different settings? How long will this continue?” Mariam expressed her frustration with ongoing exposure to WBO from her parents, saying, “When we gather as a family to eat, my parents tell me what I am supposed to eat, even though I am a grown a** woman”.
Several female participants spoke about the perception that a larger body is an impediment to getting married. Meem spoke about the dual pressures of men's preferences in a marriage partner, and families' desires to satisfy these, saying “I've also noticed that when it comes to marriage, men usually have a special request that they want to marry a slim and fit woman, with a nice body shape. Family also have a big role... As for my personal experience, my family used to tell me don't get fatter, no one wants to marry a fat woman, and usually hurtful words.”
## 3.3.3. After marriage
All eight of the married participants experienced significant levels of bullying, teasing, and harassment from their spouses after marriage. This caused significant distress for the participants, which was evident as they described how they are treated differently by their spouse now in relation to their body weight. Maha Mahmoud explained, “I was in that perfect shape in the eyes of society before I get married, after the marriage and you having kids and all of that, my husband started to comment on my body and that I should be losing weight, and that I am not the girl he married with that awful body”.
## 3.4. Theme 4: Sources of external WBO
Participants experienced external WBO from a variety of sources including family, friends, media, spouses, healthcare providers, teachers and professors, culture and society, and people at school, the gym, on public transport, in public places, and in the street.
## 3.4.1. Family
Most of the oppression based on body weight came from family members, particularly parents. Mothers and fathers put participants under constant pressure to restrict their eating and lose weight. Participants spoke about their parents mocking them because of their high weight or large size and comparing them unfavorably with their smaller sized siblings or others. Parents focused more on the participants' body weight than their achievements. As Dana recalled, “On the day of my master degree graduation, I was the top at my class, honor roll, and when I told my mum ‘Are you proud of me?' she said ‘Yes I am proud, but dear you were the biggest person on stage, you had the highest GPA and the highest weight', and that killed me.” Participants also spoke about being subjected to horrendous shaming experiences by their parents, even as very young children. Dani explained, “I was 7? 8? My biological father would gather my siblings around me as I got up on the scale and would tell them ‘Laugh at your sister' and then would sit me on the dinner table and not let me eat and everyone would call me ‘cow'.”
## 3.4.2. Friends
Friends were a prevalent source of external WBO, mostly in the form of hurtful comments and making fun of participants about their weight, often pretending they were joking. Participants spoke about the pain and embarrassment that this caused, especially when comments were made in front of others. Malak described one such situation, saying, “When my friends saw my elder sister, they were like ‘She's so much prettier than you are and she looks younger than you. Be more like her and lose weight'.” Malak described the significant hurt that experiences like this caused her. Amal described her perceptions about her relationship with friends, saying, “My friends tend to mock me and tease me because of my weight, and they abandoned me because of my weight, and they were only friends with me because they felt that I'm pathetic.”
## 3.4.3. Media
More than half of the participants cited the role of the media in perpetuating WBO. This included mass media such as movies and magazines, and social media, particularly Instagram in perpetuating unrealistic ideals about beauty and attractiveness through the use of filters and editing, and through the widespread sharing of before and after weight loss images. Butterfly summed up many participants' beliefs about the presence of models on the platform, saying “Instagram it is full of thin bodies as models, and it is really rare to find a picture of a model with fat body. We have been exposed to role models as thin and slim so it kinda have sticked in our head that the preferred or more beautiful is the thin type of bodies.” Participants also highlighted the role of media advertising and the selective representation of people with different sized bodies. Gandi explained, “There are advertisements out there that would put someone that's slender in there as if they're running in the forest and it's so beautiful behind them and someone with a large body at home he's sitting, he's depressed he has a bag of chips.” She went on to describe how these images create an association between an image and an emotion, and that such associations may lead some people to believe that this is how they should be. She explained, “I think that actually also affects people watching, so for example, if I would see that person with a larger body which is at home and just sitting and watching TV on their couch and eating chips and they look sad, (I would think) that's probably how I'm supposed to be. And not everyone recognize that's an image, they actually take it in and becomes part of their personality, and that's sad how powerful it (the media) is and how devastating at the same time. It (the media) should be used for good but it's being used for horrible things, really, really, it's so sad.” Representation of larger bodies in the media as the butt of jokes or as funny characters was also mentioned by participants as a source of negative stereotypes, making them sad and angry. As Sara 2 explained, “It really angers me, because they use their (larger) bodies to make people laugh, instead of using their presence to tell people its normal. We have personalities, rather than seeing us as just a body.” Sara 2 then described the impact of the combination of sources of WBO on mental health and wellbeing, saying “If it wasn't for the media and pop culture, and how people were raised, and what they see every day of bullying of everything that's different on TV, they wouldn't project it on other people. By constantly joking around not knowing that it can literally put people into depression.” Sara 2 went on to suggest this was a phenomenon confined to the Arabic media, and that “It's really different in American and European media.” Dana also commented on the combined role of the media and the family, saying “Family makes it happen, and then media kind of amplifies it a lot by cartoons and having comics about it, and some drawings in the newspaper of fat people being made fun of. So actually, they create it.”
## 3.4.4. Spouses
As mentioned in the thematic category about timing, all married participants reported their spouses as a source of external WBO. Participants described how their spouses tease or bully them and monitoring their food intake. As a result, they felt that they are not valued by their spouses and are neglected because of their higher weight. SA explained, “My husband is my number one bully. Can you imagine what it's like to live with a person who teases you and make fun of your body in every situation?” Um Abdulla described a recent situation, saying “Few days ago it was raining, so my husband was praying and saying, ‘Dear god, please let my wife lose weight' as a joke, and I acted like I did not care but it felt bad.” Meem described being called names and having her food intake closely monitored, saying “Even my husband teases me and calls me names such as ‘dubba' (translation: fatty), cow, and seal. And even when I eat, he watches every single bite and tells me ‘You eat a lot'.”
## 3.4.5. Healthcare providers
Some participants experienced WBO from their healthcare providers. SA described a situation common to several participants, saying, “The first sentence that a doctor would say is your BMI is high, so you need to lose weight. But when you see the other health indicators of me you would see that they are very good and nothing is wrong with me, but when it comes to BMI classification the doctor himself make stereotype for people.” Sara 3 described a similar experience of having every health issue attributed to her weight, saying, “I have regular visits to the hospital because of a certain health issue I suffer from. And each appointment I go to my doctor never forgets to mention how bad it is to be weighing this much, and that maybe all of what I'm in (my health issue) goes back to my weight.” Arif described an experience of having his vital signs assessment conducted by a nurse, saying “When I was around 18 and I got on the scale one time at the hospital, the nurse shockingly told me ‘Omg how could you carry all this weight in one body'.” Many participants described how being labeled as overw*ght or ob*se by their healthcare providers made them feel sad or depressed. Meem described the impact of being labeled ob*se by her doctor as making her “feel that I'm outcasted or rejected by the society.” She went on to describe her encounters with healthcare providers in relation to pregnancy and giving birth, saying “They always comment on my ob*se body. A doctor once told me that because I'm ob*se I won't be able to have a baby.” The doctor's bold prediction, rooted in weight bias, was completely inaccurate as Meem went on to have several children.
Health checkups by school healthcare providers were another source of exposure to WBO due to the practice of weighing students and labeling them with the BMI classification. Sara 3 explained her feelings about this, saying “I really don't think these labels are okay because they allow for so much discrimination. Students would wanna compare their results, and for anyone who isn't in the area of ‘ideal weight' or ‘normal weight' it can be such a terrible thing. I don't see the need to do it or put a label on it.” Not all participants rejected outright the healthcare providers' use of BMI classifications, regarding them as objective and even helpful, whilst paradoxically also acknowledging the negative effect. As Kaltham explained, “If it (the BMI label) is from a nutritionist or a doctor or like trainer then I want to know their opinion… it's a pressure for me to lose weight and exercise and eat healthy food. Okay it would affect me in a negative way but like at the end of the day it would be an incentive way for me to lose weight, do workouts and eat healthy.” Sarah also felt that being labeled with a BMI category would be both helpful and harmful, first saying that BMI categorization is a good thing because it helps people know where they are and what they need to do, and then highlighting the negative impact, saying that if she was labeled as ob*se, that “it will affect me, because I'll start to worry about my health.”
## 3.4.6. Teachers and professors
Teachers and professors were a source of external WBO for some participants. Participants' memories of situations with teachers from school were still intense and vivid, despite many years having elapsed. One of the participants spoke about how, at the age of 45, she could still recall every detail of the weight stigma she experienced from her teacher, how she will never forget how it felt, and the effect it continues to have on her life. Participants associated their experiences of WBO from teachers with loss of productivity, high rates of absence, and low academic performance in school. Butterfly recalled, “I remember once in my school and during the class, I was asking my teacher whether I can turn on the AC (air conditioning) because I was feeling hot, and she replied back to me ‘yeah of course you feel like it's hot because of the fat body' or something like ‘the fat you are carrying'.” The clarity with which Butterfly recalled this exchange with her teacher is indicative of the impact that even brief episodes of exposure to external WBO, particularly for children who are less well equipped to deal with them than adults. Some participants described their negative treatment at the hands of physical education teachers. Dana shared her feelings, saying, “Sports class was one of my least favorite classes. My physical education teacher gave us a test in a certain skill where you jump and flip, and on that day, it was my first menstrual day, so I wasn't able to perform. And when I told my teacher about this, she replied ‘You can just say that you're ob*se and ob*se people cannot do that'.” As described above, one participant was prevented from fulfilling her lifelong dream to study psychology due to her professor's belief that people with larger bodies are “unhealthy” and “unhealthy” people cannot be psychologists.
## 3.4.7. Culture and society
Arabic culture and society were highlighted as significant sources of WBO, with participants noting that this is contrary to the perception outside the Arab world that Arabic culture is more accepting of size diversity. Participants expressed the belief that WBO is regarded as completely acceptable and as a result is widespread in Arabic society. Participants noted that Arabic culture now mirrors western culture, with thin bodies indicating healthiness and large bodies indicating unhealthiness. Having a large body size is widely seen as sign of infertility in the Arab region. Participant also talked about the intergenerational effects, thereby challenging another misconception that WBO is a recent phenomenon in Arabic society. Sara 3 noted, “Our culture and society promotes the idea of big bodies being embarrassing, a problem, and a bad unacceptable thing.” Dana expressed her frustration with the role of culture saying, “What I hate the most about the Arabic culture, is that it always puts the blame on the girl's weight when it comes to everything, whether if she can't get pregnant or not yet married.” Battuta shared her belief that, “A perfect slim body is one of the most cherished ideologies in our Arabic culture, it's like Arab people are programmed to the idea that there is only one perfect body size.”
## 3.4.8. Others
Sources of WBO were not limited to family members, friends, healthcare providers, or teachers. Participants experienced WBO from other people including people at the gym, school, on public transport, and in streets and other public places. Sara 2 described a situation where strangers felt entitled to dictate her choice of activity, explaining, “Some ladies at the gym were looking at me in a weird way and they later came to tell me that I should do something about myself. I shouldn't be swimming because I shouldn't be wearing a swimming suit. I should go for a walk or something rather than swim, because no one wants to be looking at my body.” Sara 3 described a similar situation in which a stranger felt entitled to make unsolicited comments on her body, saying, “I was once at a conference, and I was wearing a little high waisted pants in which I guess the ‘flaws' of my figure were apparent, and this guy thought it would be okay to poke fun at my figure in the middle of everyone and he made a comment about how I looked ‘a little pregnant'.”
## 3.5.1. Normative
Although only a small number of participants spoke about the culture as a source of WBO, most participants considered WBO to be an everyday phenomenon in Arab culture. Participants believed that negative attitudes and practices toward people with larger bodies are so extensive and accepted that external WBO is regarded as normative within the Arabic culture. Sara 3 described how extensive this was for her, saying, “I was bullied for being fat in every situation and in every outfit and every celebration, Eid, weddings, etc.” Butterfly commented on the normative and intergenerational nature of external WBO, explaining, “*It is* has become like a normal thing in the society in our culture, each generation teaches the next one this idea. We grew up knowing that fat bodies are not as acceptable and even that they are shameful, and we can't be in a large size”.
## 3.6. Theme 6: Impact of internalized WBO and exposure to external WBO
Experiencing WBO resulted in negative mental, psychological, emotional, social, and physical consequences such as low self-esteem, self-isolation, depression and anxiety, restrictive eating or dieting, eating disorders, thinking about or proceeding with bariatric surgery, and suicidal ideation or attempt.
## 3.6.1. Low self-esteem
Almost all participants identified low self-esteem as a consequence of WBO. For participants, low self-esteem encompassed lack of confidence, negative body image, lack of love toward oneself, feeling unworthy and not good enough. For Battuta, “It affected my self-esteem. I started to hate my body and not accepting it. I'm not always at ease when I meet new people and I avoid meeting new people.” Amal explained, “I used to cry, and I hate doing my daily activities. It affected my productivity and sometimes it reached a point where I hurt myself. For example, I used to see myself in the mirror and say ugly! I hurt myself on purpose and I intentionally say that to myself. I really had low self-esteem.”
## 3.6.2. Self-isolation
As Battuta describe above, low self-esteem resulted in self-isolation. This was common amongst most participants, with exposure to WBO resulting in a range of negative social consequences. Participants spoke about how they actively avoid going out, taking group pictures, being around people or being socially engaged, especially on special occasions or at gatherings of family and friends. Aysha explained, “I became a person who don't want to be engaged in the community. I used to feel really shy and embarrassed, so I isolated myself from people.” Hala described a similar strategy, saying, “I isolated myself. I used to not want to be friends with anyone. I preferred to be alone to avoid people's comment. I used to hate going to occasions, and if I ever go, I used to stay in abaya because I hated how clothes look on me.” SA explained the impact on her, saying, “I hated going out to see people, I hated gathering with people. I don't approach people and talk to them because of my weight. So, it affected me socially a lot.”
## 3.6.3. Depression and anxiety
Participants strongly believed that WBO resulted in mental health conditions, including depression and anxiety requiring professional treatment. Dani described the impact for her, saying, “I suffered from depression for years and I used to be on antidepressants, till now I have depressive episodes from time to time. You hate your body, you hate yourself, you hate them as well. The psychological effect that this led to is much more serious than the weight itself.” Sara 2 also highlighted that these effects were because of exposure to WBO, explaining, “I would just say that I do not suffer from any health issues because of my weight. The only issues I have are on my mental health, anxiety and depression and these are not because of my weight they are because of how people view my weight and body”.
## 3.6.4. Restriction and dieting
Two-thirds of the participants spoke about their repeated attempts to change their body weight and thereby escape WBO through restricting their eating or embarking on diets. Many of these diets were deficient in nutrients, and unsustainable over a sustained period. Battuta recalled one such diet, saying, “I remember one time, I tried the ‘watermelon diet', so it basically tells you to eat nothing but a watermelon for a certain time, and I experienced horrible weight and hair loss at that time.” Zayed discussed the range of diets he had attempted, and the damaging consequences for his relationship with food, saying “I tried every weight loss method that can come across your mind. I reached a point where I fear food and count calories for every single food item I consume.”
## 3.6.5. Eating disorders
Almost half of the participants described how exposure to WBO led to the development of disordered eating behaviors and eating disorders, including self-induced vomiting, binge eating, emotional eating, and bulimia. Malak described her situation, saying, “I felt like I craved food more and more till it turned to binge eating. I started to eat without stopping, till I slowly reached my previous weight, and that made me commit a very awful thing, which was eating and then putting my finger in my mouth to induce vomiting, and this habit stayed with me for almost a week and then my body could not handle it anymore, so I went to the hospital.” Dani described how she developed an eating disorder as a child, saying, “I felt like this is always something that I had to focus on. I can't remember a single time period in my life that my weight obsession was out of the picture. It led to me forming bulimia at the age of 12.”
## 3.6.6. Bariatric surgery
Over half of the participants considered or had undergone bariatric surgery because of exposure to WBO, including sleeve gastrectomy, adjustable gastric band, and gastric balloon. Although this surgery has a high level of risk, it was seen as an acceptable and almost routine procedure to reduce exposure to WBO. As K stated, “Qatari society has one preference to the point that anyone with extra kilos will be told ‘there are surgeries to cut some weight, go have one'.” Dana talked about the process that led up to her decision, explaining, “I said ‘Well I'm sick and suffering, people still see that I'm fat, they don't see my achievement, so you know what, let me do this as a last resort. I'll do the gastric sleeve operation and see what happens'.”
## 3.6.7. Suicidal ideation or attempt
One of the most serious impacts of experiencing WBO was thinking about or attempting suicide. Almost half of the participants expressed wanting to end their lives at some point to relieve their suffering from exposure to WBO. Whilst recalling these feelings, participants demonstrated deep sorrow, and some were crying while sharing these experiences. Hala shared her experience, saying, “I had depression from time to time and I was always thinking of a way of dying, and I knew that if I confessed this to my family, they will not take it seriously. So I was lonely, and I attempted suicide using pills and went to hospital. At that time, I had my son who was 8 years old.” Butterfly shared her story, saying, “I couldn't take it any further, I even thought of ending my life instead of living this every single day.”
## 4. Discussion
This study explored experiences of weight-based oppression experiences among 29 students, staff, and faculty at Qatar University. WBO was perceived to be so common that it was regarded as normative and intergenerational. Experiences of WBO included teasing, harassment, stigmatization, and discrimination based on body weight from family, friends, spouses, healthcare providers, teachers, and other people. The media and Arabic culture were also regarded as important sources of WBO. Experiences of WBO occurred throughout life, but those that occurred in childhood and adolescence were particularly painful. These experiences had significant and lasting negative psychological, emotional, social, and physical consequences for participants' health and wellbeing. Participants experienced negative internalized feelings, beliefs, and attitudes about their own body weight such as low self-esteem, embarrassment, shame, body dissatisfaction, sadness, and worthlessness. Exposure to WBO resulted in social isolation, depression and anxiety, food restriction, dieting, disordered eating, and eating disorders. Some participants had thought about or tried bariatric surgery or suicide to escape WBO.
This is the first study to qualitatively explore experiences of weight-based oppression in Qatar and the Arab region, and in many respects, the study findings are comparable to those from studies in other regions of the world. In this study, participants perceived WBO to be highly prevalent in Qatar and the Arab region. In fact, WBO is perceived as being so common that it is regarded as normative and completely acceptable. This perception is consistent with studies elsewhere that have demonstrated the high prevalence of various forms of WBO such as teasing, bullying, and discrimination in countries in the Global North [69]. Further population-based studies are needed to determine if the actual prevalence of WBO is as high in the Arab region as perceived.
Participants experienced WBO at all ages, but the impact of exposure during childhood and adolescence was particularly significant. Sources of WBO at a young age included parents, friends, and teachers. Young people with higher body weight are vulnerable to weight-based bullying, harassment, stigmatization and teasing in school settings [3, 70]. Weight-based bullying is one of the most common types of bullying that children and youth face (71–73). Children and adolescents are mostly frequently exposed to WBO from family, friends, peers, and teachers. The memories of these exposures to WBO for participants in our study were very strong, and many years later evoked significant sadness. Although participants recalled many experiences WBO at other times in their lives, it was these childhood exposures that they seemed to be particularly damaging. Children are less equipped to deal with exposure to hurtful comments or behaviors, and if the perpetrators of those behaviors are people in positions of trust and authority, then the capacity of young people to reject these behaviors and resist the internalization of the messages is limited. Particular attention should therefore be paid to eliminating WBO in the structures and systems that most impact on young people, such as the family and school environments.
In adulthood, sources of WBO for our participants included family, friends, professors, healthcare providers, and for married participants, spouses. This is consistent with findings from other studies demonstrating the extent of this issue. WBO from family was the most commonly reported exposure, and participants relayed painful examples of their treatment at the hands of their parents and siblings. All married participants shared devastating experiences of WBO from their spouses. WBO from family can be the most painful experience as it comes from people who supposedly love and protect you [74]. With respect to healthcare providers, our participants spoke about the healthcare inequalities they have experienced from doctors and nurses, which reflects a significant body of literature demonstrating high levels of WBO from these professions. A recent systematic review confirmed widespread weight bias in a range of healthcare providers [75].
Interestingly, only four of our participants identified Arabic culture and society as a source of WBO, however almost all described WBO as being universal within Arabic culture. This differential may reflect the widespread belief that WBO is totally “normal”, and that is it not specific to Arabic culture. Various studies have investigated aspects of WBO such as weight bias internalization, exposure to weight stigmatizing experiences, and weight discrimination in the Global North, with most of these studies conducted in the USA [76]. Investigations in the Global South and in the Arab region in particular, are limited. More broadly, the cultural imperialism of the Global North has seen the adoption of “western” appearance ideals, particularly the thin/non-fat ideals for all genders. Rates of eating disorders have rapidly increased in the Global South [77] and identification with western culture is associated higher levels of eating disorder symptomatology for Arabic women [78]. The role of mass media and social media in this messaging was well recognized by our participants, consistent with literature demonstrating strong links between exposure to media and WBO in western cultural contexts [79].
Turning to the consequences of WBO, our study found that exposure to WBO results in significant psychological distress, including shame, embarrassment, feelings of worthlessness, depression, and anxiety. Many participants demonstrated significant levels of internalized WBO, indicating their belief that there is something inherently wrong with their larger bodies. Some participants also identified that their psychological distress was because of their unfair treatment based on their body weight. The strong relationship between psychological distress and WBO is consistent with many other studies. A recent systematic review [80] and meta-analysis [81] established that exposure to WBO is consistently associated with depression.
One of the most common behavioral consequences of exposure to WBO is changes to eating patterns. In our study we found that participants reported engaging in food restriction and dieting in response to internalized and external WBO. For many, the adoption of food restriction or dieting was strongly encouraged or even demanded by parents or family members. However, there is now a significant body of research that demonstrates the failure of dieting to sustainably reduce body size [82, 83], and subsequent weight regain is often attributed to the failure of the dieter, rather than a natural physiological response to dieting, furthering the shame that people feel about their bodies. For some participants in our study, restriction and dieting escalated into eating disorders, and others discussed developing eating disorders as a direct result of exposure to WBO. A recent scoping review revealed that rates of eating disorders in the Arab region averaged $31\%$ with some studies detecting rates up to $75\%$ in specific samples [84]. The relationship between internalized WBO, exposure to WBO, disordered eating, and eating disorders is well documented [85, 86]. Within the Arab region, studies on males in Kuwait [87] and females in the United Arab Emirates [14] have found associations between internalized or external WBO and eating disorder symptomatology. This is consistent with other studies that have demonstrated that weight-based stigmatization is associated with a range of behavioral consequences, including binge eating disorder [88]. Weight-based teasing in adolescence prospectively predicts binge eating up to 5 years later [89, 90].
Our finding that exposure to WBO resulted in suicidal ideation and suicide attempts adds to the existing literature about the severe consequences of WBO. WBO is associated with higher levels of suicidal ideation in adults, with the effect mediated by depression [91]. Likewise for young people [92] and children [93], the most serious emotional consequence of WBO is the increased risk of thinking about and attempting suicide. Adolescents who are teased about their body weight are two to three times more likely to have suicidal thoughts than those not subjected to such teasing [94]. In this study, around half of the participants had suicidal ideation or suicide attempts. This is consistent with the findings of a study that found over $50\%$ of females and $13\%$ of males who were exposed to WBO from their family and friends considered attempting suicide [9]. This is hugely concerning, and further research is urgently required to establish the prevalence of such consequences in Qatar and the Arab region.
The decision to undertake bariatric surgery such as sleeve gastrectomy, adjustable gastric band, and gastric balloon was another consequence of being exposed to WBO, and more than half of the participants in this study were considering such surgery or had already had it. This was particularly the case for participants who expressed feelings of sadness and depression, or that had disordered eating or eating disorders. This finding was expected as eating disorders, anxiety, and depression are prevalent in candidates for bariatric surgery [95], with depression and eating disorders more prevalent in bariatric surgery candidates than in the general population [96]. Undergoing bariatric surgery also increases the subsequent risk of self-harm, suicidal ideation, suicide attempts, and suicide [97, 98]. Making the choice to undergo bariatric surgery to escape WBO and its psychological sequalae is understandable, given the extremely low efficacy of other weight loss strategies, the internalization of negative beliefs and attitudes about higher weight, and ongoing exposure to the stress of WBO. Bariatric surgery is widely practiced in Qatar, and although no official government statistics are available, news reports stated that in 2014, around 2,000 surgeries were performed in a population at the time of 2.2 million people [99], with $70\%$ of recipients being women. This was compared to the rate in Japan where 200 bariatric surgeries were performed in a population of 127 million. In 2022, the government health service reported that it performs 800–1,000 surgeries per year [100]. The normative nature of WBO combined with the normalization of bariatric surgery creates the perfect storm to drive up rates of bariatric surgery.
This study explored WBO using the RLCHPM as a theoretical foundation. Exploring and understanding a health issue is part of the community assessment phase, which is the first phase in the health promotion process. By conducting regular critical reflection (represented by the stems of the plant in the RLCHPM) on the values and principles in the RLCHPM (represented by the tuber and roots), we ensured that the study explored WBO holistically and revealed a range of physical, mental, and social consequences of WBO (represented by the lotus flower pod). In addition, the RLCHPM guided us to examine the characteristics of people (represented by the stamens of the lotus flower) and different types of environments (represented by the first petal layer of the flower) that lead to WBO, and how these factors connect and operate at multiple levels from the individual level to the family, community, organization, and society levels. Using the RLCHPM therefore ensured that we took a socio-ecological or systems approach to the exploration of WBO (a value and principle represented by the tuber and roots). Finally, critically reflecting on the potential for harm throughout the research process helped us to minimize potential harms (a value and principle represented by the tuber and roots).
The findings from this study indicate that WBO operates at the intrapersonal, interpersonal, family, community, society, and population levels, and has significant negative psychological, emotional, behavioral, social, and physical consequences resulting in poor health outcomes. None of the participants indicated that WBO resulted in improvements in their health and wellbeing. This is contrary to the commonly held belief, also expressed by some public health writers, that greater exposure to WBO might give people with higher body weight the “motivation” to improve their (assumed) poor health and change their (assumed) poor behaviors [101]. This study provides evidence that such an approach would not only be unsuccessful at such “motivation”, but would perpetuate and extend the harm caused by WBO.
## 4.1. Strengths and limitations
There are several strengths of this qualitative exploratory study. It provides the first insight into the lived experiences of WBO of people in the Arab region. The study provides a holistic view of the phenomenon with exploration of participants' experiences of WBO from many different perspectives and at multiple inter-related levels. As a method for exploring a sensitive topic, using semi-structured interviews allowed participants to express themselves in their own words, and share their experiences in as much detail as they wished. In-person and WhatsApp call interviews also allowed for the observation of the body language and speaking tone of participants, and to connect their emotions to their words. Although the requirement to switch to online interview administration was initially regarded as a limitation potentially impacting on the quality and quantity of data, it was apparent that this may have inadvertently had a positive impact. We noticed that participants who used text or voice notes to complete their interviews provided significantly longer and more in-depth responses to the interview questions than many of those interviewed in person or via a WhatsApp video or audio call. This observation warrants further research to validate if using text and/or voice note exchanges provide the same rigor and quality as other more established interview administration methods.
Despite the important contributions of this study's findings, limitations must be considered. A limitation of the single face-to-face interview is that there was little time to build trust and rapport with participants. Because the interview addressed a sensitive issue and one that involves significant pain for many participants, the limited time may have inhibited their responses. A second limitation was the discrepancy between the interviewers and participants' body sizes. Neither of the interviewers is fat, and this may have affected the responses provided by participants in the in-person interviews. This may not have impacted on the interviews conducted via text or voice notes. A third limitation of the study is that the findings were generated from a relatively small group of people within the Qatar University community. Further studies are required to determine if these experiences are similar or different to those of people in the broader community in Qatar and the Middle East, and the extent of this issue in the Arab region. Finally, the findings from this study are particular to the participants and the interpretation of the researchers.
## 5. Conclusion
WBO in the Arab region is an important and unrecognized public health issue. This study, the first of its kind in the Arab region, demonstrated that WBO is so common that it is regarded as normative. For participants in our study, WBO had significant negative implications for their physical, mental, and social health and wellbeing. Further research is required to determine the nature and extent of WBO within the broader community and other countries in the Arab region. In addition, research must be conducted to develop and test the effectiveness of critical health promotion strategies to reduce internalized and external WBO in all sectors. Critical health promotion involves addressing systemic and structural sources of oppression [52] using a portfolio of strategies encompassing building healthy public policy, creating supporting environments, and strengthening community action as priority strategies. Of particular urgency is the need to develop critical health promotion programs addressing social and cultural systems and structures to reduce teasing, bullying, and negative experiences related to body weight in childhood. This will require working with governments, social media, corporations, parents, teachers, healthcare professionals, young people, and the community to develop critical health promotion programs that reduce children's exposure to toxic messages about their bodies and weight-related practices that are harmful to them.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
This study involving human participants was reviewed and approved by Qatar University Institutional Review Board. The participants provided their written informed consent to participate in this study.
## Author contributions
LO'H conceived and designed the study. BAA and BAS collected the data. All authors analyzed the data, wrote the manuscript, contributed to manuscript revision, read, and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1015181/full#supplementary-material
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|
---
title: 'Association between dietary inflammatory index and risk of endometriosis:
A population-based analysis'
authors:
- Penglin Liu
- Rashmi Maharjan
- Yixiao Wang
- Yubo Zhang
- Yanqin Zhang
- Chunyu Xu
- Yuning Geng
- Jinwei Miao
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10008869
doi: 10.3389/fnut.2023.1077915
license: CC BY 4.0
---
# Association between dietary inflammatory index and risk of endometriosis: A population-based analysis
## Abstract
### Background and aims
Chronic inflammation plays a significant role in the etiology of endometriosis, which might be affected by dietary intake. This study aimed to investigate the association between dietary inflammatory index (DII) and the risk of endometriosis.
### Methods
A cross-sectional analysis using data from the National Health and Nutrition Examination Survey (1999–2006) was conducted on 3,410 American participants, among whom 265 reported a diagnosis of endometriosis. DII scores were calculated based on the dietary questionnaire. The association of DII scores with endometriosis was evaluated by adjusted multivariate logistic regression analyzes, which were further investigated in the subgroups.
### Results
In the fully adjusted models, the odds ratio (OR) for endometriosis participants in the highest and middle tertiles of DII scores were 1.57 [$95\%$ confidence interval (CI): 1.14–2.17] and 1.18 ($95\%$ CI: 0.84–1.65), compared to the lowest tertile (Ptrend = 0.007). In subgroup analyzes, the significant positive association between DII scores and the endometriosis risk was also observed in non-obese women (ORtertile3vs1: 1.69, $95\%$ CI: 1.12–2.55; Ptrend = 0.012), women without diabetes (ORtertile3vs1: 1.62, $95\%$ CI: 1.16–2.27; Ptrend = 0.005), women with hypertension (ORtertile3vs1: 2.25, $95\%$ CI: 1.31–3.87; Ptrend = 0.003), parous women (ORtertile3vs1: 1.55, $95\%$ CI: 1.11–2.17; Ptrend = 0.011), and women using oral contraceptives (ORtertile3vs1: 1.63, $95\%$ CI: 1.15–2.30; Ptrend = 0.006).
### Conclusion
This nationally representative study found that increased intake of the pro-inflammatory diet, as a higher DII score, was positively associated with endometriosis risk among American adults. Our results suggested anti-inflammatory dietary interventions may be promising in the prevention of endometriosis. Further prospective studies are necessary to confirm these findings.
## Introduction
Endometriosis, a common benign gynecologic disease condition, is characterized by the implantation and growth of endometrial tissue outside the uterine cavity, causing inflammation and leading to the formation of scars and adhesion, which leads to kinds of symptoms, such as chronic pelvic pain, dysmenorrhea, dyspareunia, dysuria, and infertility [1]. The disease affects women of reproductive age with a high prevalence of 5–$10\%$ [2, 3]. However, current interventions for endometriosis have limited efficacy with high rates of symptom recurrence and are correlated with tremendous healthcare economic costs for long-term management [4].
Regarded as a chronic, inflammatory, endocrine, immunological, systemic and heterogeneous disease, the etiology of endometriosis remains largely elusive. However, chronic inflammation has been proposed as a well-established facilitator of the pathophysiological mechanism [1, 3]. Over the past few years, extensive scientific studies have tried to identify modifiable risk factors related to endometriosis, such as exercise and diet [5]. Diet, as a complex entity of anti-inflammatory or pro-inflammatory compounds, plays a critical role in modulating systemic inflammation (6–8).
The dietary inflammatory index (DII) [9], a literature-derived dietary assessment tool based on 45 food parameters, is designed to estimate the overall dietary inflammatory potential by a scoring algorithm and is associated with various inflammatory markers, including C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) (10–13). Furthermore, higher DII scores, representing a stronger pro-inflammatory diet, have been verified to be related to a variety of inflammatory-related conditions including diabetes [14], chronic kidney diseases [15], cancers [16], and cardiovascular diseases [17]. However, previous studies have focused on the relationship between endometriosis and specific individual nutrient intake (18–24). To our best knowledge, the relationship between DII and endometriosis has not been evaluated up to now.
Our study aims to provide the first data on the relationship between DII and the risk of endometriosis by using data from the National Health and Nutrition Examination Survey (NHANES) [25], a large population-based study in the United States (US). We also further detected whether the association varied according to obesity status, diabetes, hypertension, fertility status and the use of oral contraceptives among endometriosis patients.
## Data source
The study used a nationally representative sample from the NHANES, a cross-sectional series of interviews as well as physical and laboratory examinations conducted on the non-institutionalized US population [25]. Aiming to accurately estimate the nutritional and health status of the US population over time, NHANES collects data from approximately 10,000 people on 2-year cycles, based on a complex multistage probability sampling design with a nationally representative sample. All the NHANES protocols were approved by the US National Center for Health Statistics Ethics Review Board, and written informed consent was obtained from all participants [25].
## Sample and population
For the current study, data from 4 cycles of NHANES (1999–2000, 2001–2002, 2003–2004 and 2005–2006) were collected. A total of 21,210 female participants were enrolled, among whom women were excluded if they had missing data for the diagnosis of endometriosis ($$n = 15$$,653), and missing or unreliable dietary interview data ($$n = 322$$). In addition, those with incomplete data on the potential covariates (described below) were also excluded ($$n = 1$$,825). After exclusions, a total of 3,410 female participants were available for final analysis. The flowchart of the sample selection and study design is presented in Figure 1.
**Figure 1:** *The flowchart of this study. NHANES, National Health and Nutrition Examination Survey; DII, dietary inflammatory index.*
## Assessment of endometriosis
NHANES included data related to the diagnosis of endometriosis in the part of the “Questionnaire on Reproductive Health” from 1999 to 2006. Participants who had endometriosis were identified if they reported “yes” to the question “Told by the doctor having endometriosis?” at each survey cycle.
## Dietary inflammation index
The DII is a literature-derived scoring algorithm based on the data of dietary intake, designed to estimate the overall inflammatory potential of diet. The complete description of development and validation regarding the DII has been discussed in detail elsewhere [9].
Dietary data were obtained from 24-h dietary recall interviews in the part of the “Dietary Interview—Total Nutrient Intakes” and “Questionnaire on Alcohol Use,” which was utilized to calculate DII scores for all participants. DII scores of NHANES (1999–2002) were calculated based on the value of the first-day dietary interview data, since only the first-day dietary interview was conducted from 1999 to 2002. DII scores of NHANES (2003–2006) were calculated based on the mean value of the first-and second-day dietary interview data.
The DII food parameters available in the NHANES database included energy, total fat, dietary fiber, protein, carbohydrates, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, omega 3, omega 6, cholesterol, vitamin A, vitamins B1, vitamins B2, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, β-carotene, niacin, folic acid, Mg, Fe, Zn, Se, alcohol and caffeine. First, the Z-score of each food parameter for each participant was calculated. Second, each individual Z-score was converted to a centered percentile. Next, each centered percentile was multiplied by the standardized overall inflammatory effect score. Finally, all of the food-parameter-specific DII scores were summed to create the total DII score for each participant. The final DII score in the study was a continuous value, ranging from 4.812 (the most pro-inflammatory diet score) to -4.827 (the most anti-inflammatory diet score).
## Covariates
According to the previous studies [26, 27] and clinical experience, the following covariates were included: Hypertension was defined according to the following criteria: being told hypertension by a doctor, use of anti-hypertensive medication, the mean value of measured diastolic blood pressure ≥ 90 mmHg or the mean value of measured systolic pressure ≥ 140 mmHg (The diastolic reading with zero is not used to calculate the diastolic average; if all diastolic readings were zero, then the average would be zero; if only one blood pressure reading was obtained, that reading is the average and if there is more than one blood pressure reading, the first reading is always excluded from the average).
Diabetes was defined according to the following criteria: being told diabetes by a doctor, glycated hemoglobin A1c ≥$6.5\%$, fasting glucose ≥7.0 mmol/l, random blood glucose ≥11.1 mmol/l, 2-h oral glucose tolerance test blood glucose ≥11.1 mmol/l, use of diabetes medication or use of insulin.
## Statistical analysis
All analyzes were conducted following the NHANES analytic guidelines. The distribution of categorical covariates between endometriosis and non-endometriosis groups was compared by the chi-square test. The distribution of continuous variables was compared by the Wilcoxon rank sum nonparametric test or t-test according to the results of the normality test.
DII scores were categorized into three tertiles according to the distribution, and subsequent analyzes were performed. Multivariate logistic regression models were applied to estimate odd ratios (OR) and $95\%$ confidence intervals (CI) for the associations between DII scores and endometriosis, using the lowest tertile of DII scores as the reference category. We started by fitting a crude model with only DII scores, and then further adopted 3 adjusted models. Model 1 was adjusted for age, ethnicity, education level and obesity. Model 2 included the covariates of model 1 with additional adjustment for drinking status, smoking status, diabetes and hypertension. Model 3 included the covariates of model 2 with additional adjustment for marital status, fertility status and use of oral contraceptives. Linear trends across tertiles of DII scores were examined by modeling the median value in each tertile as a continuous variable in regression models. In addition, the association between the DII scores and endometriosis risk was tested on a continuous scale by the restricted cubic spline (RCS) curve with 4 knots (at 5th, 35th, 65th, and 95th percentiles) based on the logistic regression model. Stratified analyzes were conducted by obesity (yes, no), diabetes (yes, no), hypertension (yes, no), fertility status (Nulliparous, ≥ one birth), and use of oral contraceptives (yes, no).
A p value <0.05 was considered statistically significant (two-sided). All statistical analyzes were performed using R software (version 4.1.3).
## Characteristics of the study sample
The distributions of baseline characteristics between endometriosis and non-endometriosis groups are presented in Table 1. From the initial sample of 21,210 female participants, 17,800 were excluded due to the incomplete data, resulting in a final sample of 3,410 eligible participants, including 265 ($7.8\%$) endometriosis and 3,145 ($92.2\%$) non-endometriosis women. The age of participants ranged from 20 to 54 years in the entire study. The ethnicity differed significantly between the two groups. Furthermore, participants with endometriosis tend to be older, had a higher education level, and showed a higher prevalence of drinking, smoking, using oral contraceptives, as well as hypertension.
**Table 1**
| Characteristics | Total (n = 3,410) | Endometriosis (n = 265) | Non-Endometriosis (n = 3,145) | p value |
| --- | --- | --- | --- | --- |
| Age, median [IQR] | 40.0 [32.0, 47.0] | 42.0 [37.0, 48.0] | 40.0 [31.0, 47.0] | <0.001 |
| Ethnicity, n (%) | | | | <0.001 |
| Mexican American | 833 (24.4) | 25 (9.4) | 808 (25.7) | |
| Non-hispanic black | 798 (23.4) | 45 (17.0) | 753 (23.9) | |
| Non-hispanic white | 1,494 (43.8) | 180 (67.9) | 1,314 (41.8) | |
| Other races | 285 (8.4) | 15 (5.7) | 270 (8.6) | |
| Education, n (%) | | | | <0.001 |
| Less than high school | 924 (27.1) | 37 (14.0) | 887 (28.2) | |
| High school graduate | 831 (24.4) | 80 (30.2) | 751 (23.9) | |
| Above high school | 1,655 (48.5) | 148 (55.8) | 1,507 (47.9) | |
| Obesity, n (%) | | | | 0.154 |
| Yes | 1,330 (39.0) | 92 (34.7) | 1,238 (39.4) | |
| No | 2,080 (61.0) | 173 (65.3) | 1,907 (60.6) | |
| Smoking status, n (%) | | | | 0.005 |
| Never | 2,033 (59.6) | 133 (50.2) | 1,900 (60.4) | |
| Former | 547 (16.0) | 51 (19.2) | 496 (15.8) | |
| Now | 830 (24.3) | 81 (30.6) | 749 (23.8) | |
| Drinking status, n (%) | | | | 0.015 |
| Never | 557 (16.3) | 28 (10.6) | 529 (16.8) | |
| Former | 570 (16.7) | 54 (20.4) | 516 (16.4) | |
| Now | 2,283 (67.0) | 183 (69.1) | 2,100 (66.8) | |
| Diabetes, n (%) | | | | 0.179 |
| Yes | 258 (7.6) | 14 (5.3) | 244 (7.8) | |
| No | 3,152 (92.4) | 251 (94.7) | 2,901 (92.2) | |
| Hypertension, n (%) | | | | <0.001 |
| Yes | 941 (27.6) | 104 (39.2) | 837 (26.6) | |
| No | 2,469 (72.4) | 161 (60.8) | 2,308 (73.4) | |
| Marital status, n (%) | | | | 0.008 |
| Married | 1987 (58.3) | 168 (63.4) | 1,819 (57.8) | |
| Never married | 405 (11.9) | 16 (6.0) | 389 (12.4) | |
| Other | 1,018 (29.9) | 81 (30.6) | 937 (29.8) | |
| Fertility status, n (%) | | | | 0.068 |
| Nulliparous | 192 (5.6) | 22 (8.3) | 170 (5.4) | |
| ≥one birth | 3,218 (94.4) | 243 (91.7) | 2,975 (94.6) | |
| Oral contraceptive, n (%) | | | | <0.001 |
| Yes | 2,709 (79.4) | 234 (88.3) | 2,475 (78.7) | |
| No | 701 (20.6) | 31 (11.7) | 670 (21.3) | |
| DII, median [IQR] | 1.86 [0.74, 2.81] | 2.11 [1.03, 3.01] | 1.84 [0.71, 2.79] | 0.003 |
| DIIQ3, n (%) | | | | 0.016 |
| Lowest tertile | 1,137 (33.3) | 74 (27.9) | 1,063 (33.8) | |
| Middle tertile | 1,136 (33.3) | 82 (30.9) | 1,054 (33.5) | |
| Highest tertile | 1,137 (33.3) | 109 (41.1) | 1,028 (32.7) | |
The median DII score in the entire study sample was 1.86. Cases with endometriosis have significantly higher median DII score than those without endometriosis (2.11 versus 1.84, $$p \leq 0.003$$). Moreover, a higher percentage of participants in the endometriosis group ($41.1\%$) were found in the highest tertile of DII scores (the strongest pro-inflammatory diet) compared with $32.7\%$ in the non-endometriosis group. The baseline characteristics of the participants according to tertile categories of DII scores are presented in Supplementary Table S1.
## Association between DII and endometriosis
The associations between DII scores and endometriosis in the total cohort, are depicted in Table 2. The logistic regression showed that the crude OR of endometriosis was the greatest in the highest tertile of DII scores (OR 1.52, $95\%$ CI: 1.12–2.07) compared to that in the middle (OR 1.12, $95\%$ CI: 0.81–1.55) and lowest tertiles (Ptrend = 0.008). Similarly, in adjusted multivariate models 1, 2 and 3, we identified that participants within the highest tertile of DII score were significantly associated with a higher risk of endometriosis compared to those within the lowest tertile. Furthermore, in the final fully-adjusted model (model 3) adjusted by all covariates, participants within the highest tertile of DII scores had a $57\%$ increased risk of endometriosis compared to those within the lowest tertiles ($$p \leq 0.007$$). The trend analysis showed that the adjusted odds of endometriosis increased across increasing tertiles of DII scores in all models (Model 1: Ptrend = 0.003, Model 2: Ptrend = 0.007, Model 3: Ptrend = 0.007).
**Table 2**
| DII score | Cases with endometriosis/N | OR (95% CI) | OR (95% CI).1 | OR (95% CI).2 | OR (95% CI).3 |
| --- | --- | --- | --- | --- | --- |
| DII score | Cases with endometriosis/N | Crudea | Model 1b | Model 2c | Model 3d |
| Continuous values (−4.827–4.812) | 265/3,410 | 1.14 (1.05–1.25)* | 1.17 (1.07–1.28)* | 1.15 (1.05–1.26)* | 1.16 (1.06–1.26)* |
| Tertile categories | | | | | |
| Lowest (−4.827–1.130) | 74/1,137 | Reference | Reference | Reference | Reference |
| Middle (1.132–2.454) | 82/1,136 | 1.12 (0.81–1.55) | 1.20 (0.86–1.68) | 1.18 (0.84–1.65) | 1.18 (0.84–1.65) |
| Highest (2.458–4.812) | 109/1,137 | 1.52 (1.12–2.07)* | 1.62 (1.18–2.23)* | 1.56 (1.13–2.16)* | 1.57 (1.14–2.17)* |
| P trend | | 0.008 | 0.003 | 0.007 | 0.007 |
In addition, we also detected the relationship between the DII scores and endometriosis on a continuous scale by the RCS analysis, which also showed similar results ($$p \leq 0.017$$; Figure 2).
**Figure 2:** *Cubic regression spline of the endometriosis risk by DII scores among the entire population. Cubic regression spline was adjusted for age, ethnicity, education level, obesity, drinking status, smoking status, hypertension, diabetes, marital status, fertility status and use of oral contraceptives. ORs are indicated by solid lines and 95% CIs are presented by shaded areas. DII, dietary inflammatory index; OR, odds ratio; CI, confidence interval.*
## Subgroup analysis
In subgroup analyzes, the positive relationship between DII scores and the risk of endometriosis differed according to the stratification of obesity, diabetes, hypertension, fertility status, and use of oral contraceptives (Table 3).
**Table 3**
| Subgroup | Cases with endometriosis/N | DII scores, OR (95% CI) | DII scores, OR (95% CI).1 | DII scores, OR (95% CI).2 | DII scores, OR (95% CI).3 |
| --- | --- | --- | --- | --- | --- |
| Subgroup | Cases with endometriosis/N | Lowest tertile (–4.827–1.130) | Middle tertile (1.132–2.454) | Highest tertile (2.458–4.812) | P trend |
| Obesity | | | | | |
| Yes | 92/1,330 | | | | |
| Crudea | | Reference | 0.89 (0.51–1.55) | 1.31 (0.78–2.17) | 0.302 |
| Model 1b | | Reference | 0.96 (0.54–1.70) | 1.37 (0.81–2.33) | 0.243 |
| Model 2c | | Reference | 0.95 (0.53–1.68) | 1.32 (0.77–2.25) | 0.308 |
| Model 3d | | Reference | 0.93 (0.52–1.66) | 1.28 (0.75–2.20) | 0.356 |
| No | 173/2,080 | | | | |
| Crudea | | Reference | 1.27 (0.85–1.90) | 1.68 (1.14–2.47)* | 0.009 |
| Model 1b | | Reference | 1.36 (0.90–2.06) | 1.73 (1.16–2.59)* | 0.007 |
| Model 2c | | Reference | 1.33 (0.88–2.02) | 1.67 (1.11–2.51)* | 0.014 |
| Model 3d | | Reference | 1.33 (0.88–2.01) | 1.69 (1.12–2.55)* | 0.012 |
| Diabetes | | | | | |
| Yes | 14/258 | | | | |
| Crudea | | Reference | 0.40 (0.08–2.03) | 1.04 (0.32–3.34) | 0.983 |
| Model 1b | | Reference | 0.43 (0.08–2.33) | 0.91 (0.26–3.17) | 0.828 |
| Model 2c | | Reference | 0.47 (0.08–2.75) | 1.14 (0.30–4.38) | 0.878 |
| Model 3d | | Reference | 0.42 (0.07–2.65) | 1.06 (0.27–4.16) | 0.954 |
| No | 251/3,152 | | | | |
| Crudea | | Reference | 1.17 (0.84–1.64) | 1.57 (1.14–2.15)* | 0.006 |
| Model 1b | | Reference | 1.27 (0.90–1.79) | 1.68 (1.21–2.34)* | 0.002 |
| Model 2c | | Reference | 1.25 (0.88–1.76) | 1.61 (1.15–2.26)* | 0.005 |
| Model 3d | | Reference | 1.24 (0.88–1.75) | 1.62 (1.16–2.27)* | 0.005 |
| Hypertension | | | | | |
| Yes | 104/941 | | | | |
| Crudea | | Reference | 1.28 (0.73–2.23) | 2.07 (1.24–3.44)* | 0.005 |
| Model 1b | | Reference | 1.37 (0.77–2.41) | 2.31 (1.36–3.92)* | 0.002 |
| Model 2c | | Reference | 1.30 (0.73–2.31) | 2.16 (1.26–3.69)* | 0.004 |
| Model 3d | | Reference | 1.33 (0.74–2.37) | 2.25 (1.31–3.87)* | 0.003 |
| No | 161/2,469 | | | | |
| Crudea | | Reference | 1.05 (0.70–1.57) | 1.26 (0.85–1.86) | 0.258 |
| Model 1b | | Reference | 1.11 (0.74–1.68) | 1.27 (0.84–1.91) | 0.258 |
| Model 2c | | Reference | 1.11 (0.74–1.68) | 1.27 (0.84–1.91) | 0.263 |
| Model 3d | | Reference | 1.11 (0.73–1.68) | 1.26 (0.83–1.91) | 0.274 |
| Fertility status | | | | | |
| Nulliparous | 22/192 | | | | |
| Crudea | | Reference | 1.37 (0.48–3.91) | 1.19 (0.38–3.78) | 0.714 |
| Model 1b | | Reference | 1.90 (0.62–5.82) | 2.05 (0.58–7.18) | 0.219 |
| Model 2c | | Reference | 1.51 (0.45–5.02) | 1.34 (0.33–5.46) | 0.612 |
| Model 3d | | Reference | 1.61 (0.46–5.6) | 1.13 (0.26–4.99) | 0.759 |
| ≥One birth | 243/3,218 | | | | |
| Crudea | | Reference | 1.10 (0.78–1.54) | 1.56 (1.14–2.15)* | 0.006 |
| Model 1b | | Reference | 1.15 (0.81–1.64) | 1.62 (1.16–2.26)* | 0.004 |
| Model 2c | | Reference | 1.14 (0.80–1.61) | 1.57 (1.12–2.19)* | 0.009 |
| Model 3d | | Reference | 1.13 (0.79–1.60) | 1.55 (1.11–2.17)* | 0.011 |
| Oral contraceptive | Oral contraceptive | | | | |
| Yes | 234/2,709 | | | | |
| Crudea | | Reference | 1.11 (0.78–1.58) | 1.53 (1.10–2.13)* | 0.012 |
| Model 1b | | Reference | 1.20 (0.84–1.72) | 1.64 (1.16–2.31)* | 0.005 |
| Model 2c | | Reference | 1.19 (0.83–1.70) | 1.60 (1.13–2.26)* | 0.008 |
| Model 3d | | Reference | 1.20 (0.83–1.72) | 1.63 (1.15–2.30)* | 0.006 |
| No | 31/701 | | | | |
| Crudea | | Reference | 0.99 (0.40–2.44) | 1.24 (0.53–2.93) | 0.637 |
| Model 1b | | Reference | 1.23 (0.49–3.13) | 1.60 (0.64–3.98) | 0.317 |
| Model 2c | | Reference | 1.03 (0.39–2.70) | 1.39 (0.52–3.70) | 0.533 |
| Model 3d | | Reference | 1.03 (0.39–2.70) | 1.35 (0.50–3.62) | 0.572 |
The final adjusted model also showed a significant positive association between tertiles of DII scores and the endometriosis risk in non-obese women (OR 1.69, $95\%$ CI: 1.12–2.55; $$p \leq 0.012$$), in women without diabetes (OR 1.62, $95\%$ CI: 1.16–2.27; $$p \leq 0.005$$), in women with hypertension (OR 2.25, $95\%$ CI: 1.31–3.87; $$p \leq 0.003$$), in parous women (OR 1.55, $95\%$ CI: 1.11–2.17; $$p \leq 0.011$$), and in women using oral contraceptives (OR 1.63, $95\%$ CI: 1.15–2.30; $$p \leq 0.006$$). The increasing odds of endometriosis across increasing tertiles of DII scores in the above-mentioned subgroups were observed (Ptrend < 0.05).
Nevertheless, no significant association was observed in obese women (OR 1.28, $95\%$ CI: 0.75–2.20; $$p \leq 0.364$$), in women with diabetes (OR 1.06, $95\%$ CI: 0.27–4.16; $$p \leq 0.933$$), in women without hypertension (OR 1.26, $95\%$ CI: 0.83–1.91; $$p \leq 0.271$$), in nulliparous women (OR 1.13, $95\%$ CI: 0.26–4.99; $$p \leq 0.868$$), and in women without the use of oral contraceptives (OR 1.35, $95\%$ CI: 0.50–3.62; $$p \leq 0.549$$).
In addition, the magnitude of the relationship between DII scores and endometriosis was larger among women with hypertension in both crude (OR 2.07, $95\%$ CI: 1.24–3.44) and in the fully adjusted (OR 2.25, $95\%$ CI: 1.31–3.87) models, compared with those in the total population and other subgroups.
## Main finding
In this large population-based cohort from NHANES (1999–2006), a total of 3,410 participants were enrolled in the final analysis, with a $7.8\%$ prevalence of endometriosis. We used the tool of DII score and found a significantly positive association between dietary inflammatory load and risk of endometriosis, which suggested a promising anti-inflammatory dietary intervention for the prevention of endometriosis.
## Interpretation
To the best of our knowledge, this is the first study to determine the relationship between DII scores and the risk of endometriosis. As a common benign gynecologic disease, endometriosis exerts tremendous physical and psychological effects on the quality of life, compromises social relationships, and greatly decreases the economic productivity of society [28]. Given the increasingly important role of chronic inflammation contributing to endometriosis [1, 3, 29, 30], the modifiable risk factors related to inflammatory-related conditions, including diet, have become one of the research focuses. Previous studies frequently investigated the relationship between specific dietary nutrient intake and endometriosis (18–24).
For instance, alcohol use [18], a high consumption of trans-unsaturated fat [19] and a high intake of red meat [22] were related to a higher risk of endometriosis. In contrast, a high consumption of long-chain omega 3 fatty acids [19], a high consumption of vitamin D [20], a high intake of fruits and particularly citrus fruits [21] and a high intake of dairy foods during adolescence [23] were related to a lower risk of endometriosis. The different relationships between the above-mentioned individual dietary nutrients and the risk of endometriosis might be ascribed to the different effects of their pro-inflammatory or anti-inflammatory potential. Nevertheless, the joint inflammatory effect of the total dietary nutrients on the risk of endometriosis has not been explored to date.
Based on the previous studies, our study adopted the DII score, which integrated the mixed effect of whole food parameters on inflammation [9], confirming the positive relationship between the pro-inflammatory diet and the risk of endometriosis. In this study, we observed that participants in the US general population with the highest DII scores had a $57\%$ higher risk of endometriosis than those with the lowest DII scores after the adjustment for various covariates. Our results also support the current recommendations for a high intake of anti-inflammatory nutrients and a low intake of foods with pro-inflammatory potential.
The possible pathophysiological mechanisms of the positive relationship between DII scores and endometriosis risk might be explained by higher levels of systemic inflammation caused by the pro-inflammatory diet. Higher DII score, referring to a stronger pro-inflammatory diet, could increase the values of CRP [10, 12], IL-6 [11, 12], TNF-α [12, 13] and leucocytes as well as neutrophils [31], further contributing to the endometrial cell implantation, growth, invasion and angiogenic properties of ectopic lesions [32, 33].
Specifically, the association between the pro-inflammatory diet and the risk of endometriosis varied in subgroups in this study. When stratified by diabetes, no significant association between DII and endometriosis was observed; however, we found higher tertile of DII scores was associated with a higher risk of endometriosis among women without diabetes. Similar to endometriosis, the pathophysiology of diabetes is also closely linked to chronic inflammation triggered by the over-activation of the immune response (34–36). It is possible that women with diabetes may be bearing on a highly increased risk of endometriosis [37]. Thus, the potential influence of the pro-inflammatory diet on endometriosis risk may be more impactful among women without diabetes compared with those with diabetes.
Regarding the stratified analysis by obesity, prior researches have reported a strong and consistent inverse relationship between obesity and endometriosis risk, which demonstrated that obesity might be a protective factor against endometriosis [38]. In the current study, we also found that a higher DII score was significantly associated with a higher risk of endometriosis among the non-obese subgroup. Nevertheless, there was no significant relationship in the obese subgroup. This counter-intuitive result might be ascribed to the hypothesis that the role of the pro-inflammatory diet contributing to endometriosis was attenuated in the obese group due to the protective effect of obesity on endometriosis.
In the subgroup analysis stratified according to hypertension, a significant 2.25 times the risk possibility of endometriosis among women within the highest tertiles of DII scores compared with those within the lowest tertiles was observed in the hypertension subgroup. However, no significant association between DII scores and endometriosis was found among participants without hypertension. From a large prospective cohort study by Mu et al. in 2017, women with hypertension had a relatively higher risk for endometriosis (OR 1.29, $95\%$ CI: 1.18–1.41), compared to those without hypertension [39]. Similarly, our study indeed found a greater prevalence of endometriosis among patients with hypertension than those without hypertension, which was in line with the study of Mu et al. [ 39]. Our finding implied that hypertension might enhance the ability of a pro-inflammatory diet to increase the endometriosis risk and highlighted the significance of the anti-inflammatory diet in the female population with hypertension.
Among the subgroups of nulliparous participants and those without the use of oral contraceptives, although there was a higher risk of endometriosis associated with higher tertiles of DII scores, the results were not statistically significant. Based on the previous studies, nulliparous women and women without using oral contraceptives were associated with a higher risk of endometriosis, compared to parous women and those using oral contraceptives [26, 27, 40]. We speculate that the effect of the pro-inflammatory diet on endometriosis might be potentially weakened among the two specific women populations. Prospective researches are needed to perform the pro-or anti-inflammatory dietary intervention on these specific women groups to further explore the exact association between DII scores and endometriosis.
## Strengths and limitations
The strength of this study lies in the nationally representative sample of the US population from NHANES, which allows the findings to be generalized to the total population in the US. Additionally, a wide range of potential confounding variables were adjusted in the analyzes to decrease the bias, and subgroups were performed to evaluate the association between DII scores and endometriosis in different specific populations. Moreover, to our knowledge, this study was the first to detect the association between DII scores and endometriosis, which posed a possibility of the prevention of endometriosis by an anti-inflammatory diet, contributing significantly to the reduction of health and economic burdens related to endometriosis.
Similarly, several limitations also should be considered. One limitation of this study is that the cause and effect of the association between DII scores and endometriosis could not be identified. Another limitation is that the diagnosis of endometriosis was self-reported, and was not all confirmed laparoscopically in the NHANES. Prospective randomized controlled trials are necessary to verify the association of DII with endometriosis.
## Conclusion
This nationally representative study found that increased intake of the pro-inflammatory diet, as a higher DII score, was positively associated with endometriosis risk in American adults. Our findings suggested anti-inflammatory dietary interventions may be promising in the prevention of endometriosis. Further prospective studies are necessary to confirm these findings.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Centers for Disease Control and Prevention National Center for Health Statistics Research Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JM: conceptualization and supervision. PL and JM: data curation and writing—review and editing. RM and YW: investigation. PL and RM: methodology. PL: software, formal analysis, and writing—original draft preparation. YuZ and YaZ: visualization. CX and YG: validation. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by National Natural Science Foundation of China (grant number: 82271677); Beijing Hospitals Authority’s Ascent Plan (Code: DFL20221201); Gynecological Tumor Precise Diagnosis and Treatment Innovation Studio.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1077915/full#supplementary-material
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---
title: The effect of Tabata-style functional high-intensity interval training on cardiometabolic
health and physical activity in female university students
authors:
- Yining Lu
- Huw D. Wiltshire
- Julien Steven Baker
- Qiaojun Wang
- Shanshan Ying
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10008870
doi: 10.3389/fphys.2023.1095315
license: CC BY 4.0
---
# The effect of Tabata-style functional high-intensity interval training on cardiometabolic health and physical activity in female university students
## Abstract
Introduction: The increasing prevalence of metabolic syndrome and physical inactivity enhances exposure to cardiometabolic risk factors in university students. High-intensity interval training (HIIT) improved cardiometabolic health in clinical adults but the evidence in the university setting is limited. Furthermore, few studies examined the effect of low-volume HIIT on habitual physical activity (PA). Therefore, the primary aim of this study was to evaluate the efficacy of 12-week Tabata-style functional HIIT for improving multiple cardiometabolic health outcomes and habitual PA. We also investigated whether changes in habitual PA over the intervention period had an impact on exercise-induced health outcomes.
Methods: 122 female freshmen were randomized into the Tabata group ($$n = 60$$) and the control ($$n = 62$$). The Tabata training protocol involved 8 × 20 s maximal repeated functional exercises followed by 10 s rest with a frequency of 3 times per week for 12 weeks. Body composition, maximal oxygen uptake (VO2max), blood pressure (BP), blood lipids, fasting glucose and insulin, C-reactive protein and PA were objectively measured using standardized methods. Dietary intake was measured using a valid food frequency questionnaire. All variables were measured pre- and post-intervention.
Results: Mixed linear modelling results showed that there were large intervention effects on VO2max ($p \leq 0.001$, $d = 2.53$, $95\%$ CI: 2.03 to 3.00 for relative VO2max; $p \leq 0.001$, $d = 2.24$, $95\%$ CI: 1.76 to 2.68 for absolute VO2max), resting heart rate ($p \leq 0.001$, d = −1.82, $95\%$ CI: −2.23 to −1.37), systolic BP ($p \leq 0.001$, d = −1.24, $95\%$ CI: −1.63 to −0.84), moderate-to-vigorous intensity physical activity (MVPA) ($p \leq 0.001$, $d = 2.31$, $95\%$ CI: 1.83 to 2.77), total PA ($p \leq 0.001$, $d = 1.98$, $95\%$ CI: 1.53 to 2.41); moderate effects on %BF ($p \leq 0.001$, d = -1.15, $95\%$ CI: −1.53 to −0.75), FM ($p \leq 0.001$, d = −1.08, $95\%$ CI: −1.46 to −0.69), high-density lipoprotein (HDL) ($p \leq 0.001$, $d = 1.04$, $95\%$ CI: 0.65 to 1.42), total cholesterol ($$p \leq 0.001$$, d = −0.64, $95\%$ CI: −1.00 to −0.26); small effects on BMI ($$p \leq 0.011$$, d = −0.48, $95\%$ CI: −0.84 to 0.11), WC ($$p \leq 0.043$$, d = −0.37, $95\%$ CI: −0.74 to −0.01), low-density lipoprotein ($$p \leq 0.003$$, d = −0.57, $95\%$ CI: −0.93 to −0.19), HOMA-IR ($$p \leq 0.026$$, d = −0.42, $95\%$ CI: −0.78 to −0.05) and fasting insulin ($$p \leq 0.035$$, d = −0.40, $95\%$ CI: −0.76 to −0.03). Regression analysis showed that only the percentage change of HDL was associated with the change of MVPA ($b = 0.326$, $$p \leq 0.015$$) and TPA ($b = 0.480$, $$p \leq 0.001$$).
Conclusion: From the findings of the study we can conclude that 12-week low-volume Tabata-style functional HIIT was highly effective for university female students to improve cardiorespiratory fitness, body fat, some cardiometabolic health outcomes and habitual PA.
## 1 Introduction
It is well acknowledged that the process of atherosclerosis begins in childhood, and with the accumulation of cardiometabolic risk factors, its clinical manifestations are often observed in late adulthood (Andersen et al., 2004; Oliveira et al., 2010). Even though cardiovascular disease (CVD) mortality has reduced sharply over the last few decades in middle-aged and elderly people, the reduction rate is lower in young adults (Yano, 2021). This may be due to the accelerated aggregation of cardiovascular risk factors, which results in the increasing prevalence of metabolic syndrome (MetS) in young adults, particularly in young women (Ford et al., 2004; Regitz-Zagrosek et al., 2007; Hirode and Wong, 2020). Substantial evidence shows that a healthy lifestyle, including maintaining a favorable body mass, engaging in high levels of leisure-time physical activity (PA), and healthy eating habits, play an important role against the risk of MetS [Dickie et al., 2014; Wu et al., 2016; Kass et al., 2017; Lv et al., 2017; Lim et al., 2021;]. In addition, a high level of cardiorespiratory fitness (CRF) tends to have a protective effect against CVDs (Rankinen et al., 2007; Kim et al., 2014) and low levels of inflammation are associated with lower risks of future CVD events (Ridker et al., 2017; Zhu et al., 2018; Arnold et al., 2021). Unfortunately, a range of studies have found that these risk factors are not well controlled among university students, resulting in an increase of CVD risk in this population. For example, students are often observed to gain unhealthy body mass (Hovell et al., 1985; Vella-Zarb and Elgar, 2009), have low levels of leisure-time PA and prolonged sedentary time (Vella-Zarb and Elgar, 2009; Kwan et al., 2012; Kljajević et al., 2021), eat insufficient vegetables and fruits (El Ansari et al., 2012; Moreno-Gómez et al., 2012; Scarapicchia et al., 2015), and experience a downward trend in CRF (Scott et al., 2016; Lamoureux et al., 2019; Lan et al., 2022).
Although there have been multiple interventions aimed at improving university students’ lifestyles (Brown et al., 2014; Soriano-Ayala et al., 2020), PA levels (Sharp and Caperchione, 2016; Chiang et al., 2019; Heeren et al., 2018), or dietary habits (Lhakhang et al., 2014; Castillo et al., 2019; Whatnall et al., 2019; Hernández-Jaña et al., 2020), their effectiveness varied widely (Plotnikoff et al., 2015). This may be attributed to the absence of practice in most interventions. Practice is regarded as the key to the effectiveness of interventions, especially for those aimed at improving PA (Maselli et al., 2018). However, it seems to be a challenge to enforce a practical intervention in the university setting since lack of time is the most cited barrier to PA engagement among university students (Lovell et al., 2010), and participating in a practical trial will burden students further in addition to academic work (Sweeney, 2011). These provide a strong context for developing a novel intervention targeting modifiable risk factors associated with cardiovascular health in this population. To this end, a gender-specified strategy is necessary. Male and female students have different attitudes towards health promotion interventions (von Bothmer and Firdlund, 2005) and gender related differences in cardiovascular health risks can be observed. For example, women with insufficient PA are more likely to be obese (Vainshelboim et al., 2019). Particularly, obese young women are associated with increased risk of preterm delivery (Cnattingius et al., 2013), as well as impaired cognitive development of their infants (Casas et al., 2013). Given these findings, coupled with the low levels of PA (Haase et al., 2004; Grasdalsmoen et al., 2019) and the high prevalence of MetS and obesity in women (Riediger and Clara, 2011; Li et al., 2016; Ajlouni et al., 2020), an effective intervention to improve PA and cardiovascular health is warranted for female university students. Establishing an active lifestyle in early adulthood will contribute to long-term health (Jang and Kim, 2019).
High-intensity interval training (HIIT) has gained popularity among young people in recent years. HIIT is characterized by several short bouts of intermittent intense exercise interspersed with recovery periods of different durations (Laursen and Jenkins, 2002). Numerous data has shown that, compared to traditional moderate-intensity continuous training (MICT), HIIT can provide similar or greater improvements in maximal oxygen uptake (VO2max) with less exercise time and energy expenditure (Jelleyman et al., 2015; Milanović et al., 2015; Sultana et al., 2019). However, its effects on cardiometabolic health outcomes are controversial. The improvements in insulin sensitivity, blood pressure (BP), and body composition using HIIT were more likely to be observed in overweight or obese individuals, especially when they continued training for 12 weeks or longer (Kessler et al., 2012; Jelleyman et al., 2015; Batacan et al., 2017).
Several studies have evaluated the efficacy and feasibility of HIIT in university settings. Foster et al. [ 2015] evaluated the effect of 8-week Tabata training with eight intervals of 20-s cycling at $170\%$ VO2max/10-s rest on VO2max and reported a significant increase of $18\%$ in VO2max (Foster et al., 2015). Likewise, Eather et al. [ 2019] conducted an 8-week HIIT intervention using a frequency of three sessions per week. During each session, participants were required to complete a total of 8–12 min of training that included aerobic and strength exercises using the 30-s: 30-s work-rest ratio. After the intervention, CRF and muscular fitness were improved significantly whereas body composition had no gains (Eather et al., 2019). On the contrary, results from the study by Hu et al. [ 2022] showed that, after a 4-week functional exercise based HIIT, body composition, heart rate (HR), BP and arterial stiffness were improved in female university students with normal weight obesity (NWO). Although few improvements in cardiometabolic health were observed in short term HIIT (<12 weeks) (Batacan et al., 2017), the beneficial effects reported by Hu et al. [ 2022]’s study might be due in large part to the high levels of exercise intensity ($90\%$ of HRmax), during (3 × 9 minutes/session) and frequency (5 sessions/week) selected. Coupled with the obese participants, it was not surprising to see several improvements in cardiometabolic outcomes H following Hu et al. [ 2022]’s study. However, the total workout time of about 30 min seemed to go against the time-efficient nature of HIIT; Zhang et al. [ 2017] compared the fat-reducing effect of HIIT ($90\%$ of VO2max) and MICT ($60\%$ of VO2max) in obese female university students. After the 12-week intervention, participants in the HIIT group had similar reductions in percentage body fat (%BF) and total fat mass (FM) as those in the MICT group. Although the HIIT group had significantly shorter exercise durations than the MICT group, lasting nearly 30 min (Zhang et al., 2017). Improvements on CRF were consistent after HIIT. A recent meta-analysis of randomized controlled trials reported that HIIT protocols with short-intervals (≤30 s), low-volume (≤5 min) and short-term (≤4 weeks) were all effective in increasing VO2max (Wen et al., 2019). However, with respect to outcomes regarding to cardiometabolic health and body composition, the effects of HIIT appears to be more dependent on the FITT principle (frequency, intensity, times and type) as well as the baseline characteristics of participants.
Tabata training is recognized as one of the most efficient forms of HIIT. A Tabata-style HIIT protocol is characterized by its unique training procedure that comprises of eight bouts of 20-s exercise followed by a 10-s rest (Tabata, 2019). While the feasibility of the supramaximal intensity of $170\%$ of VO2max from its original protocol has been questioned (Gentil et al., 2016), studies have found health benefits when it is performed at an intensity of $70\%$–$80\%$ of HRmax (Menz et al., 2019; Popowczak et al., 2022). With a total of 4 min, the Tabata-style HIIT protocol had been reported to significantly improve both aerobic and anerobic capacities (Tabata et al., 1996; Murawska-Cialowicz et al., 2020). This was supported by a recent systematic review (Viana et al., 2019). Although results from this systematic review demonstrated limited evidence on the weight-reducing effect of the Tabata protocol, some improvements on body composition were reported in the study by Murawska-Cialowicz et al. [ 2020] and Domaradzki et al. [ 2020]. In addition, BP (Popowczak et al., 2022), fat oxidation (Pearson et al., 2020) and muscular performance (Menz et al., 2019; Islam et al., 2020) benefited from Tabata-style HIIT using functional exercises.
Nevertheless, there is concern that high perceived exertion and low enjoyment are associated with future PA and exercise adherence, especially in participants with a low level of CRF (Dishman, 1994; Follador et al., 2018). Intense exercises appeared to induce a subsequent decline in non-exercise PA and an increase in sedentary time (Skovgaard et al., 2019; Joshi and Dodge, 2022). This is thought to be a compensatory behavior, where the energy expended during exercises needs to be compensated for in other behaviors (Skovgaard et al., 2019). The compensatory behaviors may explain in part why the intervention did not produce the results expected (King et al., 2008). The short-term effects on compensatory movement behaviors following a Tabata-style HIIT had been investigated in our previous study and the results showed an increase in both sedentary time and moderate-to-vigorous intensity physical activity (MVPA) (Lu et al., 2022a).
Therefore, the primary purpose of this study is to evaluate the effectiveness of a Tabata-style functional HIIT on PA and cardiometabolic health in female university students with assessments at baseline and after 12 weeks of supervised training. We hypothesize that Tabata training is effective in improving cardiometabolic health and PA. Meanwhile, within the intervention group, subgroup analysis is planned to explore whether there are differential effects between normal weight and overweight/obese participants. We hypothesize that overweight/obese participants had greater improvements compared to normal weight ones after intervention. We further examine whether changes in cardiometabolic outcomes after the intervention are associated with changes in PA. We expected to see a positive relationship between changes in cardiometabolic outcomes and changes in PA.
## 2.1 Participants
This study was approved by Ningbo University ethics committee on 23 February 2022 (RAGH20220166). The participants recruitment process began in March 2022. Female students who enrolled in September 2021 from Ningbo University were invited to participate in the study via mobile messages and WeChat groups. A presentation was conducted during weekly PE classes comprising of an introduction, practical demonstration, and question and answer session. The students who showed interest were then interviewed face-to-face. During the interview, participants who decided to take part in the study were instructed to sign an informed consent form and join a WeChat group. Students with symptoms of or diagnosed CVDs, diabetes, or had any other conditions that might affect PA and dietary intake were excluded. In addition, female students who were pregnant or had the likelihood of pregnancy were also excluded. Students who confirmed participation were required to complete all pre-intervention measurements by the end of March 2022. Finally, 122 female students who had complete PA, diet, laboratory, biochemical, and lifestyle data were enrolled in this study. The process of sample and study timeline were outlined in Figure 1.
**FIGURE 1:** *The process of sample and study timeline. Note: PA, physical activity; PE, physical education.*
## 2.2 Study design
The study was a 12-week randomized controlled trial examining the effectiveness of a Tabata-style functional HIIT on PA and cardiometabolic health in female university students. Participants were randomly assigned to either Tabata or control groups. A complete randomization was used, and the random assignment was conducted using the RAND function in Excel. A researcher recorded the details of eligible participants in excel and assigned excel-generated random numbers to each of them. The random numbers given to the participants were ranked from the smallest to the largest, and the first 60 participants were assigned to the intervention group and the second 62 to the control group. Given our limited number of accelerometers and HR monitors, each of which was only 30, and the total of 122 participants, we decided to allocate 60 participants to the intervention group. During the following exercise sessions, 30 participants worked out in a group. The participants were then informed via phone or message. Because this was a practical exercise intervention, blinding was useless for participants. However, researchers were blinded to group assignment during the post-intervention measurement and data analysis. Participants allocated to the Tabata or the control group were invited to join a separate WeChat group. Participants in the control group were instructed to keep their routine habits during the intervention period. Participants in the intervention group were required to complete a total of 36 sessions of a Tabata-style functional HIIT with the frequency of three sessions per week. Each session involved a total of 19 min exercises (10 min warm-up, 4 min Tabata workout, and 5 min cool-down). Training took place in an indoor gym and was supervised by researchers. Training times were allocated on Tuesdays, Thursdays and one of the weekends, with morning and afternoon sessions from 9 to 10 a.m. and 3 to 4 p.m., respectively. Participants chose the training session based on their time schedule and adhered to it throughout the intervention. If participants were unable to attend a scheduled session, they had to make it up the next day and be monitored by a researcher. Despite the intensity of training, the retention of participants was high, with only one participant dropping out due to the incidence of hypoglycemia during the first session. The study yielded a final analysis of 59 participants in the Tabata group. Baseline measurements were completed prior to the beginning of the 12-week intervention program and the post-training tests were conducted within the week immediately following the cessation of program. It should be noted that, due to the shortage of accelerometers, the post PA data were measured in groups. The post PA measure was conducted at intervention week 11 and 12 in the intervention group, and at the following 2 weeks in the control group. All measurements, except blood related ones, were completed in the laboratory of Research Academy of Grand Health from Ningbo University. Venous blood samples were collected from a superficial antecubital vein by qualified and experienced phlebotomists according to standard phlebotomy procedures in the affiliated hospital of medical school from Ningbo University. Participants were required to abstain from foods, drinks other than water, and strenuous exercises for at least 8 h before the blood measurements.
## 2.3 Physical, physiological and body composition measurements
Height was measured in duplicate, using a standard stadiometer protocol. Weight, %BF, FM, fat free mass (FFM) and basal metabolic rate (BMR) were measured using bioelectrical impedance analysis (MC-180, TANITA CO., China). Participants were required to empty their bladder to minimize measurement error caused by “electrically silent” (Kushner et al., 1996). Participants wearing normal PE clothing without any metal items were instructed to stand barefoot on the bioelectrical impedance analysis by trained researchers. Outcomes were obtained from the associated software. Body mass index (BMI) was calculated using standardized equations. Waist circumference (WC) was measured with a flexible steel tape to the nearest 0.1 cm. For WC, two measurements were taken and if the difference between two measurements was larger than $1\%$, a third measurement was needed. The mean value was used in the analysis if two measurements were taken, and the median value was used if three measurements were taken. The WHO recommended a BMI of 25 kg/m2 or higher as the cut-off point for overweight or obesity. Although this cut-off point is controversial for the Asian population, there is not enough evidence to indicate a clear cut-off point for Asians. Furthermore, BMI was not considered as a good predictor of CVDs and mortality. According to the optimal cut-off points for identification of the CVD risks in Chinese adults (Zhou, 2002; Yang et al., 2016), participants were classified as overweight or obesity if they met one of the following criteria: 1) BMI ≥24 kg/m2; 2) WC ≥ 80 cm; and 3) %BF ≥ $35\%$.
Resting HR (HRresting), systolic BP (SBP) and diastolic BP (DBP) were measured by trained researchers using an automatic upper arm sphygmomanometer (HEM-1000, Omron, China). Prior to BP measurement, participants were required to sit and rest for at least 5 min. BP measurements were taken in a seated position from the left arm, with the upper section of the arm supported at the heart level. Three measurements were performed at 1-min intervals and the average of the second and the third readings were used for analysis. If the two readings differed by more than 5 mm Hg, an additional measurement was taken. BP was classified based on the recommendation from the American Heart Association as normal (SBP <120 mm Hg and DBP <80 mm Hg), elevated (SBP = 120–129 mm Hg and DBP <80 mm Hg), hypertension (HTN) stage 1 (SBP = 130–139 mm Hg or DBP = 80–89 mm Hg), and HTN stage 2 (SBP ≥140 mm Hg or DBP ≥90 mm Hg). Participants with elevated, HTN stage 1 or stage 2 were identified as having unhealthy BP.
## 2.4 Cardiorespiratory fitness
VO2max was used to measure the cardiorespiratory fitness of participants. The modified YMCA submaximal cycle ergometer test was used. Details of the VO2max measurement has been provided in previous studies (Lu et al., 2022a).
## 2.5 Dietary intake
Dietary intake was assessed by a staff-administered semi-quantitative food frequency questionnaire (FFQ). This 63-item FFQ was modified from the validated questionnaire used in the 2015 China Nutrition and Health Survey (Zhao et al., 2002). The frequency and quantity of food intake in the past 12 months were estimated. The dietary intake was divided into nine categories: staple food, beans, vegetables, fruits, milk, meats, eggs, snacks, and alcohol and beverages. The consumption frequency included: 1) never, 2) times per year, 3) times per month, 4) times per week, and 5) times per day and participants need to answer only one of these questions. The consumption amount for each time was recorded as Gram or ml. Samples were presented to help participants more accurately record serving amount. The intake of nutrients was calculated according to the Chinese Food Composition Tables (Yang YX and Pan, 2002) and manufacturer information.
## 2.6 Physical activity
Details of PA measurements had been provided in the previous study (Lu et al., 2022a). In brief, PA was measured using a triaxial accelerometer (ActiGraph, wGT3X-BT, Pensacola, FL, United States). Participants were instructed to wear the accelerometer on the non-dominant hip for seven consecutive days except during water-based activities. A valid day was defined as not less than $75\%$ of the wear time between 7 a.m. and 11 p.m. and participants provided at least four valid days including at least one weekend were included in the final analysis. The intensity of PA was classified according to the Freedson Adult algorithm. Sedentary was defined as < 100 counts per minute (cpm), light intensity physical activity (LPA) was defined as 100–1951 cpm, moderate intensity physical activity (MPA) was defined as 1952–5,724 cpm, vigorous intensity physical activity (VPA) as > 5,725 cpm, and MVPA as > 1952 cpm. Total physical activity (TPA) was defined as the daily vector magnitude cpm.
## 2.7 Cardiometabolic measurements
Generally, it was recommended to use fasting blood samples for blood profiling. In contrast, most of the 24 h were in a non-fasting state and the non-fasted lipid profile was suggested to better capture atherogenic lipoprotein levels (Nordestgaard, 2017). Moreover, the non-fasting blood collection would help recruit and retain participants. However, considering the large sample size and the flexibility of blood collecting time, we decided to use fasting blood to control measurement bias. Fasting blood samples were collected into EDTA-treated vacutainers and analyzed using standardized procedures in the hospital laboratory (Power Processor, Beckman Coulter’s complete range of clinical lab automation systems, United States). Samples will be analyzed for lipids (total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), HbA1c, C-reactive protein (CRP), fasting glucose (FPG) and fasting insulin. HOMA-IR was calculated (from measures described above) as follows: fasting insulin (μU/ml) x fasting glucose (mmol/L)/22.5. MetS was defined as having 3 or more of the following five abnormalities: 1) central obesity (WC: women ≥80 cm); 2) elevated TG (≥150 mg/dL (≥1.7 mmol/L)) or drug treatment; 3) low HDL (women <50 mg/dL (<1.3 mmol/L)) or drug treatment; 4) HTN (SBP ≥130 and/or DBP ≥85 mm Hg) or drug treatment; 5) elevated FPG (>100 mg/dL (>5.6 mmol/L)) or drug treatment (Alberti et al., 2009).
## 2.8 Interventions
Prior to the first session, participants were instructed to perform a familiarization session, during which 4 functional movements (jumping jacks, high knees, squat jumps, and mountain climbers in sequence) and their sequence of exercise was recorded. The researchers used the “Timer Plus” App to keep time and verbally count down 3 s to the end of each workout and rest. During the exercise, a chest strap HR monitor (Polar H10, Polar, Malaysia) was used to record HR data per second. Monitors were placed near the heart and attached by a band to the chest using non-slip silicone dots and a buckle, which did not make participants uncomfortable and affect exercise performance. HR data was processed using the Polar Flow. In this familiarization session, age-predicted HRmax (220—age) was used to examine the exercise intensity. According to the Tabata protocol, $90\%$ of HRmax was required during the sixth bout. For example, a 20-year-old participant had to reach a 180 beats per minute (bpm) of HR. After the exercise, participants would be informed whether they performed at the target intensity. The $90\%$ cut-off point was only used for researchers as the criterion for satisfactory delivery of Tabata training. Participants received qualitative feedbacks such as “you should go faster” or “you did a good job”. This feedback enabled them to perceive and familiarize themselves with the intensity required during the training. However, the age predicted HRmax was reported to overestimate among young adults (Gellish et al., 2007). Since all participants were freshmen with few age differences, the use of age-predicted HRmax equations appeared to result in excessive intensity for participants, especially for those with lower fitness. Therefore, during the formal intervention, the intensity of $90\%$ HRmax was not compulsive. Participants would receive the feedback about their performance after each exercise session.
All exercise sessions began with a 10-min low-to-moderate warm-up. The warm-up exercises involved joints movement, static stretching, and dynamic stretching. The HR was required to reach $60\%$ of HRmax during the warm-up. In the 4-min Tabata training, four functional movements were performed using participants’ own body weight. These movements were selected based on the Tabata training recommendations (Tabata, 2019) and showed a good acceptance in female university students in the polit study (Lu et al., 2022a). Participants were encouraged to repeat the movement as many times as possible during the 20-s workout, then rest for 10 s. The four movements were performed in sequence and then repeated, with a total of 4 min exercise. There was a 5-min cool down and stretching after the 4-min workout.
## 2.9 Exercise fidelity
Participants were instructed to wear a HR monitor (Polar H10, Polar, Malaysia), which was able to record HR data at 1-s intervals. The HRmean and HRpeak of each session were recorded and presented as a percentage of the individual’s HRmax. Participants’ HRmax was estimated using the conventional age-predicted equation used during the familiarization session. Although this equation was limited in predicting the accurate HRmax, with a high variability of 12 bpm among subjects of identical age, it was still recommended in clinical settings and published in resources by well-established organizations in the field (Fletcher et al., 2013). Furthermore, since the individualized exercise prescription was not the primary objective of the present study, the utility of age predicted HRmax appeared to be acceptable and reasonable. In our previous study, the HRmean achieved during exercise was $82.4\%$ ± $1.9\%$ of age predicted HRmax (Lu et al., 2022a). To reflect the high intensity, a cut-point of $80\%$ of HRmax was used to evaluate the intervention fidelity (Garber et al., 2011). Participants with a HRmean below the $80\%$ of HRmax over the 12-week intervention were excluded in the final analysis.
## 2.10 Other variables
Other variables including demographic data, lifestyle, and family history of HTN and type 2 diabetes were identified using a standardized questionnaire. Smoking, drinking alcohol, and staying up late were classified as never, sometimes, or always. The family history of HTN and type 2 diabetes were classified as yes or no.
## 2.11 Statistical analysis
All statistical analyses were performed using IBM SPSS for windows, version 23.0 (Chicago, IL, United States) and the significance level was set as $p \leq 0.05.$
Sample size was estimated by G * Power (version 3.1.9.7) (Heinrich Heine University, Dusseldorf, Germany) using a priori in relation to the primary outcome for this study. According to our previous study (Lu et al., 2022a), we calculated a correlation between groups of 0.70 and the effect size of 0.28 for the sample size estimation. The power and alpha were set at 0.95 and 0.05, respectively. Using a t-tests marched pairs design, 20 participants were required for the Tabata group.
Data normality was examined using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Logarithms were used for non-normality data. In the descriptive statistical analysis, participants were categorized into groups based on obesity status. Descriptive analyses were summarized as means with $95\%$ confidence intervals (CI), and proportions for continuous and categorical variables, respectively. For variables that were logarithmically transformed, geometric means and geometric standard deviations were used. ANOVA and χ2 test were used to analyze differences between continuous and categorical variables, respectively. Paired t-test was used to explore any differences between pre- and post-intervention within groups. Correlations between variables were examined using the Pearson product moment correlation coefficient. Mixed linear models were used to evaluate intervention effects between the Tabata and the control group, considering the percentage change in the measurements between the posttest and pretest as outcomes. Cohen’s d was used to provide a measure of effect size (mean difference on percentage change [posttest − pretest] between the Tabata and the control group over the intervention divided by the pooled SD of percentage change). The effect size was classified as trivial (<0.2), small (0.2–0.6), moderate (0.6–1.2), or large (>1.2) (Hopkins et al., 2009). Mixed linear models were also used to explore the moderating effect of weight status (normal weight vs overweight/obese) with interaction terms (intervention × weight status). The same statistical methods were used for subgroup analyses.
For cardiometabolic risk factors with significant changes after intervention, linear regression models were used to test the association between changes in cardiometabolic outcomes and changes in PA (MVPA and TPA). The variance inflation factor (VIF) was used to check the multicollinearity and an issue of multicollinearity was identified if VIF >5.0. Due to the high correlation between the change of MVPA and the change of TPA, for each cardiometabolic outcome, two models were tested. The dependent variable was the percentage change of cardiometabolic outcome. The independent variable was the percentage change of MVPA in model 1, and the percentage change of TPA in model 2. All models controlled for age, lifestyle variables, the baseline, and the percentage change of body composition, CRF and dietary variables, the baseline value of PA data, as well as the baseline value of the cardiometabolic outcome modelled. Participants’ ID numbers were used for data store and processing. Participants with less than $90\%$ attendance were excluded in the final analysis (Hernández-Jaña et al., 2020).
## 3.1 Descriptive statistics
The baseline characteristics of participants are presented in Table 1. One participant in the Tabata group dropped out the intervention due to the incidence of hypoglycemia. Four participants in the control group missed the post-test blood data. Therefore, a total of 59 participants in the Tabata group and 58 participants in the control were included in the final analysis.
**TABLE 1**
| Variables | All (n = 117) | Tabata (n = 59) | Control (n = 58) | p-value |
| --- | --- | --- | --- | --- |
| Age (years) | 20.38 (20.14 to 20.63) | 20.42 (20.03 to 20.82) | 20.34 (20.03 to 20.65) | p > 0.05 |
| Height (cm) | 163.48 (162.61 to 164.35) | 163.88 (162.70 to 165.07) | 163.07 (161.77 to 164.37) | p > 0.05 |
| Alcohol | | | | p > 0.05 |
| Never | 108 (92.31%) | 55 (93.22%) | 53 (91.38%) | |
| Sometimes | 9 (7.69%) | 4 (6.78%) | 5 (8.62%) | |
| Always | | | | |
| Staying up late | | | | p > 0.05 |
| Never | | | | |
| Sometimes | 42 (35.90%) | 20 (33.90%) | 22 (37.93%) | |
| Always | 75 (64.10%) | 39 (66.10%) | 36 (62.07%) | |
| Family history of hypertension | | | | p > 0.05 |
| Yes | 6 (5.13%) | 3 (5.08%) | 3 (5.17%) | |
| No | 111 (94.87%) | 56 (94.92%) | 55 (94.83%) | |
| Family history of diabetes | | | | p > 0.05 |
| Yes | 3 (2.56%) | 2 (3.39%) | 1 (1.72%) | |
| No | 114 (97.44%) | 57 (96.61%) | 57 (98.28%) | |
| Weight (kg) | 56.60 (55.19 to 58.01) | 56.23 (54.13 to 58.33) | 56.97 (55.03 to 58.92) | p > 0.05 |
| BMI (kg/m2) | 21.16 (20.69 to 21.63) | 20.92 (20.22 to 21.63) | 21.40 (20.76 to 22.04) | p > 0.05 |
| WC (cm) | 73.82 (72.91 to 74.72) | 73.92 (72.59 to 75.24) | 73.72 (72.45 to 74.98) | p > 0.05 |
| %Body fat | 27.42 (26.69 to 28.14) | 27.94 (26.79 to 29.08) | 26.88 (25.97–27.80) | p > 0.05 |
| FM (kg) | 15.71 (14.98–16.43) | 15.90 (14.80–16.99) | 15.52 (14.54–16.49) | p > 0.05 |
| FFM (kg) | 40.89 (40.07–41.72) | 40.34 (39.06–41.61) | 41.46 (40.39–42.52) | p > 0.05 |
| Basal Energy expenditure (kcal) | 1234.33 (1219.07–1249.60) | 1236.17 (1212.74–1259.60) | 1232.47 (1212.24–1252.70) | p > 0.05 |
| VO2max (mL/kg/min) | 34.45 (33.72–35.19) | 34.24 (33.21–35.27) | 34.67 (33.59–35.76) | p > 0.05 |
| VO2max (L/min) | 1.94 (1.89–2.00) | 1.92 (1.84–1.99) | 1.97 (1.88–2.06) | p > 0.05 |
| Resting Heart Rate (bpm) | 89.48 (87.76–91.20) | 89.03 (86.55–91.52) | 89.93 (87.47–92.39) | p > 0.05 |
| SBP (mm Hg) | 122.19 (120.03–124.29) | 121.37 (118.37–124.38) | 123.02 (120.00–126.04) | p > 0.05 |
| DBP (mm Hg) | 70.43 (69.29–71.56) | 71.46 (69.88–73.04) | 69.38 (67.74–71.01) | p > 0.05 |
| HDL (mmol/L) | 1.75 (1.65–1.84) | 1.72 (1.58–1.85) | 1.78 (1.65–1.91) | p > 0.05 |
| LDL (mmol/L) | 2.58 (2.48–2.69) | 2.63 (2.47–2.78) | 2.54 (2.40–2.68) | p > 0.05 |
| TG (mmol/L) | 1.10 (1.01–1.18) | 1.09 (0.97–1.22) | 1.10 (0.98–1.21) | p > 0.05 |
| TC (mmol/L) | 4.94 (4.81–5.08) | 4.91 (4.70–5.11) | 4.98 (4.80–5.16) | p > 0.05 |
| HbA1c (%) | 5.08 (5.03–5.14) | 5.06 (4.99–5.14) | 5.11 (5.03–5.18) | p > 0.05 |
| FPG (mmol/L) | 4.92 (4.83–5.01) | 4.93 (4.81–5.04) | 4.91 (4.76–5.05) | p > 0.05 |
| Fasting insulin (μU/mL) | 4.58 (4.38–4.79) | 4.62 (4.34–4.90) | 4.55 (4.24–4.85) | p > 0.05 |
| HOMA-IR | 1.00 (0.95–1.05) | 1.01 (0.95–1.07) | 0.99 (0.92–1.07) | p > 0.05 |
| CRP (mg/L) | 0.81 (0.79–0.83) | 0.79 (0.76–0.82) | 0.82 (0.80–0.85) | p > 0.05 |
| MVPA (min/d) | 103.68 (100.66–106.70) | 105.14 (100.72–109.56) | 102.20 (97.98–106.41) | p > 0.05 |
| TPA (cpm) | 1189.26 (1142.38–1236.13) | 1167.21 (1099.78–1234.63) | 1211.69 (1145.04–1278.33) | p > 0.05 |
| Energy intake (kcal/day) | 1752.56 (1661.31–1843.82) | 1706.27 (1591.21–1821.32) | 1799.66 (1655.01–1944.30) | p > 0.05 |
| Vegetable (g/d) | 263.85 (241.97–285.72) | 249.90 (220.29–279.51) | 278.04 (245.30–310.78) | p > 0.05 |
| Fruit (g/d) | 202.83 (181.28–224.37) | 217.59 (183.39–251.79) | 187.81 (161.27–214.34) | p > 0.05 |
At baseline, according to the definition of overweight and obesity used in this study, 27 ($23.08\%$) participants were classified as overweight or obese. Based on the MetS definition used in this study, the prevalence of MetS was $5.13\%$. For individual components of MetS, high BP was the most prevalent, with 36 ($30.77\%$) participants had a SBP ≥130 mm Hg. 25 ($21.37\%$) participants had low HDL, 20 ($17.09\%$) participants had central obesity, 13 ($11.11\%$) had elevated TG, and 10 ($8.55\%$) had elevated FPG. 43 ($36.75\%$) participants had one MetS component, 22 ($18.80\%$) participants had two MetS components and 46 ($39.32\%$) participants had none of these. Participants showed a relatively high level of PA, with only one participant failed to meet the minimum level of MVPA recommended. The average daily PA was 103.68 ($95\%$ CI: 100.66 to 106.70) minutes for MVPA and 1189.26 ($95\%$ CI: 1142.38 to 1236.13) cpm for TPA. Based on the recommended daily vegetable (300–500 g) and fruit (200–350 g) intake for Chinese people (Gu et al., 2021), most participants failed to meet the minimum daily vegetable and fruit intake ($64.96\%$ for vegetable and $53.85\%$ for fruit). The average daily vegetable and fruit intake were 263.85 ($95\%$ CI: 241.97 to 285.72) g and 202.83 ($95\%$CI: 181.28 to 224.37) g, respectively. Participants consumed an average of 1752.56 ($95\%$CI: 1661.31 to 1843.82) calories per day. Alcohol and smoking were less common among female university students, with only 15 participants reporting alcohol drinking sometimes and no participants reporting smoking. Family history of hypertension and diabetes were reported by 6 and 3 participants, respectively. Staying up late was more common among participants, with 27 participants reporting staying up late sometimes and 90 reporting always. The relative and absolute cardiorespiratory fitness (VO2max) was 34.45 ($95\%$ CI: 33.72 to 35.19) mL/kg/min and 1.94 ($95\%$ CI: 1.89 to 2.00) L/min, respectively. According to the CRF percentiles recommended by Kaminsky et al. [ 2017], 24 ($20.51\%$) participants were below the 50th percentile VO2max of women aged 20 to 29 years of 31.0 mL/kg/min. Table 1 presented the separate data for Tabata and control groups and there were no significant between-group differences in parameters between groups at baseline.
## 3.2 Exercise fidelity
The average HRmax for the total Tabata group was 199.58 ± 1.52 bpm. All participants met the minimum level of intensity and a total of 2,124 individual heart rate data were analyzed. Over the 12-week intervention, the HRmean during exercise was $83.24\%$ ($95\%$ CI: $82.75\%$ to $83.74\%$) of individual HRmax, with a between-subject SD of $1.89\%$. The HRmean varied between participants from $80.8\%$ to $86.6\%$. The within-subject SD was $0.24\%$. The participants’ HRmean varied from $82.77\%$ to $83.77\%$ over 36 exercise sessions. The HRpeak for exercise session across the intervention was $93.32\%$ ($95\%$ CI: $92.52\%$ to $94.11\%$) of individual HRmax, with a between-subject SD of $3.05\%$. The HRpeak varied between participants from $89.8\%$ to $97.3\%$. The within-subject SD was $0.30\%$. The participants’ HRpeak varied from $92.84\%$ to $93.91\%$ within different exercise sessions over the intervention. There were no differences between HRmean and HRpeak during session 1 to session 12.
## 3.3 Post-intervention effects
The total time spent over the 12-week intervention was 684 min, with 360 min for warming up, 144 min for Tabata training, and 180 min for cooling down and stretching. Over the intervention, participants performed a total of 96-min of high-intensity exercise (8 min per week). Only 1 participant dropped out the intervention, resulting in a satisfied exercise adherence of $98.31\%$. The effects of the Tabata style functional HIIT are presented in Table 2. There were significant ($p \leq 0.05$) interaction effects for WC ($$p \leq 0.002$$), MVPA ($$p \leq 0.007$$), TPA ($$p \leq 0.009$$), LDL ($$p \leq 0.004$$), TG ($$p \leq 0.010$$), TC ($p \leq 0.001$), daily energy intake ($$p \leq 0.012$$) and vegetable intake ($$p \leq 0.001$$). Therefore, within the Tabata group, we also conducted a separate analysis for participants with overweight or obesity and those with normal weight (Table 3).
## 3.4 Body composition
Body mass and BMI were unchanged in the Tabata group after intervention, while they increased in the control group ($1.25\%$, $95\%$ CI: $0.46\%$ to $2.03\%$), with statistically significant differences on percentage changes between Tabata group and control group ($$p \leq 0.011$$, d = −0.48, $95\%$ CI: −0.84 to −0.11). Both effects were small in magnitude. There was no statistically significant change in WC for both groups while a statistically significant differences on percentage changes was observed ($$p \leq 0.043$$, d = −0.37, $95\%$ CI: −0.74 to −0.01). The intervention effect was small on WC. There were significant decreases in %BF (−$2.57\%$, $95\%$CI: −$4.06\%$ to −$1.09\%$) and FM (−$2.61\%$, $95\%$ CI: −$4.36\%$ to −$0.85\%$) for the Tabata group, with moderate intervention effects between the Tabata group and control group ($p \leq 0.001$, d = −1.15, $95\%$ CI: −1.53 to −0.75 for %BF; $p \leq 0.001$, d = −1.08, $95\%$ CI: −1.46 to −0.69 for FM). FFM and metabolic basal rate (MBR) were improved significantly in the Tabata group ($1.07\%$, $95\%$ CI: $0.37\%$ to $1.77\%$ for FFM; $0.95\%$, $95\%$ CI: $0.33\%$ to $1.56\%$ for MBR) and no significant intervention effects were observed between two groups ($$p \leq 0.051$$ and $$p \leq 0.050$$ for FFM and BMR, respectively).
Subgroup analysis showed that weight and BMI significantly decreased in participants with overweight or obesity only (−$3.54\%$, $95\%$ CI: −$4.63\%$ to −$2.45\%$). There were significant differences on percentage changes on weight and BMI between the overweight/obese group and the normal weight group after adjusting for the training group (morning/afternoon group). ( $p \leq 0.001$, d = −2.19, $95\%$ CI: −2.95 to −1.38). The effects were large in magnitude. Although WC was not improved after intervention in the pooled sample in Tabata group, a significant decrease on WC was observed in participants with overweight or obesity (−$1.24\%$, $95\%$ CI: −$2.13\%$ to −$0.35\%$). Even though %BF and FM decreased significantly in both normal weight and elevated weight participants; we observed significant differences on percentage changes on FM between groups ($$p \leq 0.001$$, d = −1.17, $95\%$ CI: −1.86 to −0.44). Among overweight or obesity participants, we observed a decrease of $5.28\%$ ($95\%$ CI: $2.52\%$ to $8.04\%$) for %BF and $8.61\%$ ($95\%$ CI: $5.37\%$ to $11.85\%$) for FM. There was a decline of $2.02\%$ ($95\%$ CI: −$0.33\%$ to −$3.71\%$) for %BF and $1.38\%$ ($95\%$ CI: −$0.48\%$ to $3.24\%$) for FM in normal-weight participants. Only participants with normal weight had an increase in FFM and RBR after intervention ($1.47\%$, $95\%$ CI: $0.71\%$ to $2.23\%$). There was a significant difference on percentage change on FFM between groups ($$p \leq 0.010$$, d = −0.93, $95\%$ CI: −1.62 to −0.22).
## 3.5 Cardiorespiratory fitness
After the 12-week intervention, both relative and absolute VO2max were significantly improved in the Tabata group ($12.87\%$, $95\%$ CI: $11.00\%$ to $14.73\%$ for relative VO2max; $12.73\%$, $95\%$ CI: $11.02\%$ to $14.44\%$ for absolute VO2max), with a large intervention effect between groups ($p \leq 0.001$, $d = 2.53$, $95\%$ CI: 2.03 to 3.00 for relative VO2max; $p \leq 0.001$, $d = 2.24$, $95\%$ CI: 1.76 to 2.68 for absolute VO2max). Neither relative nor absolute VO2max increased in the control. We found a significant decrease in HRresting in Tabata group only (−$8.62\%$, $95\%$ CI: −$10.34\%$ to −$6.89\%$), with a significant between-group difference ($p \leq 0.001$, d = −1.82, $95\%$ CI: −2.23 to −1.37). The intervention effect was large.
Subgroup analysis showed that there was a significant difference on percentage change on relative VO2max between the overweight/obese participants and the normal weight counterparts ($$p \leq 0.045$$, $d = 0.71$, $95\%$ CI: 0.01 to 1.39). The effect was moderate. After the intervention, participants with overweight or obesity had a greater improvement in relative VO2max of $16.98\%$ ($95\%$ CI: $12.68\%$ to $21.28\%$) compared with those with normal weight ($12.03\%$, $95\%$ CI: $9.98\%$ to $14.08\%$). However, no between-group differences were observed on absolute VO2max ($$p \leq 0.963$$) or HRresting ($$p \leq 0.792$$).
## 3.6 Blood pressure
For BP data, only SBP decreased significantly in the Tabata group (−$3.66\%$, $95\%$ CI: −$4.73\%$ to −$2.58\%$), with a large intervention effect ($p \leq 0.001$, d = −1.24, $95\%$ CI: −1.63 to −0.84). DBP was unchanged after intervention in both groups. Among the Tabata group, there was no difference on percentage change on SBP ($$p \leq 0.798$$) or DBP ($$p \leq 0.991$$) between participants with overweight or obesity and those with normal weight.
## 3.7 Lipid profiles
HDL significantly increased in the Tabata group ($8.54\%$, $95\%$ CI: $5.82\%$ to $11.27\%$), with a moderate intervention effect between groups ($p \leq 0.001$, $d = 1.04$, $95\%$ CI: $0.65\%$ to $1.42\%$). Tabata group showed significant decreases in LDL (−$1.95\%$, $95\%$ CI: −$3.53\%$ to −$0.38\%$), TG (−$1.08\%$, $95\%$ CI: −$2.85\%$ to $0.69\%$) and TC (−$1.07\%$, $95\%$ CI: −$2.00\%$ to −$0.13\%$), whereas control group demonstrated no changes in LDL and TG and a significant increase in TC ($0.90\%$, $95\%$ CI: $0.25\%$ to $1.55\%$). A small intervention effect on LDL ($$p \leq 0.003$$, d = -0.57, $95\%$ CI: −0.93 to -0.19) and a moderate effect on TC ($$p \leq 0.001$$, d = −0.64, $95\%$ CI: −1.00 to −0.26) were observed.
Subgroup analysis showed significant differences in LDL ($p \leq 0.001$, d = −1.51, $95\%$ CI: −2.22 to −0.76), TG ($$p \leq 0.007$$, d = −0.98, $95\%$ CI: −1.67 to −0.26) and TC ($p \leq 0.001$, d = −1.42, $95\%$CI: −2.12 to -0.67) between overweight/obese participants and their normal weight counterparts. The effects were large in LDL and TC, and the effect was moderate in TG. Significant improvements in LDL, TG and TC were only observed in overweight/obese participants9, with a decrease of $8.59\%$ ($95\%$ CI: $2.10\%$ to $15.09\%$), $6.30\%$ ($95\%$ CI: $3.37\%$ to $9.23\%$) and $4.83\%$ ($95\%$ CI: $2.35\%$ to $7.31\%$) for LDL, TG and TC, respectively. Both overweight/obese participants ($13.65\%$, $95\%$ CI: $6.73\%$ to $20.57\%$, $p \leq 0.001$) and their normal weight counterparts had a significant improvement in HDL ($7.50\%$, $95\%$ CI: $4.52\%$ to $10.48\%$, $p \leq 0.001$). However, there was no statistically significant difference on the percentage change on HDL between groups ($$p \leq 0.090$$).
## 3.8 Carbohydrate metabolism and endocrine regulators
There were no significant changes in FPG and HbA1c for both groups, with no intervention effect. HOMA-IR improved significantly in the Tabata group (−$1.24\%$, $95\%$ CI: −$2.50\%$ to $0.03\%$) after intervention. There was a small intervention effect on HOMA-IR between groups ($$p \leq 0.026$$, d = −0.42, $95\%$ CI: −0.78 to −0.05). Similarly, a small intervention effect was observed on fasting insulin ($$p \leq 0.035$$, d = −0.40, $95\%$ CI: −0.76 to −0.03). There was a $0.83\%$ ($95\%$ CI: −$0.17\%$ to $1.82\%$) decrease in fasting insulin from preintervention to postintervention for those in the Tabata group ($$p \leq 0.018$$).
Although FPG was not improved in the overall sample in the Tabata group, in the subgroup analysis it significantly decreased in overweight or obese participants (−$1.50\%$, $95\%$ CI: −$2.92\%$ to −$0.09\%$, $$p \leq 0.034$$). There was a significant difference in percentage change in HbA1c between the overweight/obese group and the normal weight group ($$p \leq 0.008$$, d = −0.97, $95\%$ CI: −1.66 to −0.25). The effect was moderate. HOMA-IR and fasting insulin were improved only in the overweight or obesity group (−$3.84\%$, $95\%$ CI: −$6.11\%$ to −$1.57\%$, $$p \leq 0.009$$ for HOMA-IR; −$2.36\%$, $95\%$ CI: −$4.52\%$ to −$0.19\%$, $$p \leq 0.042$$), while no effects on weight status was observed ($$p \leq 0.061$$ for HOMR-IR; $$p \leq 0.166$$ for fasting insulin).
## 3.9 Inflammation markers
There was no statistically significant change in CRP for both groups after the intervention, with no statistically significant intervention effect ($$p \leq 0.971$$). Subgroup analysis showed no significant improvement in CRP in either the overweight/obese or the normal weight group.
## 3.10 Metabolic syndrome
Following 12-week intervention, the prevalence of MetS decreased significantly in the Tabata group from $5.08\%$ to $0.00\%$, whereas it did not change significantly in the control group ($5.17\%$ vs $5.17\%$). Among the Tabata group, 27 ($79.41\%$) participants improved at least 1 component of MetS after training. Among the remaining seven participants, 2 of them had insufficient improvement of SBP, two participants had insufficient improvement of WC, 1 participant had insufficient improvement of TG and 1 participant had insufficient improvement of HDL. For the remaining 1 participant, both WC and HDL were not improved sufficiently to below cut points.
## 3.11 Physical activity
MVPA and TPA significantly increased in the Tabata group ($15.51\%$, $95\%$ CI: $13.27\%$ to $17.74\%$, $p \leq 0.001$ for MVPA; $14.92\%$, $95\%$ CI: $12.23\%$ to $17.51\%$ for TPA), whereas both MVPA and TPA decreased in the control group (−$2.95\%$, $95\%$ CI: −$4.88\%$ to −$1.03\%$, $$p \leq 0.001$$ for MVPA; −$2.03\%$, $95\%$ CI: −$3.85\%$ to $0.22\%$, $p \leq 0.001$ for TPA), with a large intervention effect between groups ($p \leq 0.001$, $d = 2.31$, $95\%$ CI: 1.83 to 2.77).
Subgroup analysis showed that there were significant differences in percentage change on MVPA ($$p \leq 0.004$$, $d = 1.05$, $95\%$ CI: 0.33 to 1.74) and TPA ($$p \leq 0.005$$, $d = 1.02$, $95\%$ CI: 0.30 to 1.71) between overweight/obese and normal weight groups after the intervention. Both effects were moderate. MVPA significantly increased by $22.50\%$ ($95\%$ CI: $17.76\%$ to $27.24\%$) and $14.08\%$ ($95\%$ CI: $11.71\%$ to $16.45\%$) for participants with overweight/obesity and normal weight, respectively ($p \leq 0.001$). TPA significantly increased by $22.84\%$ ($95\%$ CI: $17.07\%$ to $28.60\%$) and $13.30\%$ ($95\%$ CI: $10.56\%$ to $16.04\%$) for participants with overweight/obesity and normal weight, respectively ($p \leq 0.001$).
## 3.12 Dietary data
After intervention, both the Tabata group and control group showed slightly but not significant increases in dietary intake including energy intake, vegetable, and fruit intake. There were no intervention effects. While in the sub-group analysis, overweight/obese participants significantly decreased daily energy intake by $5.78\%$ ($95\%$ CI: $0.24\%$ to $11.31\%$) and increased vegetable intake by $5.37\%$ ($95\%$ CI: $0.49\%$ to $10.25\%$). There were significant differences on daily energy intake ($$p \leq 0.006$$, d = −1.00, $95\%$ CI: −1.69 to −0.28) and vegetable intake ($$p \leq 0.002$$, $d = 1.10$, $95\%$ CI: 0.38 to 1.80) between the overweight/obese participants and the normal weight ones. Both effects were large. For daily fruit intake, only normal weight participants showed a significant increase ($1.15\%$, $95\%$ CI: −$2.85\%$ to $5.16\%$, $$p \leq 0.022$$), with weight status effect ($$p \leq 0.803$$).
## 3.13 Correlation data
At baseline, body composition parameters including BMI, WC, %BF, and FM, were significantly and positively associated with BMR, LDL, TG, TC, HbA1c, fasting insulin, HOMA-IR, CRP. VO2max was significantly associated with weight (r = −0.221), BMI ($r = 0.209$), FFM ($r = 0.228$), MVPA ($r = 0.398$) and HRresting (r = −0.339). Body composition variables were highly correlated with each other. Regarding to PA data, MVPA was significantly associated with TPA ($r = 0.406$), HRresting (r = −0.186), SBP (r = −0.295), HDL ($r = 0.630$), LDL ($r = 0.210$), and TC ($r = 0.203$). While TPA was only observed to be significantly associated with HDL ($r = 0.391$). For dietary data, daily energy intake was significantly associated with weight ($r = 0.382$), BMI ($r = 0.458$), WC ($r = 0.511$), %BF ($r = 0.273$), FM ($r = 0.369$), FFM ($r = 328$), BMR ($r = 0.222$), DBP (r = −0.199), LDL ($r = 0.314$), TG ($r = 0.220$), TC ($r = 0.328$), HbA1c ($r = 0.252$), fasting insulin ($r = 0.209$), HOMA-IR ($r = 0.212$) and CRP ($r = 0.235$). Vegetable intake was significantly associated with SBP (r = −0.337) and HDL ($r = 0.188$). There were significant and negative associations between fruit intake and CRP (r = −0.211). It should be noted that there was a strong correlation between the percentage change of MVPA and the percentage of TPA ($r = 0.936$, $p \leq 0.001$).
Correlation data was used to determine the highly collinear variables, which were removed from the regression model to avoid potential multicollinearity.
## 3.14 Regression analysis
According to the literature review, age, body composition (weight, BMI, %BF, FM, and FFM), cardiorespiratory fitness (VO2max), PA (MVPA and TPA), and dietary intake (daily energy intake, daily vegetable, and fruit intake) were all included in the regression model. Additionally, to evaluate the effect of the intervention, both the baseline value and the percentage change of covariables were included in the model. We also adjusted for the baseline value of cardiometabolic outcomes.
Regression analysis for cardiometabolic indicators were outlined in Table 4. In Model 1, after controlling for potential variables, neither the baseline value of MVPA nor TPA were associated with the percentage change of SBP (ΔSBP), HDL (ΔHDL), LDL (ΔLDL), TG (ΔTG), or TC (ΔTC). The baseline value of MVPA was significantly associated with the percentage change of fasting insulin (Δfasting insulin) ($b = 0.278$, $$p \leq 0.049$$) and HOMA-IR (ΔHOMA-IR) ($b = 0.287$, $$p \leq 0.020$$).
**TABLE 4**
| Dependent variable | Dependent variable.1 | Independent variable | Standardized beta | p-value | Δ R 2 |
| --- | --- | --- | --- | --- | --- |
| Δ HDL | Model 1: adjusted R 2 = 0.601 | TPA | -0.171 | 0.112 | |
| Δ HDL | | MVPA | 0.018 | 0.900 | |
| Δ HDL | Model 2: adjusted R 2 = 0.692 | TPA | 0.121 | 0.323 | |
| Δ HDL | | MVPA | -0.040 | 0.751 | |
| Δ HDL | | Δ TPA | 0.480 | 0.001 | 0.074 |
| Δ HDL | Model 3: adjusted R 2 = 0.645 | TPA | 0.014 | 0.912 | |
| Δ HDL | | MVPA | -0.052 | 0.704 | |
| Δ HDL | | Δ MVPA | 0.326 | 0.015 | 0.039 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HDL |
| Δ LDL | Model 1: adjusted R 2 = 0.228 | TPA | 0.060 | 0.683 | |
| Δ LDL | | MVPA | 0.296 | 0.080 | |
| Δ LDL | Model 2: adjusted R 2 = 0.251 | TPA | 0.127 | 0.503 | |
| Δ LDL | | MVPA | 0.336 | 0.048 | |
| Δ LDL | | Δ TPA | -0.309 | 0.131 | 0.031 |
| Δ LDL | Model 3: adjusted R 2 = 0.219 | TPA | -0.021 | 0.911 | |
| Δ LDL | | MVPA | 0.307 | 0.073 | |
| Δ LDL | | Δ MVPA | -0.309 | 0.131 | 0.007 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, LDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, LDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, LDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, LDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, LDL | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, LDL |
| Δ TG | Model 1: adjusted R 2 = 0.254 | TPA | 0.094 | 0.542 | |
| Δ TG | | MVPA | 0.087 | 0.546 | |
| Δ TG | Model 2: adjusted R 2 = 0.241 | TPA | 0.021 | 0.912 | |
| Δ TG | | MVPA | 0.109 | 0.490 | |
| Δ TG | | Δ TPA | -0.116 | 0.604 | 0.004 |
| Δ TG | Model 3: adjusted R 2 = 0.237 | TPA | 0.106 | 0.566 | |
| Δ TG | | MVPA | 0.092 | 0.556 | |
| Δ TG | | Δ MVPA | 0.033 | 0.869 | 0.000 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TG | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TG | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TG | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TG | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TG | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TG |
| ΔTC | Model 1: adjusted R 2 = 0.499 | TPA | 0.208 | 0.083 | |
| ΔTC | | MVPA | 0.051 | 0.702 | |
| ΔTC | Model 2: adjusted R 2 = 0.488 | TPA | 0.178 | 0.255 | |
| ΔTC | | MVPA | 0.059 | 0.669 | |
| ΔTC | | Δ TPA | -0.049 | 0.767 | 0.001 |
| ΔTC | Model 3: adjusted R 2 = 0.499 | TPA | 0.298 | 0.049 | |
| ΔTC | | MVPA | 0.036 | 0.79 | |
| ΔTC | | Δ MVPA | 0.154 | 0.312 | 0.009 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TC | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TC | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TC | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TC | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TC | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, TC |
| Δ SBP | Model 1: adjusted R 2 = 0.780 | TPA | 0.003 | 0.965 | |
| Δ SBP | | MVPA | 0.033 | 0.697 | |
| Δ SBP | Model 2: adjusted R 2 = 0.788 | TPA | 0.110 | 0.281 | |
| Δ SBP | | MVPA | -0.001 | 0.993 | |
| Δ SBP | | Δ TPA | 0.174 | 0.111 | 0.010 |
| Δ SBP | Model 3: adjusted R 2 = 0.779 | TPA | 0.054 | 0.581 | |
| Δ SBP | | MVPA | 0.024 | 0.777 | |
| Δ SBP | | Δ MVPA | 0.087 | 0.392 | 0.003 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, SBP | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, SBP | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, SBP | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, SBP | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, SBP | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, SBP |
| Δ fasting insulin | Model 1: adjusted R 2 = 0.391 | TPA | -0.139 | 0.287 | |
| Δ fasting insulin | | MVPA | 0.278 | 0.049 | |
| Δ fasting insulin | Model 2: adjusted R 2 = 0.388 | TPA | -0.239 | 0.171 | |
| Δ fasting insulin | | MVPA | 0.302 | 0.037 | |
| Δ fasting insulin | | Δ TPA | -0.170 | 0.381 | 0.008 |
| Δ fasting insulin | Model 3: adjusted R 2 = 0.400 | TPA | -0.263 | 0.110 | |
| Δ fasting insulin | | MVPA | 0.295 | 0.037 | |
| Δ fasting insulin | | Δ MVPA | -0.213 | 0.212 | 0.017 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, fasting insulin | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, fasting insulin | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, fasting insulin | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, fasting insulin | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, fasting insulin | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, fasting insulin |
| Δ HOMA-IR | Model 1: adjusted R 2 = 0.539 | TPA | -0.105 | 0.358 | |
| Δ HOMA-IR | | MVPA | 0.287 | 0.020 | |
| Δ HOMA-IR | Model 2: adjusted R 2 = 0.543 | TPA | -0.217 | 0.150 | |
| Δ HOMA-IR | | MVPA | 0.316 | 0.013 | |
| Δ HOMA-IR | | Δ TPA | -0.197 | 0.251 | 0.011 |
| Δ HOMA-IR | Model 3: adjusted R 2 = 0.544 | TPA | -0.207 | 0.147 | |
| Δ HOMA-IR | | MVPA | 0.302 | 0.015 | |
| Δ HOMA-IR | | Δ MVPA | -0.178 | 0.23 | 0.012 |
| Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HOMA-IR | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HOMA-IR | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HOMA-IR | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HOMA-IR | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HOMA-IR | Model 1: adjusted for age, BMI, ΔBMI, VO2max, ΔVO2max, energy intake, Δ energy intake, vegetable, Δ vegetable, fruit, Δ fruit, HOMA-IR |
In Model 2, adding the percentage change of TPA (ΔTPA) significantly increased the explained variation in ΔHDL to $69.2\%$ (Adjusted R 2 = 0.692). ΔTPA was independently associated with ΔHDL ($b = 0.480$, $$p \leq 0.001$$), accounting for $7.4\%$ of the variation. For every $1\%$ increase in TPA, HDL improved by $0.5\%$. The baseline value of MVPA was significantly associated with ΔLDL ($b = 0.336$, $$p \leq 0.048$$), Δfasting insulin ($b = 0.302$, $$p \leq 0.037$$), and ΔHOMA-IR ($b = 0.316$, $$p \leq 0.013$$) in Model 2. While ΔTPA was not associated with ΔSBP, ΔLDL, ΔTG, or ΔTC.
In Model 3, the percentage change of MVPA (ΔMVPA), significantly increased the explained variation in ΔHDL to $64.5\%$ (Adjusted R 2 = 0.645). ΔMVPA was independently associated with ΔHDL ($b = 0.326$, $$p \leq 0.015$$), accounting for $3.9\%$ of the variation. For every $1\%$ increase in MVPA, HDL improved by $0.398\%$. The baseline value of TPA was significantly associated with ΔTC ($b = 0.298$, $$p \leq 0.049$$) in model 3. Furthermore, the baseline level of MVPA was significantly associated with Δfasting insulin ($b = 0.326$, $$p \leq 0.015$$) and ΔHOMA-IR ($b = 0.302$, $$p \leq 0.015$$).
## 4 Discussion
Even though there is accumulating evidence for the health benefits of HIIT in adults, the most consistent benefits had been seen in improving cardiorespiratory fitness. The benefits of HIIT on cardiometabolic health remain controversial. Particularly, there was limited data on the effects of HIIT on habitual PA. Since PA was found to be associated with cardiometabolic risk factors, it remains unknown whether PA played a mediating role on the effectiveness of HIIT. Furthermore, the effectiveness of a Tabata-style functional HIIT utilizing very short intervals and the exercise modality other than running or cycling on favorable changes in health has not been fully explored. The primary aim of the present study, therefore, was to examine the effects of a 12-week Tabata-style functional HIIT program on cardiometabolic risk factors and PA levels in female university students. The Tabata-style functional HIIT involved eight bouts of 20-s “all-out” functional exercises, intermitted by 10-s rest between each bout. In terms of exercise adherence, only 1 student dropped out due to the hypoglycemia during the first session. There was a satisfied adherence that $98.33\%$ of participants attended all sessions. The fidelity of the intervention, which was evaluated by heart rate responses during exercises, was largely upheld since a high intensity was delivered to all of the participants (between-subject SD), consistently throughout the 12-week intervention (within-subject SD). The variation in both HRmean and HRpeak across different exercise sessions were small ($0.24\%$ for HRmean and $0.30\%$ for HRpeak), indicating that the exercise remained relatively consistent across the sessions. Furthermore, we did not find any difference on both HR measures between sessions. This might have been due to the familiarization session prior to the intervention.
After the 12-week intervention, compared to the control group, favorable intervention effects were observed in the Tabata group on cardiorespiratory fitness, most variables of body composition, some outcomes of cardiometabolic risk factors, and daily MVPA and TPA. These findings extended beneficial effects reported by previous studies and moreover, they were of particular importance from a perspective of health promotion in emerging adults, since the increasing prevalence of MetS and physical inactivity were observed in this population (Ford et al., 2004; Hirode and Wong, 2020; Tcymbal et al., 2020). Collectively, the Tabata-style functional HIIT provided a feasible and effective strategy to improving young women’s cardiometabolic health and habitual PA in the university setting.
## 4.1 Cardiorespiratory fitness effect
The improvement in cardiorespiratory fitness measured by VO2max after HIIT were consistently reported by previous studies (Batacan et al., 2017; Jelleyman et al., 2015; Kessler et al., 2012). It was supported by our finding that VO2max increased by $12.87\%$ ± $7.16\%$ in the Tabata group after intervention. Likewise, previous studies based on young adults showed similar improvements. De Revere et al. [ 2021] investigated the effect of a 3-week cycling-based HIIT protocol in non-obese and inactive women. Participants were required to complete 8–10 sets of 1-min workout followed by 75-s recovery. After a total of nine sessions, VO2max increased about $10\%$. In the study by Menz et al. [ 2019], following a 4-week Tabata-style HIIT protocol with a total of 14 sessions, participants (5 females and 2males) improved their VO2max by $11\%$ ± $7\%$ (Menz et al., 2019). However, compared with our protocol, the exercise volume was higher in De Revere et al. [ 2021]; Menz et al. [ 2019]’s protocols, which was about 16 min per session. With a higher training volume, it was not surprising that participants were able to benefit from the HIIT program with short duration. Indeed, evidence from a previous systematic review showed that the improvement in VO2max can be achieved following 2 weeks of HIIT with few exercise sessions (Kessler et al., 2012). Although the longer intervention duration appeared to contribute to additional increases in VO2max (Milanović et al., 2015), it was not true in the present study. One of the potential explanations were the low training volume of each session. In our protocol, the total workout duration per session was just 4 min, which was only a quarter of Menz et al. [ 2019]’s and one-fifth of De Revere et al. [ 2021]’s protocol. Moreover; Rosenblat et al. [ 2022] suggested that although the improvement in VO2max seemed similar between protocols with different interval types, those with shorter intervals (2s–60s) were more likely to increase skeletal capillary density and mitochondrial respiration. This might facilitate the ultimate improvements in whole body exercise capacity and endurance in untrained people (Jacobs et al., 2013). A greater improvement of $18\%$ was reported by Foster et al. [ 2015] after an 8-week (24 sessions) cycling-based Tabata. The original exercise intensity for Tabata training of $170\%$ VO2max was used. Although an additional increase in VO2max were obtained under the high intensity stimulation, it resulted in a negative affective response in the participants (Foster et al., 2015). It was the fact that the feasibility of the original intensity of $170\%$ VO2max was questioned in the real-world setting. The result of this study revealed that Tabata protocol utilizing a modified lower intensity ($83.24\%$ of HRmax) was an alternative to effectively improve cardiorespiratory fitness in untrained individuals.
In contrast, in the work by Islam er al. [ 2020], participants completed a conventional 4-min Tabata training 4 times a week for 4 weeks. The exercise modality was whole body functional exercises including burpee push-ups, mountain climber push-ups, jumping jacks and squat and thrusts. After intervention, no significant improvement on VO2max was observed (Islam et al., 2020). This might be due to the short duration of the intervention, as well as the better cardiorespiratory fitness of participants at baseline. Baseline cardiorespiratory fitness and initial training status were found to be associated with the training effects (Milanović et al., 2015; Støren et al., 2017).
Several studies examined the effects on cardiorespiratory fitness in overweight and obese individuals. After a 5-week HIIT intervention with a total of 20 sessions, VO2max increased by $7.9\%$ in obese young women (Kong et al., 2016). In Kong et al. [ 2016]’s protocol, each session lasted 20 min and comprised of 60 repeats of 8-s cycling followed by 12-s rest. The HRmean over the intervention was 164 ± 8 bpm ($81\%$ of age predicted HRmax). In the study by Hu et al. [ 2021], a high volume of HIIT protocol was used. Each session involved 4 min of cycling at $90\%$ of VO2max followed by 3 min rest for a total of 60 min, with a frequency of 3 times a week. Following 36 training sessions, the obese participants had a significant increase of $20\%$ in VO2max (Hu et al., 2021). The study be Sun et al. [ 2019] reported a greater improvement of $25\%$ in VO2max in overweight young females following a 12-week HIIT protocol with a frequency of 3 times per week. During each session, participants were required to complete nine sets of 4-min cycling ($90\%$ of VO2max) followed by 3-min rest (Sun et al., 2019). Despite the low volume in the present study, in line with previous studies, findings from our sub-group analysis revealed that overweight and obese participants had a significant increase in VO2max after the intervention ($16.98\%$ ± $6.01\%$). Furthermore, we found that the percentage increase in relative VO2max differed statistically significantly between elevated- and normal-weight participants. However, absolute VO2max showed no differences between groups. This might be due to the larger decrease on weight in the overweight/obese group. There was limited data on the direct comparison of the effects on cardiorespiratory fitness between normal weight adults and overweight or obese adults. Findings from a systematic review and meta-analysis showed that short-term HIIT (<12 weeks) had a large effect on improving VO2max in normal weight adults, while a medium effect in overweight or obese adults. Meta-analysis was also available for the effect of long-term HIIT (≥12 weeks) in overweight or obese adults and the pooled result showed a large effect in overweight or obese adults (Batacan et al., 2017). It suggested that the duration of intervention was positively associated with the effectiveness, at least for overweight or obese populations. When studying the mechanism associated with the improvements on VO2max, central factors and peripheral factors should be considered. After HIIT, plasma volume, lest ventricular mass, maximal stroke volume, and maximal cardiac output were increased. In addition to central adaptations, capillary density, maximal citrate synthase activity and mitochondrial respiration were increased (Rosenblat et al., 2022). These physiological adaptations were responsible for the improvements on VO2max.
## 4.2 Body composition effect
Despite the increasing popularity of HIIT for weight and fat loss, its effectiveness remains controversial. Our results demonstrated that a 12-week Tabata-style functional HIIT was effective on reducing %BF and FM and increasing FFM. There were no changes in weight, BMI, or WC. The results from the current study indicated that HIIT was effective for fat loss but not for weight loss. It was in line with previous studies (Macpherson et al., 2011; Zhang et al., 2021). Macpherson et al. [ 2011] used a typical running-based HIIT that involved four to six sets of 30-s sprint followed by 4-min recovery. After 6 weeks’s intervention (3 times per week), %BF and FM decreased significantly and FFM increased significantly, but body mass was unchanged (Macpherson et al., 2011). The fat-reducing effect was also supported by Zhang et al. [ 2021]’s study, in which the reductions in whole-body and regional FM were reported after 12-week’s HIIT intervention in obese young women (Zhang et al., 2021). This was in line with a previous systematic review and meta-analysis that HIIT were able to significantly reduce total, abdominal and visceral fat mass (Maillard et al., 2018). However, the authors indicated that the reduction in abdominal fat mass could only be detected by computed tomography scan or magnetic resonance imaging. This might account for the absence of significant change in WC in the present study. Furthermore, only the HIIT protocol utilizing the exercise modality of running or cycling were included, and our results expanded the knowledge of the efficacy of HIIT in reducing fat. Non-etheless, there was controversy over whether HIIT was effective in lowering fat. Proponents argued that HIIT increased both aerobic and anaerobic capacity, reduced insulin resistance, and thus increased fat oxidation (Boutcher, 2011). On the contrary, the counterargument was that when exercises were performed at an intensity of $85\%$ of VO2max or greater, fat had little to do with energy, and metabolic energy comes almost exclusively from the breakdown of sugars in the body (Achten and Jeukendrup, 2004; Venables et al., 2005). From the perspective of energy balance, we believed that without controlling total energy expenditure and intake, it was hard to determine whether the fat-lowering effect of HIIT was caused by training itself or by dietary intake or habitual physical activity. Well-controlled studies are expected in the future.
However, the simultaneous loss of weight and body fat after HIIT training has been reported in several studies. Trapp et al. [ 2008] investigated the effects of a 15-week cycling-based HIIT on fat loss in young normal weight women. Participants performed 8-s sprinting followed by 12-s recovery for 60 repeats. After a total of 45 session (20 min each session), the body mass, fat mass, and %BF were significantly reduced (Trapp et al., 2008). In the study by Tjønna et al. [ 2008], participants completed a total of 48 sessions of HIIT ($90\%$ of HRmax) with the frequency of three sessions a week. After 16 weeks’ intervention, body mass and fat were significantly reduced (Tjønna et al., 2008). Given the fact that there was no change in energy intake between pre- and post-tests, the absence of reduction on body mass might be due to the following reasons: 1) the low exercise volume resulted in low energy expenditure, which was not sufficient to induce energy deficit and further reducing weight; 2) participants were normal weight at baseline and most favorable effects on body mass were observed in participants with overweight or obese (Tjønna et al., 2008; Martins et al., 2016; D’Amuri et al., 2021). It was supported by findings from our subgroup analysis that body mass, BMI and WC significantly decreased in overweight/obese participants after intervention whereas these variables did not change in normal weight participants. Although the weight-lowering effect of the Tabata-style functional HIIT was not significant in the present study, it appeared to be a time-efficient way to prevent the abnormal weight gain among freshmen. Moreover, results from the present study demonstrated that overweight/obesity had an effect on weight change but did not affect the association between Tabata training and weight change. The greater weight-lowering effect observed in overweight/obese participants might be attributed to their larger increase in PA and decrease in energy intake compared to normal weight counterparts. This was further reinforced by acknowledging that the role of exercise training in the maintenance or improvement of weight was predominantly influenced by the cumulative effect of energy deficit during the daily life (LaForgia et al., 2006).
On the contrary, a systematic review and meta-analysis evaluated the effect of low-volume HIIT on body composition and reported that improvements on body composition outcomes such as FM, %BF or FFM were hardly observed following low-volume HIIT (Sultana et al., 2019). In the present study, the favorable effects observed on some body composition measures might be partially explained by the increased daily MVPA and TPA. According to the meta-analytical findings from our previous study, there was a moderate correlation between TPA and %BF (Lu et al., 2022b). The study also indicated that the improvement on adiposity could be seen when PA performed at moderate or higher intensity. On the other hand, the adaptations of fat in response to low-volume HIIT suggested a different underlying mechanism for fat reduction with MICT. The fat reduction after low-volume HIIT was not likely dependent on the amount of energy expended during exercise sessions. This might be attributed to the larger improvement on the metabolic rate and fat expenditure post intervention, because the magnitude and duration of excess post-exercise oxygen consumption was greater after HIIT (LaForgia et al., 2006) and lipolytic hormones, such as catecholamines and growth hormone, have been reported to increase with exercise intensity (McMurray et al., 1987). Moreover, HIIT was found to elicit a larger elevation of plasma catecholamines compared with steady-state exercise. This potentially facilitated fat reduction after HIIT (Zouhal et al., 2008).
## 4.3.1 Blood pressure
Aerobic exercise was well documented to reduce resting BP and was recommended in the primary and secondary prevention of CVDs (Cornelissen and Smart, 2013; Johnson et al., 2014). While there was emerging evidence from intervention studies that HIIT was effective on improving resting SBP (Nybo et al., 2010; Holloway et al., 2018; Aghaei Bahmanbeglou et al., 2019; de Oliveira et al., 2020) or both SBP and DBP (Ciolac et al., 2010; Hu et al., 2022). Results from the present study showed that only SBP was significantly decreased after 12-week intervention. This agreed with several previous studies.
In the work by Aghaei Bahmanbeglou et al. [ 2019], participants with stage 1 hypertension completed either short interval HIIT (work rest ratio: 30-s/30-s at $80\%$–$100\%$ of VO2max) or long interval HIIT (work rest ratio: 4-min/4-min at $75\%$–$90\%$ of VO2max) for a total of 8 weeks. After the intervention, SBP was significantly decreased in both short interval HIIT and long interval HIIT, suggesting that the SBP-lowing effect of HIIT was irrespective of the intensity and exercise interval (Aghaei Bahmanbeglou et al., 2019). Similarly, de Oliveira et al. [ 2020] also reported a significant decrease in SBP but not in DBP after an 8-week’s HIIT intervention in young obese women with elevated BP at baseline. The training protocol involved four bouts of 4-min high-intensity running at $85\%$–$95\%$ of HRmax, followed by 3-min active recovery at 65–75 of HRmax (de Oliveira et al., 2020). It seemed that HIIT was effective in improving SBP in young and middle-aged individuals with abnormal SBP. It was supported by a recent systematic review and meta-analysis by Costa Lêdo et al. [ 2018]. The authors reported that HIIT was equally effective in reducing BP compared to MICT in participants with pre- and established hypertension (Costa Lêdo et al., 2018). Nevertheless, inconsistent with our results, this systematic review suggested that DBP could also be improved by HIIT. The baseline value of DBP might explain the inconsistence because higher baseline values were more likely to be improved by exercises (Bravata et al., 2007).
In the subgroup analysis, we observed significant decreases in both overweight/obese group and normal weight group and there was no between-group difference. Most studies evaluated the effectiveness of HIIT in overweight or obese participants. A systematic review and meta-analysis examined the effect of HIIT in overweight/obese and normal weight population (Batacan et al., 2017). The results indicated the difference in the effect of HIIT on BP between participants with different BMI. The BP-lowing effect of HIIT was only observed in overweight/obese participants. However, our results showed that normal weight participants with elevated SBP could also benefit from HIIT. This was confirmed by previous studies that the degree of BP reduction was related to its baseline value (Pescatello et al., 2004; Bravata et al., 2007). A greater reduction on BP were found in participants with higher baseline BP readings.
However, a recent study provided opposite results that functional HIIT was not effective in reducing BP (Nunes et al., 2022). In Nunes et al. [ 2022]’s protocol, participants were required to complete 10 sets of 60-s of functional exercise followed by 60-s active recovery. After 12 weeks’ intervention (36 sessions), neither SBP nor DBP were improved significantly. The lack of a significant reduction in BP might be explained by the age of participants. In Nunes et al. [ 2022]’s study, participants were postmenopausal women with the mean age of 61.5 years, whereas participants in our study were young females with the age of 20.42 years. On one hand, SBP and DBP increased with age (Landahl et al., 1986). On the other hand, postmenopausal women were at high risk of hypertension due to the decline in estrogen (Saeed et al., 2017). Collectively, it seemed reasonable that post-intervention BP was not improved in older women after low-volume HIIT.
*In* general, BP improvements were more likely to be observed in aerobic, resistance and concurrent training (moderate-intensity aerobic exercise and high intensity resistance exercise) with a volume of 150 min per week (Sabbahi et al., 2016; Corso et al., 2016; Son et al., 2017). Our finding supported the favorable effect on SBP following low-volume HIIT. This favorable change might be due to the high intensity achieved during exercises (Eicher et al., 2010). Higher intensity was reported to be associated with greater acute reduction on BP following exercises, which contributed to chronic BP lowing responses (Liu et al., 2012). From a physiological perspective, several mechanisms had been proposed for BP reduction after aerobic training, such as improved vascular function, lowered inflammation, and oxidative stress. The work by Sawyer et al. [ 2016] suggested different vascular adaptations between HIIT and MICT (Sawyer et al., 2016). HIIT was found to increase brachial artery flow-mediated dilation (Tjønna et al., 2008) while MICT increased resting artery diameter and low flow-mediated constriction. These vascular adaptations could occur without improvements in body composition, which further supported our findings. Although only an improvement in SBP was detected after the intervention, it had important implications for CVD risk factors management, as a 10 mm Hg increase in SBP during young adulthood was found to be associated with a $14\%$ increased risk of CVD mortality over a 41-year follow-up (McCarron et al., 2000). However, whether such HIIT protocol could be used in the clinical setting to improve the BP in participants with established hypertension need further investigations.
## 4.3.2 Lipid profiles
Our findings suggested favorable effects on HDL, LDL, and TC after intervention, while all these improvements were observed in participants with overweight and obesity. We also found intervention × weight status interaction effects for LDL, TC and TG. This might be due to the higher pre-test value involved in the overweight/obese group. Our findings were in line with a previous study by Tjønna et al. [ 2008], in which HDL increased significantly after a 16-week (48 sessions) HIIT with an exercise intensity of $90\%$ of HRmax in overweight/obese participants (Tjønna et al., 2008). Our findings were partly supported by a systematic review and meta-analysis that neither short-term nor long-term HIIT had significant effects on cardiometabolic risk factors in normal weight participants (Batacan et al., 2017). However, the authors indicated that the lipid profile was not improved in overweight/obese participants neither. Findings from another systematic review reported the same results that TC, TG, HDL, or LDL were not improvement after HIIT (Kessler et al., 2012).
Few studies examined effects on lipid biomarkers after HIIT in normal weight participants. Nevertheless, normal weight obesity had gained increasing attention in recent years and a study by Hu et al. [ 2022] investigated the effects of HIIT in this population. The HIIT protocol used in Hu et al. [ 2022]’s study involved three sets of 9-min workout at $90\%$ of HRmax followed by 1-min rest, with a high frequency of 5 days per week. After 4 weeks’ training, TC, TG, LDL and HDL were significantly improved in young women with NOW (Hu et al., 2022). Although the intervention duration was only 4 weeks, which was one-third of that in the present study, its training volume was as high as 1350 MET-min/week. This volume was sufficient to see a meaningful amelioration in lipid levels (Mann et al., 2014).
Most beneficial effects of HIIT on lipid profiles were reported among overweight/obese men. In the work by Fisher et al. [ 2015], after 6 weeks’ intervention, TC, TG, LDL, and HDL were improved in young men with overweight or obesity (Fisher et al., 2015). A previous study reported similar results that HDL increased after an 8-week HIIT program in untrained young men. However, TC was unchanged. It was believed that HDL was the most easily improved lipid profile component from exercise (Mann et al., 2014). This was supported by evidence from the study by Nybo et al. [ 2010]. The authors indicated that TC/HDL ratio was the only index that improved significantly after 150 min of MICT weekly at $65\%$ of VO2max for 12 weeks in untrained young men. Additionally, the authors compared the MICT with HIIT (40 min workout at $95\%$ HRmax weekly) and there were no improvements in lipid profiles after HIIT (Nybo et al., 2010). This suggests that the volume of exercise, rather than the intensity of exercise, was the key to improving blood lipids and a relationship between body composition (body mass and %BF decreased only in MICT) and blood lipids was proposed. Similar findings were reported by Ho et al. [ 2012] that only combination exercise, after which weight, %BF, and FM decreased, had a beneficial effect on lipid profiles including TG, TC, HDL, and LDL (Ho et al., 2012). This was supported by a previous systematic review that weight loss provided significant favorable changes on blood lipid (Aucott et al., 2011). Therefore, despite the low training volume, the favorable effects on blood lipid observed in our study might be due to the reduction on body mass and fat.
## 4.3.3 Carbohydrate metabolism and fasting insulin
Findings from the current study revealed that FPG and HbA1c were not significantly decreased after the Tabata-style functional HIIT, but HOMA-IR and fasting insulin decreased significantly. The improvements on fasting insulin and HOMA-IR observed in the present study were supported by a previous systematic review and meta-analysis (Jelleyman et al., 2015). Jelleyman et al. [ 2015] evaluated the effects of HIIT on biomarkers of glucose regulation and insulin resistance and meta-analytical findings demonstrated that compared to the control and MICT, insulin resistance significantly reduced following HIIT. The significant reduction on FPG only occurred in participants with elevated FPG value or diagnosed type 2 diabetes. This may help to explain the lack of advancement of FPG reduction in the present study as the baseline FPG value was normal in our samples. In contrast to our finding, HbA1c decreased significantly following HIIT compared to control. Another systematic review and meta-analysis provided similar finding related to insulin resistance, but FPG was not improved after HIIT (Kessler et al., 2012). These results appeared to be more consistent with the amelioration of insulin resistance following HIIT. Particularly, a previous study by Babraj et al. [ 2009] indicated that insulin sensitivity was improved following as short as 2 weeks of HIIT in normal weight adults. Unfortunately, this conclusion was based on males exclusively (Babraj et al., 2009). Contrast to the present study, Arad et al. [ 2015] reported that insulin sensitivity was not changed significantly after a 14-week HIIT in overweight/obese women (Arad et al., 2015). This discrepancy might be due to fact that neither weight nor fat were reduced in participants from Arad et al. [ 2015]’s study. It was evidenced by other studies that exercise training did not increase insulin sensitivity without weight and fat loss, whether that weight and fat loss is exercise-induced or diet-related (Ross et al., 2000; Gillen et al., 2013). However, a recent study indicated that both exercise training and weight loss (diet-induced) interventions improved insulin sensitivity. Body weight and fat mass were not significantly changed in participants in the exercise training group (Ryan et al., 2021). The authors also suggested that there were differential effects on signaling pathways in skeletal muscle between the exercise training and the diet-predominated weight loss intervention. It was supposed that exercise had an independent mechanism for improving insulin sensitivity and, in addition, might increase insulin sensitivity by losing weight. The latter might be explained from the perspective of energy expenditure, as evidence from previous studies suggests that total energy expenditure rather than exercise intensity is key to stimulating insulin sensitivity (Mayer-Davis et al., 1998). Previous studies studied physiological and molecular responses to a low-volume HIIT. Results from the Parolin et al. [ 1999]’s study showed that, during 3 × 30s all-out cycling, glycogen phosphorylase is predominantly activated during the first 15s of the first bout (Parolin et al., 1999). The study by Metcalfe et al. [ 2015] substantiated that the glycogen degradation with HIIT incorporating 20s all-out sprint was similar to that observed with those involving prolonged intervals (Metcalfe et al., 2015). The glycogen degradation was associated with the activation of AMPK (McBride and Hardie, 2009), which further contributed to the increase in peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) and glucose transporter 4 (GLUT 4) gene expressions (Gibala et al., 2009). As such, it remained unclear whether the improvement in insulin sensitivity was induced by the intervention. On the one hand, the intervention effect observed in our study did not control for changes of body weight. On the other hand, we did not control the timing of the post-test, which could occur 1–7 days after the last session. Since a previous study showed no difference on insulin sensitivity between pre-training and 4 days after 12 weeks of training (Ryan et al., 2020), early post-intervention measurement might contribute to a higher level of insulin sensitivity. In line with our subgroup’s finding in relation to FPG; Tjønna et al. [ 2008] reported that after 16-week HIIT (48 sessions), overweight/obese participants had improved FPG, but insulin was not changed significantly (Tjønna et al., 2008). While it should be noted that the overweight/obese participants recruited by Tjønna et al. [ 2008] had elevated FPG, which was not the case for our participants. Another study based on overweight/obese participants with normal FPG reported that FPG decreased after 5-week HIIT (20 sessions) in young women (Kong et al., 2016). In was consistent with findings from a previous systematic review and meta-analysis that FPG improved significantly after HIIT in overweight/obese participants (Kessler et al., 2012). The change on FPG after training appeared to be independent of the pre-training value, but rather related to overweight/obesity. Results from our study suggested that low-volume HIIT was effective in improving FPG for overweight/obese young women with normal FPG value. This finding was important as MHO was an instable and transient phenotype and individuals with MHO were more likely to develop CVD events in the future (Eckel et al., 2016).
## 4.4 Inflammation
It is well known that exercise could mitigate the deleterious effects of aging, not only by improving adiposity and mitochondrial function, the key to oxidative stress and inflammation, but also by enhancing the antioxidant and anti-inflammatory capacities (Sallam and Laher, 2016). Some researchers claimed that exercise performed at high intensities increased inflammation and oxidative stress rather than reduced them (Davies et al., 1982; Bergholm et al., 1999; Goto et al., 2003), while others suggested that acute bouts of exercise induced oxidative stress and inflammatory responses (Moldoveanu et al., 2001; Farias-Junior et al., 2019). Our results revealed that inflammation, measured by pro-inflammatory indices of CRP, was not significantly changed following a 12-week Tabata-style functional HIIT. Likewise, in the work by Allen et al. [ 2017], participants completed a 9-week cycling-based HIIT with a frequency of 3 times per week. There were no significant changes on inflammatory biomarkers including TNF-α and CRP after intervention (Allen et al., 2017). These were not surprising because on one hand, intense exercise induced oxidative stress was normally recovered within 24 h according to our previous systematic review (Lu et al., 2021); on the other hand, 12 weeks was not sufficient long to exert the long-term anti-oxidative and anti-inflammatory effects of exercise training. Long-term HIIT (12 months), accompanied by high levels of habitual PA was recommended to establish a significant anti-inflammatory effect (Balducci et al., 2010). Additionally, Balducci et al. [ 2010] claimed that exercise induced anti-inflammatory effect was independent of weight loss. This was further confirmed by the present study that although weight and fat reduced significantly among overweight/obese participants, their CRP did not change following intervention.
## 4.5 Physical activity
There was limited data on the effect of HIIT on habitual PA. According to previous study, short-term Tabata-style functional HIIT was able to increase MVPA and TPA (Lu et al., 2022a). The present study examined the long-term effect on PA and similar results were observed. This suggested that long-term low-volume HIIT did not induce compensatory movement behaviors such as decreasing habitual PA among young women. It might be due to the low energy expenditure during the exercises. Compared to the control group, whose daily PA was significantly decreased over 12 weeks, the Tabata-style functional HIIT seemed to be a time-efficient way to promote habitual PA in the university setting. Furthermore, we also found a high correlation between the increase in MVPA and TPA, suggesting that the majority of TPA increased over 12 weeks was accumulated from the increase in MVPA. Although all participants in our study met the PA recommendation for health maintenance, MVPA that higher than 300 min weekly could provide additional health benefits (Bull et al., 2020). Surprisingly, we found intervention × weight status interactions for MVPA and TPA. There were greater increases on MVPA and TPA in overweight/obese participants compared to normal weight counterparts. It was hard to explain. This might be explained by several psychological changes. The intervention might have a greater effect on the autonomous motivation and exercise in overweight/obese women, facilitating the internalization of exercise behavioral regulation (Silva et al., 2011). Comparative studies were warranted in the future. Moreover, to our knowledge, this was the first study to examine the association between changes in PA and in cardiometabolic outcomes. Our hypothesis that there was a positive relationship between changes in cardiometabolic outcomes and changes in PA was partially confirmed. Results from the regression analysis showed that increasing TPA and MVPA were both independently associated with improvements on HDL. However, other cardiometabolic indicators showed no associations with improvements in MVPA or TPA. Surprisingly, we found a positive association between the baseline value of MVPA and the percentage of fasting insulin and HOMA-IR, indicating that participants with higher level of daily MVPA had greater increase in fasting insulin and HOMA-IR. It was hard to explain and might be due to the statistical error. Evidence from cross-sectional studies revealed that MVPA was negatively associated with fasting insulin and HOMA-IR values (Green et al., 2014). Therefore, the lower baseline value of fasting insulin and HOMA-IR had the potential to result larger percentage change, and the slight increase in post-test values were induced by the measurement errors. We also found a positive relationship between the baseline value of TPA and ΔTC. It might be related to the dietary pattern which was not examined in the present study.
## 5 Limitations
This was the first study to evaluate the effects of a Tabata-style functional HIIT on multiple cardiometabolic outcomes and physical activity in university female students. The strengths of our study included the randomized controlled design with a relatively large sample, supervised exercise training and robust measures of cardiometabolic biomarkers in a clinical setting. There were several Limitations that should be noted. Firstly, it was noted that there were associations between dietary intake and multiple investigated cardiometabolic measures, however, the nature of these associations was not fully investigated and well controlled in this study. Secondly, the present study was conducted based on a population who were more likely to gain weight during their first year of university. Such weight gain was not only associated with lifestyle changes, but also with several psychological factors such as perceived stress (Economos et al., 2008; Hootman et al., 2018) and the influence of peers (Smith-Jackson and Reel, 2012). Therefore, future studies aimed at weight management, psychological factors should be taken into consideration. Furthermore, the inter-individual variability for exercise fidelity was not further investigated and might have limited the ability to evaluate the intervention effects. Finally, although long-term HIIT (≥12 weeks) were recommended to evaluate the effects of HIIT, the duration of 12 weeks was still too short to evaluate clinical changes and sustainability in certain physiological and cardiometabolic health outcomes. Particularly, due to the lack of follow-up, we were unable to determine whether participants were willing or able to commit to such low-volume HIIT for a long period of time. Future research needs to be rigorously designed to include follow-up measures that will confidently assist policymakers in recommending the Tabata-style functional HIIT to promote health in the university setting.
## 6 Conclusion
The findings of the present study demonstrated that a 12-week Tabata-style functional exercises based HIIT intervention improved the cardiorespiratory fitness, body composition, some cardiometabolic biomarkers (SBP, HDL, LDL, TC, fasting insulin, and HOMA-IR), as well as daily habitual PA (MVPA and TPA) in female freshmen. Most health benefits in relation to body composition and cardiometabolic risk factors were observed in overweight/obese individuals. Furthermore, this study extends the current knowledge, by showing that increases in habitual PA following intervention were associated with a greater improvement on HDL post intervention.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of Research Academy of Grand Health, Ningbo University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YL, HW, and JB contributed to conception and design of the study. YL and SY organized the database. YL and QW performed the statistical analysis. YL wrote the first draft of the manuscript. YL and SY wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer ZK declared a past co-authorship with the author JSB to the handling editor.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes
mellitus via microarray data analysis
authors:
- Xian-wen Ye
- Meng-nan Liu
- Xuan Wang
- Shui-qing Cheng
- Chun-shuai Li
- Yu-ying Bai
- Lin-lin Yang
- Xu-xing Wang
- Jia Wen
- Wen-juan Xu
- Shu-yan Zhang
- Xin-fang Xu
- Xiang-ri Li
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC10008874
doi: 10.3389/fnagi.2023.1071391
license: CC BY 4.0
---
# Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis
## Abstract
### Background
Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.
### Purpose
The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and DM.
### Methods
Download the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. The common differentially expressed genes (DEGs) were identified by limma package analysis. Then we analyzed the six kinds of module analysis: gene functional annotation, protein–protein interaction (PPI) network, potential drug screening, immune cell infiltration, hub genes identification and validation, and prediction of transcription factors (TFs).
### Results
The subsequent analyses included 339 common DEGs, and the importance of immunity, hormone, cytokines, neurotransmitters, and insulin in these diseases was underscored by functional analysis. In addition, serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis are closely related to both. DEGs were input into the CMap database to screen small molecule compounds with the potential to reverse AD and DM pathological functions. L-690488, exemestane, and BMS-345541 ranked top three among the screened small molecule compounds. Finally, 10 essential hub genes were identified using cytoHubba, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. For the characteristic Aβ and Tau pathology of AD, RAPGEF3 was associated significantly positively with AD and NF1 significantly negatively with AD. In addition, we also found ADCY5 and NF1 significant correlations with DM phenotypes. Other datasets verified that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers of DM complicated with AD. Meanwhile, the immune cell infiltration score reflects the different cellular immune microenvironments of the two diseases.
### Conclusion
The common pathogenesis of AD and DM was revealed in our research. These common pathways and hub genes directions for further exploration of the pathogenesis or treatment of these two diseases.
## Introduction
Type 2 diabetes mellitus (DM) is a complex disease characterized by insulin resistance, the neurodegenerative mechanisms are inflammation, endoplasmic reticulum stress, autophagy, and mitochondrial dysfunction (Burillo et al., 2021). The primary pathology of Alzheimer’s disease (AD) is the accumulation of amyloid β (Aβ) and tau hyperphosphorylation. Insulin action and impaired glucose metabolism are also involved in the occurrence and development of AD. Pathological features similar to DM in the brains of patients with AD were observed, such as insulin efficacy and lack of glucose metabolism (Takeishi et al., 2021). A study used weighted gene co-expression network analysis to discover the common mechanisms of AD and DM, such as circadian entrainment, phagosomes, and glutathione metabolism (Zhu et al., 2020). These characteristics suggest that AD may be associated with DM, leading to a new term, type 3 diabetes (Diniz Pereira et al., 2021). Both DM and AD occur commonly in elderly people, and DM has been considered a potential critical risk factor for AD (Wang et al., 2017). DM increases the risk of dementia in carriers with the APOE ɛ4 allele, and the heritability of the two diseases is estimated to be more than $50\%$ (Li et al., 2020). A meta-analysis of 28 observational studies shows that people with DM are more likely to develop AD. Compared with non-diabetic patients people with a history of diabetes had a $73\%$ increase in the risk of all types of dementia, a $56\%$ increase in AD, and a $127\%$ increase in vascular dementia (Gudala et al., 2013). AD and DM share many pathophysiological characteristics, comprising defects in glucose transporters, mitochondrial dysfunctions in the brain, impaired insulin sensitivity, Aβ accumulation, tau hyperphosphorylation, brain vasculopathy, inflammation, and oxidative stress (Tumminia et al., 2018). For instance, the activation of glycogen synthase kinase 3β requires insulin, which in turn causes tau phosphorylation to form neuronal fiber tangles. Interestingly, not only insulin significantly contributes to the formation of amyloid plaques but also amylin co-secreted with insulin favors this process (Kandimalla et al., 2017). Chronic hyperglycemia also leads to neuroinflammation and tau hyperphosphorylation in the hippocampus leading to cognitive decline (Wirt et al., 2021). Studies have shown that Aβ deposition and tau phosphorylation might be achieved through altered insulin pathways, both leading factors for AD development (Boccardi et al., 2019). Neuroinflammation is a recognized central mechanism of aging-related diseases, such as cognitive impairment and diabetes. To further add to these injuries, adult neurogenesis that provides neuronal plasticity is also impaired in the diabetic brain (Pugazhenthi et al., 2017). It has been found that low-dose STZ-induced hyperglycemia impairs network activity in the hippocampus and anterior cingulate cortex, mainly by increasing the phosphorylation of tau in the hippocampus and cortex (Wirt et al., 2021). Studies have shown that a vanadium compound bis(ethyl maltol to)-oxovanadium (IV), used initially to treat DM, effectively improves the inflammation in the brain of AD mice, significantly reduces the level of Aβ, and the spatial learning and memory activities of AD mice revised substantially (He et al., 2021). Since DM is a long-term chronic disease, it takes some time to develop into AD, and more attention should be paid to protecting brain function to avoid AD during DM treatment (Li et al., 2021).
The common transcriptional feature provided a novel and feasible scheme for the common pathogenesis of AD and DM at the genetic level. We analyzed the two gene expression matrix (GSE97760 and GSE95849). Comprehensive bioinformatics and enrichment analysis will determine common DEGs and their function on AD and DM. In addition, the PPI network was constructed with the STRING database and analyzed with Cytoscape software. ImmuCellAI is a reliable and efficient platform for immune infiltration analysis, successfully quantifying 24 immune cell subsets of AD and DM. Finally, we identified and validated 10 significant representative hub genes in AD and DM. In addition, we validated the transcription factors of these genes and their expression in the final analysis. Revealing the hub genes of AD and DM helps clarify the common mechanism between them. It provides a new means for exploring the molecular biological mechanism of other multiple diseases.
## Data source
GEO,1 containing many high-throughput sequencing and microarray gene sets, is a public database submitted by research institutes worldwide (Edgar et al., 2002). We searched for related gene expression datasets using Alzheimer’s disease (AD) and Type 2 *Diabetes mellitus* (DM) as keywords. Two microarray datasets [GSE97760 and GSE95849] from the blood genome were downloaded from GEO(Agilent GPL16699 platform, Phalanx Human lncRNA One Array v1_mRNA GPL22448 platform). The GSE97760 dataset contains patients with AD ($$n = 9$$) and healthy controls ($$n = 10$$) (Naughton et al., 2015). GSE95849 consists of patients with DM ($$n = 6$$) and healthy female controls ($$n = 6$$) from the peripheral blood mononuclear cells (Luo et al., 2017).
## Identification of DEGs
The GEO query package reads matrix data in Rstudio. Remove probe sets without gene symbols and take their maximum values for genes with multiple probe sets. Only the genes value with $p \leq 0.05$ and |logFC| ≥ 1 were identified as DEGs. The Funrich was used to obtain the common DEGs between AD and DM.
Figure 1 presents the idea of the article. After correlation analysis, standardizing processing of the microarray results (Figures 2A,B, 3A,B), DEGs (8,091 in GSE97760 and 3,004 in GSE95849) were identified (Figures 2C,D, 3C,D). The 339 common DEGs (97 down-regulated, 242 upregulated) were obtained after excluding genes with opposite expression trends in GSE97760 between GSE95849 (Figures 4A). In the DEGs analysis, GSM2527027 was treated as an outlier sample, so this sample was removed during the subsequent analysis (Figures 3A:a1,a2,B:b1,b2).
**Figure 1:** *Research design flow chart. (A) Download AD and DM blood transcriptome data from GEO and analyze, (B) functional enrichment analysis of common differential genes, (C) PPI network map of common genes, (D) DM or AD immune infiltration analysis, (E) Hub gene expression level and co-expression network analysis, (F) Hub gene neural network analysis, (G) Hub gene TFs expression level and phenotypic correlation analysis.* **Figure 2:** *Microarray normalization and differential gene analysis in the AD group. (A) The heatmap of AD, (B) The PCA map of AD, (C) The volcano map of AD, and (D) Differential heatmap of partial gene expression between AD and normal group.* **Figure 3:** *Microarray normalization and differential gene analysis in the DM group. (A) The heatmap of DM, (B) The PCA map of DM, (C) The volcano map of DM, and (D) Differential heatmap of partial gene expression between DM and normal group. The data is represented by a1/b1 before processing and a2/b2 after processing.* **Figure 4:** *DEGs enrichment analysis results. (A) The two datasets showed an overlap of 339 DEGs, (B) The enrichment analysis results of GO, and (C–D) The enrichment analysis results of the KEGG Pathway. Adjusted p-value <0.05 was considered significant.*
## Enrichment analyses of DEGs
Gene ontology (GO) is a multifaceted annotation of the genome for biological processes, cellular components, and molecular functions (Ye et al., 2022). The Kyoto Encyclopedia of Genome and Genome (KEGG) annotates the genetic pathways of different species in many ways, providing information about biological functions (Ye et al., 2020). The GO and KEGG pathways were enriched by the omicshare database.2 The GO enrichment results were plotted by Chiplot.3 $p \leq 0.05$ was considered significant.
## Construction of protein–protein interaction networks and prediction of small molecule drugs
Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org) (version 11.5) constructed an interaction network between genes with a combined score of over 0.4 (Deng et al., 2020b). Cytoscape4 (version 3.9.0) observes the connections between targets and can be used to visualize this PPI network (Deng et al., 2020a). Using the Connectivity Map (CMap, https://clue.io/) database, DEGs were compared with a reference dataset, pert type selected for perturbation types, and trt cp selected for compounds. A connectivity score was obtained according to the enrichment of DEGs in the reference gene expression profile. A negative correlation analysis was performed to predict small molecule drugs capable of reversing the pathology of the disease (Subramanian et al., 2017).
## Analysis of immune cell infiltration
Immune Cell Abundance Identifier (ImmuCellAI, http://bioinfo.life.hust.edu.cn/ImmuCellAI#!/), a widely used database for evaluating cell infiltration in the microenvironment (Healy et al., 2008). ImmuCellAI can predict the abundance of 24 immune cell types in samples. The immune cell infiltration in different groups will be analyzed with ImmuCellAI in the examined group. Using the ImmuCellA algorithm, the study analyzed patients with AD or DM data and quantified the relative proportion of 24 infiltrating immune cells.
## Selection and analysis of hub genes
This study used the cytoHubba plugin of Cytoscape to identify hub genes and nine standard algorithms (MCC, Stress, Betweenness, Closeness, MNC, DMNC, Degree, Radiality, EPC) to evaluate and select hub genes. Subsequently, based on these hub genes, we constructed a co-expression network via GeneMANIA,5 a reliable and efficient bioinformatics tool for mining the intrinsic links between genes through the multi-angle of literature data (Warde-Farley et al., 2010).
According to the characteristics of the nine algorithms of cybHubba, we obtain the first 30 hub genes, respectively (Supplementary Table S4). Notably, these hub genes share 10 targets, including seven upregulated genes (PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, and RAB14) and three down-regulated genes (ADCY5, RAPGEF3, PRKACG) (Figure 7A). Table 1 shows the full name and the hub genes of related functions. Based on the GeneMANIA database, we got a complex PPI network with a co-expression of $59.87\%$, Reactome of $31.95\%$, physical interactions of $7.09\%$, and pathway of $1.10\%$. GO analysis involved response to glucagon, cellular response to peptide hormone stimulus, cAMP metabolic process, insulin secretion, regulation of neurotransmitter secretion, cellular response to the metal ion, and innate immune response activating cell surface receptor signaling pathway. These results of Reactome emphasized the critical role of the immune system and insulin in AD and DM (Figure 7B). Furthermore, pathway analysis with WebGestalt is associated with the serotonergic synapse, ovarian steroidogenesis, and estrogen signaling pathway, regulation of lipolysis in adipocytes, and human cytomegalovirus infection (Figure 7D). Interestingly, two genes (ADCY5 and PRKACG) were almost involved in all top 10 KEGG Pathways (Figure 7C). Thus, neurotransmitters, insulin, immunity, and sex hormones play essential roles in developing these two diseases. Figure 7E shows the mRNA expression of 10 *Hub* genes.
**Figure 7:** *Acquisition of Hub Gene and Analysis of Co-expression Network. (A) The Venn diagram showed that nine algorithms have screened out 10 overlapping hub genes, (B) Ten hub genes and their co-expression network were analyzed by GeneMANIA, (C) Functional distribution of *Hub* gene, (D) *Pathway analysis* with WebGestalt, and (E) The mRNA expression of 10 *Hub* genes. Unpaired t-test, Mean ± SD, p-value <0.05 was considered significant, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$* TABLE_PLACEHOLDER:Table 1 To explore the contribution of the *Hub* gene to the immune infiltration of disease, we carried out a correlation analysis. For AD patients, EEA1, LRRK2, NF1, PTGS2, RAB10, RAB14, and SOS1 were significantly positively associated with immune infiltration scores, and ADCY5, RAPGEF3 were significantly negatively associated with immune infiltration scores (Figure 6C). And only PTGS2 was significantly positively associated with immune infiltration scores in DM patients (Figure 6D).
In addition, we established multilayer perceptron (MLP) networks, which have two hidden layers and five neurons in each hidden layer. For the obtained mlpcla model, the network structure of the model can be visualized using the plotnet () function. After running the program, the image shown in Figure 8 can be obtained. In the connection weights between neurons in the network, the positive importance uses red lines, the negative consequence uses gray streaks, and the line’s thickness reflects the weight’s size (Figures 8A,D). For the MLP network, the importance of each independent variable to the model prediction results can be computed and visualized. Our study found that the top three most positive important variables for AD classification were PRKACG, RAPGEF3, and LRRK2. Three negative weights, such as EEA1, RAB14, and NF1. The top three most positive variables for DM classification were PRKACG, RAPGEF3, and RAB14.
**Figure 8:** *Multilayer perceptron networks analysis. (A,D) Neural network analysis of Hub gene in AD and DM groups, (B,E) The importance of each independent variable to the AD or DM model prediction, and (C,F) The prediction effect of the MLP classifier on the dataset of AD or DM.*
Similarly, the negative weights are NF1, LRRK2, EEA1, etc. ( Figures 8B,E). The prediction effect of the MLP classifier on the dataset can also be visualized using the confusion matrix. As shown in Figure 8, the seeds of AD and DM can be found in the confusion matrix, which can be predicted $100\%$ correctly (Figures 8C,F).
Interestingly, after querying the AlzData (high-throughput omics data for AD, http://www.alzdata.org/), we found that RAPGEF3 was significantly positively correlated with Aβ and Tau, while NF1 was significantly negatively correlated with Aβ and Tau. Besides, we also use the Attie lab diabetes database (the interactive database of gene expression and diabetes-related clinical phenotypes, http://diabetes.wisc.edu/correl_f2.php) and found that ADCY5 and SOS1 had a significant positive association with insulin and body weight, but significantly negatively associated with glucose; On the contrary, PTGS2, EEA1 were significantly and positively associated with glucose, but a significant negative association with insulin and body weight. Human Genetic Evidence Calculator (HuGE Calculator, Evidence from human genetics can provide important support for hypotheses about the roles of genes in disease, https://hugeamp.org/hugecalculator.html?prior), the query results showed ADCY5, NF1 significant associations with type 2 diabetes phenotypes (Figures 9C).
**Figure 9:** *TFs regulatory network and its expression in GSE97760 and GSE95849. (A) The expression level of TFs in GSE97760 and GSE95849. The comparison between the two sets of data uses the mean T-test. *p < 0.05, **p < 0.01, ***p < 0.001, (B) TFs regulatory network. TFs were marked in green, and the hub genes were marked in red, and (C) Evidence from human genetics can provide important support for hypotheses about the roles of genes in DM via the HuGE Calculator.*
## Prediction and verification of transcription factors
iRegulon implements a genome-wide ranking and recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets (Janky et al., 2014). Subsequently, we verified the TFs that regulate the hub genes and the expression levels of these TFs in GSE97760 and GSE95849 with the T-test, a p-value <0.05 was considered significant.
## Analysis of the functional features of common DEGs
GO functions and KEGG Pathway enrichment analyses were performed to analyze the biological functions and pathways involved in the 339 common DEGs (Supplementary Table S1). GO analysis results show that 4,236 biological process (BP), which contains 3’-UTR-mediated mRNA stabilization, vesicle organization, cytokinetic process, and astrocyte development; 567 cellular components (CC), involved endosomal part, lipopolysaccharide receptor complex, Wnt signalosome and glycosylphosphatidylinositol-mannosyltransferase I complex; 720 molecular functions (MF), such as structural constituent of muscle, Toll-like receptor 4 binding, lipopolysaccharide receptor activity and Toll-like receptor binding (Figure 4B). KEGG Pathway includes organismal systems, metabolism, environmental information processing, and human diseases. Three significant enrichment pathways in terms of metabolism are glycan biosynthesis and metabolism, metabolism of cofactors, glycan biosynthesis and metabolism; thyroid hormone synthesis, ovarian steroidogenesis, and regulation of lipolysis in adipocytes were enriched in organismal systems; MAPK signaling pathway, cAMP signaling pathway and Hippo signaling pathway found in environmental information processing; endocrine resistance, malaria, legionellosis classed in human diseases (Figures 4C,D). These results indicate that inflammatory, hormones, cytokines, and glycan are jointly involved in the occurrence and development of AD and DM.
## PPI network construction and small molecule drug prediction
The PPI network contains 233 nodes and 286 interaction pairs (Figure 5). DEGs were input into the CMap database to predict small molecule compounds that may reverse two diseases’ pathology by connectivity (Supplementary Table S2). To explore the feasibility of this method, we searched the approved drugs for both disorders in Drugbank6 and obtained 10 drugs for AD and 52 medications for DM (Supplementary Table S3). Interestingly, in the CMap results, seven drugs for AD were matched with a score range of −0.9247 to −1.3658; similarly, 27 pills for DM were obtained with a score range of −0.6906 to −1.0733. Among the 7,952 negatively correlated small molecule compounds, the score range of the top 10 small molecules is −1.6824 to −1.8921, which is significantly lower than the listed drugs of the two diseases, suggesting that these small molecules have the potential to reverse two diseases’ pathology.
**Figure 5:** *PPI network and common DEGs. Red indicates upregulated genes, and blue-violet indicates down-regulated genes.*
## Immune infiltration analyses
Detecting the microenvironment has a significant reference value for clinical treatment sensitivity and disease diagnosis (Liu et al., 2021). After studying the relationship between immune infiltration and gene matrix, we further explored the potential molecular mechanism of genes affecting the progression of the two diseases (Figure 6). The results indicated that the AD group’s fractions for monocytes, NKT, Tr1, iTreg, Tcm, and Tem were remarkably higher than those of the regular patients. In comparison, the particles of many cells are lower than those of normal patients, such as DC, Neutrophil, nTreg, and CD8_navie (Figure 6A). However, the condition of immune infiltration behaved differently in the DM group. Compared with regular patients, Neutrophil increased significantly in the DM group, whereas monocytes, iTreg, and iTreg decreased significantly, and other significant decreases were NK, CD4_T, CD8_T, Tgd, CD4_navie, nTreg, Tfh, and CD8_naive (Figure 6B). The above results reflect the different cellular immune microenvironments of various diseases.
**Figure 6:** *Immune infiltration analysis. (A) Analysis of immune infiltration in an AD group (blue) and blank group (red), (B) Analysis of immune infiltration in DM group (red) and blank group (blue), (C) Analysis of the relationship between Hub gene and immune infiltration in the AD group, and (D) Analysis of the relationship between Hub gene and immune infiltration in the DM group. Unpaired t-test, Mean ± SD, p-value <0.05 was considered significant.*
## Key gene validation
Accumulating epidemiological and biochemical evidence suggests that insulin resistance in the brain leads to altered gene expression profiles in the hippocampus and prefrontal cortex of rats, suggesting an association between type 2 diabetes mellitus and Alzheimer’s dementia. The GSE34451 dataset contains triplicates of samples prepared from each brain region of type 2 diabetic Goto-Kakizaki rats and controls animals, providing further experimental evidence for the recently elaborated theory that Alzheimer’s is type 3 diabetes(Abdul-Rahman et al., 2012). Compared with the normal group, EEA1, NF1, and RAB14 were significantly up-regulated in the hippocampus. ADCY5 and RAPGEF3 were significantly down-regulated in the cortex, LRRK2, RAB10, and SOS1 were significantly down-regulated in the striatum, and NF1 was significantly up-regulated. It has been shown that T2D db/db mice exhibit deficits in short-term and spatial working memory compared to db/m mice. Microarray analysis of hippocampal tissue from T2D db/db mice using the GSE151294 dataset revealed that EEA1 and LRRK2 were significantly down-regulated in the hippocampus, while NF1 was significantly up-regulated.
The above validation results look somewhat different from those of our bioinformatics analysis, which may be due to the different species and subjects tested. In this paper, human blood transcriptomics of DM and AD were studied, and the verification samples were animal brain tissues for verification, but the final results were favorable to us. For example, the mRNA expression trend of NF1, RAB14, ADCY5, and RAPGEF3 in the blood is the same as that in encephalopathy, suggesting that the purpose of assessing brain state can be achieved by detecting blood-related indicators (Table 2).
**Table 2**
| Gene | Value of p | LogFC | Tissues | Data source |
| --- | --- | --- | --- | --- |
| EEA1 | 0.001 | 0.91 | hippocampus | GSE34451 |
| NF1 | 0.001 | 1.05 | hippocampus | GSE34451 |
| RAB14 | 0.015 | 0.64 | hippocampus | GSE34451 |
| ADCY5 | 0.031 | −0.64 | prefrontal cortex | GSE34451 |
| RAPGEF3 | 0.014 | −0.76 | prefrontal cortex | GSE34451 |
| LRRK2 | 0.038 | −1.14 | striatum | GSE34451 |
| NF1 | 0.03 | 0.91 | striatum | GSE34451 |
| RAB10 | 0.037 | −0.90 | striatum | GSE34451 |
| SOS1 | 0.038 | −0.73 | striatum | GSE34451 |
| EEA1 | 0.021 | −0.45 | hippocampus | GSE151294 |
| LRRK2 | 0.036 | −0.40 | hippocampus | GSE151294 |
| NF1 | 0.02 | 1.10 | hippocampus | GSE151294 |
## Prediction and verification of TFs
Based on the iRegulon plugin, we found that 10 TFs (NES ≥ 5) may regulate the expression of hub genes (Figure 9A, Supplementary Table S5). Further verification revealed that CEBPD, E2F6, and FOXO4 were significantly upregulated in the DM group, while KDM4A and HOXB7 were significantly down-regulated. Similarly, CNOT4 was significantly upregulated in the AD group, but FOXO4 was significantly down-regulated. They coordinated in regulating 10 hub genes (Figure 9B).
## Discussion
There is growing evidence that both DM and AD diseases are involved in impaired glucose homeostasis and changes in brain function (Baglietto-Vargas et al., 2016). Current theories and hypotheses suggest that defective insulin signal transduction in the brain causes synaptic dysfunction and cognitive impairment in AD (Pugazhenthi et al., 2017). In addition to common risk factors and clinical symptoms, metabolic defects, such as reduced cerebral glucose metabolism and central insulin resistance, are now considered inherent in AD. Therefore, some researchers believe AD is a “type 3 diabetes” (de la Monte et al., 2018; de la Monte, 2019). There is no doubt that insulin resistance is the bridge between DM and AD. The primary purpose of our study is to identify the common DEGs in AD and DM, reveal potential targets, clarify their common possible pathogenesis, and prevent and treat DM complicated with AD.
In this study, we screened 10 hub genes from 339 overlapping DEGs, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. GO, and KEGG pathway enrichment disclosed that hub genes were significantly involved in inflammatory, immune, insulin regulation, and hormone metabolism pathways. These 10 genes may play an important role in AD and DM diseases, and play a regulatory role in the occurrence and development of the two diseases.
Endocytosis is an active transport system, which involves the cell membrane of transport molecules entering and leaving the cell through the endocytosis transport system, and all components of the system form the endocytosis pathway (Hansen and Nichols, 2009). When the plasma membrane is partially trapped, endocytosis occurs, in which the contents of the vesicles are internalized through a grid-dependent or grid-independent pathway (Khan and Steeg, 2021). Increasing evidence shows that endocytosis plays a role in Aβ metabolism (Choy et al., 2012). Neurons can clear β-amyloid precursor protein (APP) through endocytosis. Genetic studies have shown that the occurrence and progress of AD are related to some endocytosis-related genes (Hollingworth et al., 2011; Lambert et al., 2013). Therefore endocytosis gene mutations that destroy the physiological function of neurons may contribute significantly to the pathophysiology of AD (Kimura and Yanagisawa, 2018). Recently, a study investigated their association with AD, mild cognitive impairment (MCI) and brain magnetic resonance structural phenotypes by constructing multiple genetic risk scores (MGRS), suggesting that the MGRS capture endocytosis pathway is significantly associated with MCI (Ahmad et al., 2018). However, the effects of endocytosis on multiple genes of brain function seem to be unknown in the AD spectrum (Zhu et al., 2021). It is intriguing that membrane traffic-associated genes, such as RAB10 and EEA1, are included in the 10 hub genes.
Adenosine cyclase type 5 (ADCY5) can be used as an effector of neurotransmitters such as the D2 dopamine receptor, mu, δ opioid receptor, and mGluR3 glutamate receptor. It is preferentially expressed in the dorsal striatum and nucleus accumbens, and to a lesser extent in other regions of the brain, such as the prefrontal cortex and cerebellum (Lee et al., 2002; Kim et al., 2017). The change of ADCY5 expression in the β-cells leads to impaired glucose signal transduction, which indicates that ADCY5 gene polymorphism may affect fasting blood glucose levels and diabetes risk (Ustianowski et al., 2021). For the common influencing factors of AD and DM, such as obesity and depression, the expression of ADCY5 is increased. ADCY5 gene expression in adipose tissue is related to obesity in men and mice. In humans and mice, visceral ADCY5 expression is significantly higher in obese compared to lean individuals, and changes in adipose tissue ADCY5 expression are related to obesity and fat distribution, but not to impaired glucose metabolism and T2DM (Knigge et al., 2015).
LncRNA PTGS2 can damage islet β-cell function by regulating miR-146a-5p and upregulating RBP4, suggesting that LncRNA PTGS2 has potential value in the diagnosis of DM (Chen et al., 2021). Elevated levels of cyclooxygenase-2 (COX-2/PTGS2) and prostaglandins (PGs) are involved in the pathogenesis of AD, COX-2 dysregulation influences abnormal cleavage of the β-amyloid precursor protein, aggregation, and deposition of β-amyloid plaques and the inclusion of phosphorylated tau in neurofibrillary tangles. The mechanisms of PTGS2 regulation of AD may include neuroinflammation, oxidative stress, synaptic plasticity, neurotoxicity, autophagy, and apoptosis (Guan and Wang, 2019).
RAB10 is a small Rab GTP enzyme involved in vesicle transport and has recently been identified as a new protein related to AD. Interestingly, RAB10 is the key substrate of leucine-rich repetitive kinase 2 (LRRK2) (Healy et al., 2008). Besides, RAB10 phosphorylation leads to neurodegeneration, which may be responsible for vesicular transport aberration observed in AD (Tavana et al., 2019).
Early endosomal antigen 1 (EEA1) was significantly increased in the human cerebrospinal fluid from AD patients compared with neurological controls, and EEA1 levels correspond to the increased total-tau levels (Armstrong et al., 2014). EEA1 gene is a candidate mutation for susceptibility to diabetes in the Japanese population, which has been confirmed by a genetic background of familial clustering of diabetes using genome-wide linkage analysis combined with exome sequencing (Tanaka et al., 2013). For DM patients, SOS1 was statistically significantly associated with gestational diabetes mellitus risk at the gene level (Chen et al., 2018).
Based on the above literature, we speculate (Figure 10) that ADCY5 stimulates RAPGEF3 and sAPPα through the cAMP signal pathway to play a neuroprotective effect, while PTGS2 can inhibit the activity of ADCY5. The trend of the expression of these genes is consistent with our conjecture, so we have reason to suggest that the loss of control of these genes leads to the destruction of neuroprotection; Both PRKACG and SOS1 can inhibit the Gap junction channel, destroying the blood–brain barrier and brain disease; LRRK2 and substrate RAB10 jointly mediate apoptosis, but this study found that the expression of these two genes in DM and AD were significantly increased, suggesting that their elevation led to neuronal apoptosis and promoted the transformation of DM to AD.
**Figure 10:** *Exploring the hub genes related to the pathogenesis of DM complicated with AD by bioinformatics analysis. This figure was drawn by the ScineceSlides plugin.*
In addition, according to the DEGs of AD and DM, the small molecule drug prediction was carried out through the CMap database, and the small molecule compound, such as L-690488, exemestane, and BMS-345541 may reverse the pathology of AD and DM, was identified. Compared with other bioinformatics studies, our research focus is slightly different. In addition to exploring the common hub genes and biological pathways involved in AD and DM, we have also explored possible therapeutic drugs and related TFs. By building complex interactive networks, it is easy to get their common DEGs and identify potential key targets. This comprehensive bioinformatics approach is reliable and informative in a variety of diseases (Su et al., 2021; Ye et al., 2021). In addition, we also analyzed the related TFs expression levels in the original data set, to explore whether TFs will also be affected by the disease. Although previous studies have explored the central genes related to AD and DM respectively, this study focuses on the molecular mechanism of DM complicated with AD, providing a potential direction for the mechanism of diseases with complications (Gudala et al., 2013; Li et al., 2020; Zhu et al., 2020).
However, we have to point out some limitations of this study. First of all, this is a microarray data analysis study, which theoretically belongs to the retrospective study, although this approach can speed up our work efficiency in discovering disease mechanisms, more external verification is needed to verify the key objects of our findings; Secondly, the development of DM into AD is a dynamic and slow process. In the future, we will study the gene matrix of DM complicated with AD, and look for the marker genes of this process (perhaps the key genes ADCY5, PTGS2, RAB10, etc. that we have studied play a role in this dynamic process), thereby effectively controlling the development of DM symptoms; Thirdly, the function of hub genes in disease needs further verification by corresponding biological models. Whether these genes have positive significance for clinical evaluation remains to be explored, which will be a challenge for our future work in AD and DM.
## Conclusion
In conclusion, we explored the possible DEGs of AD and DM, and performed routine bioinformatics analysis and PPI network construction. As we expected, AD and DM contribute various commonplace pathogenic mechanisms, which perchance voluntary by individual hub genes. Glucose homeostasis and changes in brain function, NF1, RAB14, ADCY5, and RAPGEF3 could be the focus of later studies (Figure 10). Up to now, the connection between essential genes and immune infiltration of AD and DM has been infrequently reported. Whether the key genes have clinical diagnostic significance and whether the factors related to immune infiltration are conducive to the diagnosis of AD or DM remains to be explored. This study states a new concept for the continued exploration of the molecular mechanism of DM accompanied by AD or other diseases.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
## Author contributions
X-fX and X-rL developed a major research plan. W-jX and XW analyzed the data. S-qC, Y-yB, and C-sL drew charts. M-nL and X-wY wrote manuscripts. L-lY and X-xW helped to collect data and references. JW and S-yZ implemented corrections in the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Natural Science Foundation of China (no. 81973480) and the National Key Research and Development Program of China (no. 2019YFC1711500).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi.2023.1071391/full#supplementary-material
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|
---
title: 'Effects of individualized dietary counseling on nutritional status and quality
of life in post-discharge patients after surgery for gastric cancer: A randomized
clinical trial'
authors:
- Hongxia Yan
- Fang He
- Jianjian Wei
- Qiuxiang Zhang
- Chunguang Guo
- Jinnv Ni
- Fangyu Yang
- Yingtai Chen
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10008882
doi: 10.3389/fonc.2023.1058187
license: CC BY 4.0
---
# Effects of individualized dietary counseling on nutritional status and quality of life in post-discharge patients after surgery for gastric cancer: A randomized clinical trial
## Abstract
### Background
Currently, the supporting evidence for dietary counseling is insufficient. The aim of this study is to evaluate the impact of individualized dietary counseling on nutritional outcomes and quality of life (QOL) in patients undergoing surgery for gastric cancer.
### Methods
This study was a prospective, single-center, randomized controlled trial. The patients after surgery for gastric cancer were randomly assigned (1:1) to the intervention group and the control group. In the intervention group, patients receive individualized dietary counseling based on individual calorie needs and symptom assessment at 24 h before discharge, 14, 21, 30, and 60 days postoperatively. Patients in the control group received routine dietary counseling. The primary endpoint was body mass index (BMI) loss at 30, 60, and 90 days after surgery; the secondary endpoints were calorie and protein intake at 30 and 60 days after surgery, blood parameters, the 90-day readmission rate, and QOL at 90 days after surgery.
### Results
One hundred thirty patients were enrolled; 67 patients were assigned to the intervention group and 63 patients to the control group. Compared with the control group, patients in the intervention group were significantly less BMI loss at 30 days (−0.84 ± 0.65 vs. −1.29 ± 0.83), 60 days (−1.29 ± 0.92 vs. −1.77 ± 1.13), and 90 days (−1.37 ± 1.05 vs. −1.92 ± 1.66) after surgery (all $P \leq 0.05$). Subgroups analysis by surgery type showed that the intervention could significantly reduce BMI loss in patients undergoing total and proximal gastrectomy at 30 days (−0.75 ± 0.47 vs. −1.55 ± 1.10), 60 days (−1.59 ± 1.02 vs. −2.55 ± 1.16), and 90 days (−1.44 ± 1.19 vs. −3.26 ± 1.46) after surgery (all $P \leq 0.05$). At 60 days after surgery, calorie goals were reached in 35 patients ($77.8\%$) in the intervention group and 14 patients ($40.0\%$) in the control group ($$P \leq 0.001$$), and protein goals were reached in 40 patients ($88.9\%$) in the intervention group and 17 patients ($48.6\%$) in the control group ($P \leq 0.001$). Regarding the QOL at 90 days after surgery, the patients in the intervention group had a significantly lower level of fatigue, shortness of breath and stomach pain, better physical function, and cognitive function ($P \leq 0.05$).
### Conclusions
Post-discharge individualized dietary counseling is an effective intervention to reduce post-gastrectomy patient weight loss and to elevate calorie intake, protein intake, and QOL.
## Introduction
Gastric cancer (GC) is the fifth most common type of cancer and the third most common cause of cancer-related death worldwide, seriously threatening human life and health [1]. So far, treatment modalities include surgery, chemotherapy, and radiotherapy, and surgery remains the main and most effective therapy for GC [2]. However, GC surgery has caused reduction of food storage volume and various gastrointestinal symptoms, threatening the nutritional status of patients to varying degrees (3–5). Body weight loss and malnutrition are frequently observed in patients who undergo gastrectomy [6]. Malnutrition has been indicated to have negative influence patients’ clinical outcomes, including increased risk of recurrence, decreased tolerance to treatment, and quality of life (QOL) (7–10).
Home rehabilitation after GC surgery is a special period. Patients with reconstructed gastrointestinal tracts have just regained partial function and are still at suffering from post-surgical syndromes and the risk of readmissions [11]. During this period, patients gradually transition from semi-liquid foods to soft or regular foods, and their diet is inevitably restricted [12]. Because of the influence of gastrointestinal symptoms, some patients often take the reduced intake method to relieve gastrointestinal symptoms. Poor eating habits that lack high-quality protein in the diet. These factors can lead to insufficient calorie and protein intake [13]. A Korean study confirmed that most patients after GC surgery experienced reduced food intake and rapid weight loss during this period [14]. Therefore, appropriate nutritional support should be adopted to assist patients in a smooth transition to complete oral feeding, which has an important clinical significance for maintaining postoperative body weight and improving chemotherapy tolerance.
Previous studies have shown that oral nutritional supplement (ONS) and nutrition education are beneficial for patients with GC after surgery (15–18). However, there is a gap between the actual intake of ONS and the recommended amount due to factors such as intolerance [19, 20] and the role of dietary intervention and counseling is uncertain, and further research into optimal nutrition support interventions and timing of interventions is required [21, 22]. In recent years, individualized dietary nutrition counseling strategies based on the individual calorie needs of patients have provided nutritional support to patients at nutritional risk, showing benefits on clinical outcomes of patients [23]. The study was carried out only on patients who were hospitalized. Home-based dietary nutrition counseling is rarely reported due to the existence of many barriers, such as dietary restrictions, symptom burden, time and space inconvenience, and patient compliance.
In the present study, we aim to systematically evaluate the impact of individualized dietary counseling based on individual calorie and protein needs compared with routine discharge counseling follow-up on nutritional outcomes, the 90-day readmission rate, and QOL in post-discharge patients after GC surgery.
## Study design and patients
This prospective, single-center, randomized controlled trial was conducted in the Department of Pancreatic and Gastric Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital from August 2021 to January 2022. This study was approved by the hospital ethics committee, and the ethics approval number is $\frac{21}{281}$-2952. After informed consent, patients were randomly allocated into individualized dietary counseling group (intervention group) and routine dietary counseling group (control group) on a 1:1 ratio through excel random number table. All patients’ calorie and protein calculations in the diet diary were calculated by masked nurses according to the China Food Composition Tables—Standard Edition [24].
We enrolled patients who were aged 18–75 years, discharged after radical gastrectomy, and reconstruction of gastrointestinal tract function recovery, allowing food intake and barrier-free communication. The patients were excluded if they were combined with other organ resections, had complications, and were not allowed to eat, such as anastomotic leakage, intestinal obstruction, and gastroparesis after surgery; and were in other nutritional intervention studies at the same time. Patients were rejected if they withdrew the informed consent, were lost to follow-up, were readmitted to the hospital and unable to eat for more than 72 h, were found with local recurrence of tumor, and received radiotherapy that seriously affected eating.
## Study protocol
An individualized nutrition support team consisting of surgeons, nurses, and dieticians was set up. Surgeons were responsible for the treatment of gastrointestinal symptoms and diagnosis and treatment of readmissions; dieticians guided nurses to calculate calorie requirements, customize the recipe, and make dynamic adjustments; and nurses were the main executors of individualized dietary counseling.
All patients signed the consent form 24 h before discharge and collected baseline data. We collected patients’ general and clinical information including age, gender, education level, long-term residence, medical history, smoking status, alcohol consumption, type of surgery, surgery time (minutes), postoperative hospital stay (days), preoperative complications, American Joint Committee on Cancer (AJCC) stage, neoadjuvant chemotherapy, and adjuvant chemotherapy. A symptom assessment was conducted, and a dietary diary for 24 h was handed out. On discharge day, morning weight and dietary diary for 24 h are withdrawn. All patients received the first dietary counseling, the intervention group received individualized dietary counseling, and the control group received conventional dietary counseling.
In the intervention group, patients received individualized dietary counseling to reach protein and calorie goals, as shown in Figure 1. The daily protein requirement is set at 1.2 g/kg according to the nutritional guidelines [25]. Calorie requirements were predicted using the Mifflin-St Jeor equation; it contains resting energy expenditure (REE), the stress factor, which is set to 1.0, and the activity factor, which is set to 1.4 [26]; and the calculation method is as follows:
**Figure 1:** *Intervention group study flow chart. Twenty-four–hour diet diary (self-designed form); symptom assessment (MDASI-C); “*”, calculate according to the patient’s needs.*
In this study, if body mass index (BMI) ≤ 28, then actual body weight is applied; if BMI > 28, then adjusted body weight is applied to calculate calorie and protein needs. According to calorie and protein needs, it is converted into food in an appropriate proportion. When calories are converted into food, the protein requirements are first converted into foods such as fish, meat, eggs, beans, milk, and other foods and then the energy produced by protein foods is excluded; the remaining energy required is distributed: $50\%$ of the calories comes from carbohydrates, $30\%$ from lipids, $10\%$ from vegetables, and $10\%$ from fruits. ONS is routinely recommended because of the insufficient food intake in the early postoperative period, and the recommended dose is 400–600 ml per day [27]. In this study, ONS is added in addition to food, so it does not occupy the total amount of calories needed. The intake is determined by the patient and is not mandatory. If protein intake is insufficient, then it is recommended to supplement with protein supplements of 10–30 g per day. It is recommended that patients cook semi-liquid or soft food before eating. The dietary plan was dynamically adjusted according to the actual calorie and protein intake in the 24-h diary, and symptom assessment was conducted at 14, 21, 30, and 60 days after surgery. The European Society for Parenteral and Enteral Nutrition (ESPEN) defines insufficient oral intake as an oral intake ≤$75\%$ of the estimated daily calorie needs [28]. At 30 and 60 days after surgery, if the total oral intake (including food and ONS) is less than $60\%$, then intravenous supplementation is recommended. Patients with intakes in the range of $60\%$–$75\%$ received individualized adjustments based on patient diet and symptom assessment. Intakes greater than $75\%$ were considered to achieve the target amount; if the calorie intake is lower than $100\%$, then the intake appropriately increases; if the calorie intake is higher than $100\%$, then the patient is reminded to maintain the current calorie intake to prevent excessive gastrointestinal burden and further diversify the diet.
Patients in the control group received conventional dietary counseling. It includes dietary principles, types, methods, and contraindications. ONS and protein powder can be supplemented, if necessary. If they encountered problems such as persistent vomiting and severe abdominal pain, then they were accessible to consultation with medical staff through WeChat, telephone, and outpatient or emergency medical treatment in time. Routine follow-up was performed 30 and 60 days after surgery, including 24-h dietary intake and symptoms assessment, and assisted with readmissions. There is no dynamic dietary modification regimen based on the patient’s calorie needs and actual calorie intake.
## Endpoints
The primary endpoint was BMI loss at 30, 60, and 90 days postoperatively. Height measurement is uniformly measured by nurses at admission. Body weight (kilograms) was measured in the morning after defecation on an empty stomach; the patients are wearing light clothing and removed their shoes, cell phones, watches, etc. Post-discharge body weights were collected in the form of patient self-reports, and then, nurses calculated BMI.
The secondary endpoints were the 24-h calorie and protein intake at 30 and 60 days after surgery, blood parameters, the 90-day readmission rate, and QOL at 90 days after surgery. This study used a prospective dietary record approach, and food was weighed for each meal at 30 and 60 days after surgery. Blood parameters including total protein (TP), albumin (ALB), hemoglobin (HGB), and total lymphocyte count (TLC). Ninety-day readmission rates (unplanned readmission within 90 days of surgery) and QOL (EORTC QLQ-C30 and EORTC QLQ-STO22) were assessed at 90 days after surgery. Loss of follow-up, completion of diet records at each follow-up, and completion of review at 90 days after surgery were used as indicators to evaluate compliance.
## Data collection
We assessed QOL with the European Organization for Research and Treatment of Cancer (EORTC) generic cancer (QLQ-C30) and GC (QLQ-STO22) modules [29]. Patients complete QOL assessment via electronic questionnaire at 90 days after surgery. EORTC QLQ-C30 is a reliable and validated measure of the QOL of patients with cancer in multicultural clinical research. The questionnaire is a cancer-specific, self-administered, structured questionnaire that contains 30 questions, which are categorized into the global health status; five functioning scales: physical, role, cognitive, emotional, and social; three symptom scales: fatigue, pain, and nausea and vomiting; and six single items: dyspnea, sleep disturbance, appetite loss, constipation, diarrhea, and financial difficulties. The GC module (QLQ-STO22) is a supplement to the QLQ-C30. The QLQ-STO22 consists of 22 questions that evaluate five multi-item symptoms scales (dysphagia, eating restrictions, pain, reflux, and anxiety) and four single-item symptoms scales (dry mouth, body image, hair loss, and taste loss). For global QOL and the functional scales, a higher score indicates better QOL, with 100 being perfect. For symptom scales, a lower score indicates better QOL, with 0 being perfect or no symptoms reported.
Symptoms were assessed using the Chinese version of the M. D. Anderson Symptom Inventory–China (MDASI-C). Scale items are calculated on a scale of 1–10, with 0 indicating no influence, and 10 indicating severe influence. MDASI-C has good internal consistency reliability, general symptoms, and gastrointestinal symptoms, which had *Cronbach alpha* coefficients of 0.86 and 0.84 [30]. This study only used this scale to evaluate gastrointestinal symptoms and provide dietary counseling to patients, and symptom score is not used as an outcome measure.
## Sample size and statistical analysis
In this study, two independent sample mean comparison superiority experiments were used to calculate the sample size. On the basis of the previous RCT study [18], the postoperative weight loss (−6.9 kg vs. −9.1 kg) was calculated, taking into account the loss rate and dropout rate of $20\%$; the sample size was estimated to be about 130 cases; and the intervention group and the control group took a 1:1 ratio entry.
SPSS 25.0 statistical software was used for data analysis. Mean ± standard deviation, median, and quartile were used to describe the characteristics of continuous data, and frequency, rate, and percentage were used for count data. Differences between the two groups were presented through a t-test or nonparametric test, and a chi-square test was used for univariate analysis. Repeated-measures ANOVA were used to test the between-group, within-group, and interaction effects of repeated-measures measurement data. A two-sided test was used, and $P \leq 0.05$ was considered statistically significant.
## Patient characteristics
From August 2021 to January 2022, we screened 149 patients and enrolled 130, 17 patients were excluded according to the exclusion criteria, and two patients refused to participate. Of these, 67 patients were randomly assigned to the intervention group and 63 to the control group. In our study, 21 patients were shaved, 17 patients fasted for more than 72 h due to readmission, and four underwent postoperative radiotherapy due to local recurrence, which severely affected eating. Three patients ($2.31\%$) were lost to follow-up. Our final evaluable cohort consisted of 106 patients (55 patients in the intervention group and 51 patients in the control group), as shown in Figure 2.
**Figure 2:** *Trial profile.*
There was no significant difference in baseline data between the two groups except for preoperative comorbidities ($P \leq 0.05$), as shown in Table 1. The compliance of dietary records in the intervention group was higher than that in the control group at 30 days after surgery ($$P \leq 0.048$$), as shown in Table 2.
## Nutritional outcomes
There were no significant differences in the mean BMI between the two groups before discharge, 30, 60, and 90 days after surgery ($P \leq 0.05$). Repeated-measures ANOVA (BMI at discharge was used as the covariate) for BMI loss in the two groups showed that there was a statistically significant difference between the within-subject effect ($$P \leq 0.001$$), the between-subject effect ($$P \leq 0.026$$), and the interaction effect ($$P \leq 0.051$$). Compared with the control group, patients in the intervention group have significantly less BMI loss at 30 days (−0.84 ± 0.65 vs. −1.29 ± 0.83, $$P \leq 0.002$$), 60 days (−1.29 ± 0.92 vs. −1.77 ± 1.13, $$P \leq 0.020$$), and 90 days (−1.37 ± 1.05 vs. −1.92 ± 1.66, $$P \leq 0.044$$) after surgery, as shown in Table 3.
**Table 3**
| Time | Variable | Intervention | Control | P |
| --- | --- | --- | --- | --- |
| Discharged | Weight | 64.44 ± 11.12 | 64.31 ± 12.23 | 0.957 |
| Discharged | BMI | 22.79 ± 2.69 | 23.59 ± 3.66 | 0.201 |
| 30 days after surgery | Weight | 62.04 ± 10.52 | 60.85 ± 11.47 | 0.578 |
| 30 days after surgery | Weight loss | −2.39 ± 1.88 | −3.46 ± 2.27 | 0.009 |
| 30 days after surgery | BMI | 21.95 ± 2.58 | 22.30 ± 3.33 | 0.551 |
| 30 days after surgery | BMI loss | −0.84 ± 0.65 | −1.29 ± 0.83 | 0.002 |
| 60 days after surgery | Weight | 60.75 ± 10.34 | 59.60 ± 11.45 | 0.589 |
| 60 days after surgery | Weight loss | −3.69 ± 2.72 | −4.71 ± 2.99 | 0.067 |
| 60 days after surgery | BMI | 21.49 ± 2.54 | 21.82 ± 3.25 | 0.561 |
| 60 days after surgery | BMI loss | −1.29 ± 0.92 | −1.77 ± 1.13 | 0.02 |
| 90 days after surgery | Weight | 60.53 ± 10.70 | 59.26 ± 11.52 | 0.558 |
| 90 days after surgery | Weight loss | −3.91 ± 3.05 | −5.05 ± 4.36 | 0.118 |
| 90 days after surgery | BMI | 21.41 ± 2.69 | 21.67 ± 3.12 | 0.653 |
| 90 days after surgery | BMI loss | −1.37 ± 1.05 | −1.92 ± 1.66 | 0.044 |
Subgroups analysis by surgery type showed that the intervention could significantly reduce BMI loss in patients undergoing total gastrectomy (TG) and proximal gastrectomy (PG) at 30 days (−0.75 ± 0.47 vs. −1.55 ± 1.10, $$P \leq 0.013$$), 60 days (−1.59 ± 1.02 vs. −2.55 ± 1.16, $$P \leq 0.025$$), and 90 days (−1.44 ± 1.19 vs. −3.26 ± 1.46, $$P \leq 0.001$$) after surgery, as shown in Figure 3A. However, the intervention for patients undergoing distal gastrectomy (DG) was only different at 30 days after surgery (−0.88 ± 0.72 vs. −1.21 ± 0.71, $$P \leq 0.046$$), and there was no significant difference at 60 days (−1.17 ± 0.86 vs. −1.49 ± 1.00, $$P \leq 0.133$$) and 90 days (−1.35 ± 0.99 vs. −1.46 ± 1.47, $$P \leq 0.691$$) after surgery, as shown in Figure 3B.
**Figure 3:** *(A) Graph of intervention on BMI loss after total gastrectomy and proximal gastrectomy. (B) Graph of intervention on BMI loss after distal gastrectomy. A=Discharge day; B=30 days after surgery; C=60 days after surgery; D=90 days after surgery.*
There was no significant difference in calorie and protein requirements and intake before discharge between the two groups (P s> 0.05). Compared with patients in the control group, patients in the intervention group had significantly higher calorie intake (1,404.75 ± 387.40 kcal vs. 1,056.33 ± 396.22 kcal at 30 days after surgery, $P \leq 0.001$; 1,611.11 ± 423.11 kcal vs. 1,313.11 ± 408.26 kcal at 60 days after surgery, $$P \leq 0.002$$), as shown in Figure 4A. The protein intake in the intervention group was significantly higher than that in the control group (67.15 ± 21.78 g vs. 46.71 ± 22.89 g at 30 days after surgery, $P \leq 0.001$; 76.89 ± 22.21 g vs. 59.59 ± 25.84 g at 60 days after surgery, $$P \leq 0.002$$), as shown in Figure 4B. Although ONS was recommended, patients’ intake was not satisfactory. The ONS intake in the intervention group was higher than that in the control group (163.65 ± 154.16 ml vs. 79.77 ± 112.32 ml at 30 days after surgery, $$P \leq 0.003$$; 123.11 ± 148.41 ml vs. 43.25 ± 74.85 ml at 60 days after surgery, $$P \leq 0.003$$).
**Figure 4:** *(A) Calorie intake chart. (B) Protein intake chart. A=Calorie/protein requirements; B=24-h calorie/protein intake before discharge; C=30 days calorie/protein intake after surgery; D=60 days calorie/protein intake after surgery.*
At 30 days after surgery, calorie goals were reached in 25 patients ($48.1\%$) in the intervention group and five patients ($11.9\%$) in the control group ($P \leq 0.001$), and protein goals were reached in 36 patients ($69.2\%$) in the intervention group and 12 patients ($40.0\%$) in the control group ($P \leq 0.001$). Calorie intake was less than $60\%$ in 11 patients ($21.2\%$) in the intervention group and 23 patients ($54.8\%$) in the control group ($$P \leq 0.001$$), and the patients were advised to supplement energy through an intravenous route on the basis of oral diet, but only two patients received intravenous supplementation. At 60 days after surgery, calorie goals were reached in 35 patients ($77.8\%$) in the intervention group and 14 patients ($40.0\%$) in the control group ($$P \leq 0.001$$), and protein goals were reached in 40 patients ($88.9\%$) in the intervention group and 17 patients ($48.6\%$) in the control group ($P \leq 0.001$). Calorie intake was less than $60\%$ in 4 patients ($8.9\%$) in the intervention group and nine patients ($25.7\%$) in the control group ($$P \leq 0.043$$); however, no patient received intravenous supplementation therapy, and three patients used traditional Chinese medicine.
Blood parameters showed that there was no significant difference in TP, serum ALB, HGB, and TLC between the two groups before discharge and 90 days after surgery ($P \leq 0.05$), as shown in Table 4.
**Table 4**
| Time | Index | Intervention (n = 55) | Control (n = 51) | P |
| --- | --- | --- | --- | --- |
| Before discharge | TP | 60.99 ± 4.94 | 58.31 ± 6.81 | 0.078 |
| Before discharge | ALB | 34.64 ± 3.03 | 34.08 ± 3.55 | 0.387 |
| Before discharge | HGB | 112.73 ± 18.14 | 112.96 ± 15.47 | 0.994 |
| Before discharge | TLC | 1.38 ± 0.58 | 1.41 ± 0.58 | 0.775 |
| 90 days after surgery | TP | 70.71 ± 3.36 | 66.99 ± 4.43 | 0.653 |
| 90 days after surgery | ALB | 42.93 ± 1.88 | 40.39 ± 3.26 | 0.494 |
| 90 days after surgery | HGB | 124.95 ± 16.47 | 122.67 ± 18.74 | 0.657 |
| 90 days after surgery | TLC | 1.96 ± 0.48 | 1.92 ± 0.68 | 0.367 |
## Ninety-day readmission rate
During the course of the study, eight patients in the intervention group had readmission, and nine patients in the control group had readmission within 90 days after surgery; there was no statistically significant difference ($P \leq 0.05$). The specific reasons for readmission are shown in Table 5.
**Table 5**
| Reason for readmission | Intervention (n = 67) | Control (n = 63) | Total |
| --- | --- | --- | --- |
| Gastrointestinal dysfunction | 3 | 4 | 7 |
| Abdominal infection | 2 | 0 | 2 |
| Bleeding | 1 | 1 | 2 |
| Intestinal obstruction | 0 | 2 | 2 |
| Heartburn reflux | 1 | 1 | 2 |
| Diarrhea | 1 | 0 | 1 |
| Cholecystitis | 0 | 1 | 1 |
| Total | 8 | 9 | 17 |
For the outcomes of readmission patients, among them, 14 patients improved after infusion therapy, one patient with intestinal obstruction improved after surgery, one bleeding patient improved after emergency surgery for hemostasis, and one bleeding patient died after interventional hemostasis.
## Quality of life
Regarding the QOL at 90 days after surgery, the patients in the intervention group had a significantly lower level of fatigue, shortness of breath, and stomach pain; and better physical function and cognitive function ($P \leq 0.05$), as shown in Table 6.
**Table 6**
| Unnamed: 0 | Intervention (n = 55) | Control (n = 51) | P |
| --- | --- | --- | --- |
| EORTC-QLQ-C30 | EORTC-QLQ-C30 | EORTC-QLQ-C30 | EORTC-QLQ-C30 |
| Global health | 75 (58, 83) | 66 (50, 83) | 0.156 |
| Physical function | 93 (86, 100) | 86 (73, 86) | 0.001 |
| Role function | 100 (66, 100) | 83 (66, 100) | 0.174 |
| Emotional function | 66 (58, 75) | 66 (50, 75) | 0.786 |
| Cognitive function | 100 (83, 100) | 83 (66, 100) | 0.006 |
| Social function | 83 (66, 100) | 66 (66, 100) | 0.115 |
| Fatigue | 0 (0, 22) | 22 (0, 33) | 0.003 |
| Nausea and vomiting | 0 (0, 16) | 0 (0, 33) | 0.116 |
| Pain | 0 (0, 0) | 0 (0, 0) | 0.393 |
| Dyspnea | 0 (0, 0) | 0 (0, 33) | 0.002 |
| Insomnia | 0 (0, 33) | 0 (0, 33) | 0.069 |
| Appetite loss | 0 (0, 33) | 0 (0, 33) | 0.388 |
| Constipation | 0 (0, 0) | 0 (0, 0) | 0.611 |
| Diarrhea | 0 (0, 0) | 0 (0, 0) | 0.087 |
| Financial difficulties | 0 (0, 33) | 33 (0, 66) | 0.054 |
| EORTC-QLQ-STO22 | EORTC-QLQ-STO22 | EORTC-QLQ-STO22 | EORTC-QLQ-STO22 |
| Dysphagia | 0 (0, 0) | 0 (0, 0) | 0.783 |
| Abdominal pain | 0 (0, 8) | 8 (0, 25) | 0.014 |
| Reflux symptoms | 0 (0, 11) | 0 (0, 11) | 0.207 |
| Eating restrictions | 0 (0, 0) | 0 (0, 0) | 0.592 |
| Anxiety | 22 (0, 33) | 22 (0, 33) | 0.471 |
| Having dry mouth | 0 (0, 0) | 0 (0, 33) | 0.423 |
| Taste | 0 (0, 0) | 0 (0, 33) | 0.105 |
| Body image | 0 (0, 0) | 0 (0, 0) | 0.806 |
## Discussion
It is well known that malnutrition after GC surgery is closely associated with poor prognosis and decreased QOL. For a long time, dietary counseling has been performed to improve the nutritional status of patients after a gastrectomy; it is regarded as an essential and valuable tool for influencing nutritional status. However, the effect of dietary counseling on the nutritional status of patients after GC surgery is not clear. The patients in our study received individualized dietary counseling, and each patient’s nutritional goals and required nutritional support were individually defined. Therefore, our study provides evidence that an overall strategy of providing individualized dietary counseling based on calorie and protein requirements and a symptom assessment to achieve protein and calorie goals during postoperative recovery are beneficial to patients. Our findings validate some previous trials [15, 16] but contradict the findings of the meta-analysis [22], which reported that all nutritional counseling studies did not show significant differences. Another meta-analysis also reported, finding very low-quality evidence to support the effect of oral nutritional interventions on post-hospital weight and energy or protein intake [21]. However, there was no significant difference in 90-day readmission rates and blood parameters, which is also consistent with previous nutritional intervention studies [17].
Many studies have shown that, after TG, patients with more weight loss and more nutritional problems are more significant [31, 32]. Therefore, the effect of intervention measures on patients with TG will be more concerned. Subgroups analysis by surgery type showed that the intervention could significantly reduce BMI loss in patients undergoing TG and PG at 30, 60, and 90 days after surgery. This is similar to the results of the related nutritional intervention study in Japan [18, 33], which showed that oral enteral nutrition intervention in patients after GC surgery had a significant effect in patients with TG, but there was no difference in patients undergoing DG. Analysis of the reasons may be that patients with TG and PG face greater nutritional risks and require more scientific diet and nutritional interventions to meet their physical needs. Compared with TG and PG, patients undergoing DG face lower nutritional risks [34], and most patients can gradually adapt to postoperative changes in the gastrointestinal tract by adjusting their diets. Only in the early postoperative stage that some patients with severe gastrointestinal symptoms require further individualized counseling and management.
With respect to the QOL, it is always a major concern for the prognostic after nutritional support. Related research in Korea shows that follow-up management on nutritional intervention for patients undergoing gastrectomy will have a positive impact on their QOL [15]. Our research also supports these results, and nutritional status is closely related to QOL. Relevant studies in the United States have identified HRQOL issues related to dietary changes and restrictions after upper gastrointestinal cancer treatment, involve family caregivers, and are tailored and flexible to patient and family caregiver’s needs and preferences [35]. The recommendations of this study’s findings were fully considered in our study design. While the underlying reason for the effect on the QOL was not investigated in the present study, we speculated that the reduced fatigue, shortness of breath, and stomach pain may be associated with individualized dietary counseling to reasonably adjust food types and intake based on symptom assessment, and increased calorie and protein intake was associated with improved physical function and cognitive function.
Several points of this trial are worth mentioning. First, in our study, the patient was recovering from major abdominal surgery, and the calorie intake of the patients was about $25\%$ of the required amount at the time of discharge. All patients had NRS2002 scores of 4–5 with nutritional risk, so it was necessary to develop an individualized nutritional treatment plan based on clinical practice. Our research philosophy is to address the recent postoperative food intake restriction by improving food quality to improve the nutritional status of patients. This study is a comprehensive intervention plan that includes concepts of dietary counseling, ONS, symptom management, and ongoing management. Second, dynamic adjustment is made according to the patient’s dietary habits and actual intake of nutrients. The results of a Korean study show that simplified dietary education is ineffective for patients after GC surgery. It is very challenging to influence patients’ dietary habits to improve their nutritional status in surgical patients, especially after gastrectomy, and points out the need for consultation sufficient time, appropriate materials to support, and iterative and regular feedback [36]. Therefore, we adjust the composition of various nutrients in the food according to the patient’s actual food intake type and intake and increase the content of calories and protein in food as much as possible under the same tolerated volume. This adjustment was repeated, with at least five feedback and adjustments per patient. We help patients to form a high-protein, high-calorie, and small-volume dietary pattern suitable for the characteristics of the postoperative gastrointestinal tract. Third, regarding the time frequency of intervention and adjustment, according to the results of previous studies, the weight loss of patients with GC was the largest within 90 days after surgery, especially within 30 days after surgery [6, 14, 37]. Therefore, the duration of intervention in this study was determined to be within 90 days after surgery, which is a critical period for the reconstruction of patients’ dietary patterns. The main adjustment and feedback frequency are determined to be once a week within 30 days after surgery, once a month after the 30th day, and until the 90th day after surgery; particular questions can be consulted through the online clinic at any time. Finally, no specific adverse side effects of the intervention were observed in our study. To ensure the safety of patients, patients’ compliance was evaluated by the degree of completion of home dietary guidance and postoperative review (mainly includes loss of follow-up, whether to keep a food diary, and whether to conduct postoperative review at 90 days after surgery), and dietary intake was not used for compliance evaluation. We only set calorie and protein targets for patients and instruct and assist patients in adjust the amount of each meal, food characteristics, and cooking methods according to the symptoms after eating. We also applied the MDASI-C to truly reflect the burden of digestive tract symptoms of patients, screened the symptoms of emergency readmission and recommended prompt medical treatment, and stratified management of other minor symptoms, ensuring patients’ safety.
## Prospect of further research
First prospect of future research is to explore the mechanism of postoperative weight loss in patients with GC. In recent years, epigenetic studies have shown that many dietary components may indirectly influence genomic pathways for DNA methylation, and there is evidence of a biochemical link between nutritional quality and mental health [38]. Precision nutrition is an emerging area of nutrition research, with a primary focus on the individual variability in response to dietary and lifestyle factors [39]. Second, intelligent terminal products are developed, and the convenience of artificial intelligence and network is utilized to match the food types, the intake of each food, and the gastrointestinal symptoms of patients, so as to provide quantitative and dynamic whole-life nutrition management for postoperative patients with GC and to improve the overall nutritional status and QOL of patients with GC. Third, symptom management was used as the main method of nutritional intervention in patients with GC after operation to explore its influence on nutritional status and QOL.
We are aware of the limitations of our study. First, although the primary results at 30, 60, and 90 days were objective, and calorie and protein intake calculations were masked, and some of the outcomes assessed during the home setting might have been vulnerable to observer (patient and caregiver) bias. Second, three ($5.5\%$) patients in the intervention group and nine ($17.6\%$) patients in the control group did not record their diet as required, despite implementation of the post-discharge individualized dietary counseling by trained nurses 30 days after surgery, and the number of patients further increased at 60 days after surgery. Similar to real-life experience, several patient, treatment, and hospital factors (for example, patient delay or refusal to initiate enteral or parenteral nutrition, and interference of chemotherapy with nutritional support) might have prevented full adherence to the protocol. Third, chemotherapy starts at 30 days after surgery, and the impact of chemotherapy on patients cannot be tracked in detail. Although data collection should be avoided during the chemotherapy period and within 7 days after chemotherapy, the delayed side effects of chemotherapy drugs may affect patients. Fourth, the sample size of our study was small, and the observation period was short. Although individualized dietary counseling is too complex for an expanded population, some variables may be overlooked due to the small number of cases. Fifth, some patients were unable to return to the National Cancer Center for review at 90 days after surgery. Therefore, the study lacked body composition (fat content and muscle content), body measurements (waist circumference, arm circumference, etc.), and PG-SGA scores that required face-to-face assessment. Only changes in BMI and food intake reflect nutritional status. Finally, in our study, because of the short intervention time and limited research funds, epigenetics and postoperative metabolic changes (blood glucose, blood pressure, and blood lipid) and other related results were not collected. We did not yet investigate the costs of the intervention, but we have planned to do a future cost-effectiveness analysis on the basis of the trial data (researcher’s time cost, economic cost, etc.); it is planned to adopt artificial intelligence assistance system to benefit more patients.
In conclusion, the trial showed that early use of individualized dietary guidance to help patients achieve protein and calorie goals after GC surgery could effectively increase energy and protein intake, reduce BMI loss, and improve QOL. The nutritional problems of patients after GC surgery are severe and complex. Further multi-center individualized dietary counseling research is needed to determine individualized interventions for different population characteristics, different surgical procedures, and different symptom burdens so as to improve the nutritional status of patients after GC surgery.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
This study was approved by the hospital ethics committee, and the ethics approval number is $\frac{21}{281}$-2952. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
All authors made substantial contributions to the intellectual content of this paper. HY: protocol design, patient management, data analysis, and manuscript writing; FH: program design and supervision; JW: data collection and manuscript writing; QZ: program design and nutritional guidance; CG: medical processing and data analysis; JN: patient management, data collection, and collation; FY: supervision and critical review; YC: protocol design, supervision, and critical review. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Effect of sea buckthorn extract on production performance, serum biochemical
indexes, egg quality, and cholesterol deposition of laying ducks
authors:
- Bing-nong Yao
- Fu-you Liao
- Jiao-yi Yang
- Ai Liu
- Jiao Wang
- Bao-guo Zhu
- Gang Feng
- Sheng-lin Yang
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10008885
doi: 10.3389/fvets.2023.1127117
license: CC BY 4.0
---
# Effect of sea buckthorn extract on production performance, serum biochemical indexes, egg quality, and cholesterol deposition of laying ducks
## Abstract
The purpose of this experiment was to study the effect of sea buckthorn extract (SBE) supplementation on the production performance, serum biochemical indexes, egg quality, and cholesterol deposition of laying ducks. A total of 240 23-week-old laying ducks (female ducks) with similar body weight were randomly divided into four treatment groups with 6 replicates of 10 each. The experimental groups were fed diets supplemented with 0, 0.5, 1.0, and 1.5 g/kg of SBE, respectively. The results showed that the addition of 1.0 g/kg SBE to the diet had significant increase ($P \leq 0.05$) in average egg weight and feed conversion ratio. The inclusion of SBE showed the significant improvement ($P \leq 0.05$) in yolk weight, shell strength, egg white height and haugh unit. Ducks fed with 1.0 and 1.5 g/kg SBE displayed a significant decrease ($P \leq 0.05$) in yolk cholesterol. The significant improvements were observed in the contents of total amino acid essential amino acids, non-essential amino acids, umami amino acids, monounsaturated fatty acids, and docosahexenoic acids of eggs ($P \leq 0.05$) when supplemented with SBE. However, the contents of total saturated fatty acids, polyunsaturated fatty acids, n-3 polyunsaturated fatty acids and n-6 polyunsaturated fatty acids in eggs showed decrease when ducks fed with SBE diets ($P \leq 0.05$). SBE diets may reduce ($P \leq 0.05$) the levels of serum total cholesterol, triglyceride, and low-density lipoprotein cholesterol, while increased ($P \leq 0.05$) the levels of serum superoxide dismutase, total antioxidant capacity, and glutathione catalase compared to the control. The levels of serum immunoglobulin G, immunoglobulin A and immunoglobulin M were improved in SBE diets ($P \leq 0.05$) in comparation to the control. The addition of SBE to diets can improve feed nutrient utilization, increase egg weight, optimaze egg quality and amino acid content in eggs, reduce blood lipids, improve fatty acid profile and yolk cholesterol in eggs, and increase antioxidant capacity and immunity in laying ducks.
## 1. Introduction
Meat and egg products from poultry serve as a source of food for humans, especially eggs can supply humans with high quality proteins, of which eggs are rich in many umami amino acids such as glutamic acid and glycine [1]. Fatty acids in eggs are also beneficial for human health, among which n-3 polyunsaturated fatty acids in eggs are effective in preventing cardiovascular diseases [2], but the docosahexaenoic acid (DHA) content of n-3 polyunsaturated fatty acids in poultry meat and eggs is known to be very low [3]. Fat and cholesterol enriched in eggs are mainly synthesized in the liver and transported via the blood to be deposited in eggs [4, 5]. Generally, consumption of one to two eggs per day is sufficient to meet the body's dietary cholesterol requirements, and excessive intake can cause cardiovascular disease. Therefore, how to balance the nutritional structure in eggs has become a hot research topic. On the other hand, the use of antibiotics has led to the detection of antibiotic residues in a large number of livestock and poultry products, posing a safety risk to human health [6]. Therefore, countries have introduced a series of regulations for the use of antibiotics, and China has explicitly prohibited the use of antibiotics in feed, and it has become a trend to find alternative antibiotic products from feed additives. It has been reported that plants and plant extracts can be used as feed additives to improve egg production performance, egg quality and immunity of chickens, and to reduce cholesterol levels [7]. As everyone knows, plants are rich in many natural bioactive compounds, such as flavonoids and polysaccharides, among which mulberry leaf flavonoids can improve egg production and antioxidant capacity of chickens and improve feed conversion ratio [8].
Sea buckthorn (Hippophae rhamnoides L., SBT) for the elaeagnaceae family, the genus of sea buckthorn. SBT is cold and drought tolerant and can grow in harsh environments. SBT is abundant and native to China and is distributed all over the world, including England and France [9]. Feeding of SBT to broiler diets can improve feed conversion [10]. SBT is a medicinal and food plant, and the flavonoids, polysaccharides, and other bioactive compounds extracted from the stems, leaves, fruits, and seeds of SBT are collectively known as Sea buckthorn extract (SBE) [11]. Among them, Seabuckthorn flavonoids (SBF) has the largest proportion and is the most abundant active compound in SBT. SBE has high medicinal value and the medicinal components are isorhamnetin, quercetin, and kaempferol [12], and its medicinal functions are lipid-lowering (cholesterol, triglycerides, etc.) [ 13], antioxidant [14], anticancer, and immunomodulation [15], etc. It has been reported that SBT and SBE were widely used as feed additives in pig [16], cattle [17], chicken [18], and other production tests. To our knowledge, few studies on effect of SBE in ducks have been reported, especially, little is known about the study of amino acids, fatty acid profile, and egg quality in duck eggs.
Guangxi small hemp duck is a local Chinese poultry breed, produced in Guangxi, China, and is an egg-laying duck with good egg production performance. However, duck eggs contain higher levels of cholesterol and fat than chicken eggs and quail eggs, and humans are concerned about the health risks associated with high fat and cholesterol levels when consuming them. Therefore, we hypothesized that the addition of SBE to the diet could improve the production performance of laying ducks, improve egg quality, improve the amino acid and fatty acid structure of eggs, and reduce egg cholesterol. The aim of this study was to investigate the effect of SBE on production performance, serum biochemical parameters, egg quality, amino acid, fatty acid profile of egg, and cholesterol deposition during the laying period, and to provide a theoretical basis for the development of SBE as a functional feed additive for laying ducks.
## 2. Materials and methods
This experiment was reviewed and approved by the Guizhou University Sub-Committee of Experimental Animal Ethics (Guiyang, China; No. EAE-GZU-2021-E012).
## 2.1. Experiment material
The raw material of sea buckthorn extract (powdered form) used in the experiment was sea buckthorn fruit, which was produced in Shaanxi, China. The active ingredient of SBE was detected by high performance liquid chromatography-mass spectrometry, and the extractant was alcohol. 286.23 mg/g of flavonoids, 103.79 mg/g of quercetin, and 23.66 mg/g of kaempferol constituted the main components of SBE, and the purity of SBE was $40.31\%$. Another $20.18\%$ was sea buckthorn polysaccharide. Produced by a company in Shanxi, China.
## 2.2. Experimental design
This experiment was carried out in the research duck farm of Guizhou University from September 2021 to December 2021. The experimental ducks, called Guangxi small hemp duck, were purchased from a farm in Guangxi, China, A total of two hundred and forty 23-week-old laying ducks (female ducks) with similar body weight (BW, 1,250 ± 60 g; mean ± standard deviation) were applied and randomly divided into four groups, every group contained six replicates of 10 ducks each. The experimental diets were supplemented with 0.5, 1.0, and 1.5 g/kg SBE, respectively. The basal rations were formulated according to the Criterion of Nutrients Requirements of laying ducks (SAC, GBT/41189-2021; China, 2021). The basal diet formula and nutritional level are shown in Table 1. Nutrients in the diets were tested using the AOAC method [19]. The adaptation period of this experiment was 14 days, and the formal experimental period was 70 days. During the experiment, the ducks were provided with natural and artificial lighting to ensure 16 h of light each day, and the relative humidity was kept at about $65\%$. Feeding and drinking water freely throughout the feeding period, and the duck house was cleaned once a day.
**Table 1**
| Ingredients, % | Content, % |
| --- | --- |
| Corn | 55.75 |
| Soybean meal | 27.40 |
| Wheat bran | 1.50 |
| Rapeseed cake | 4.00 |
| CaHPO4 | 2.75 |
| Limestone | 7.25 |
| NaCl | 0.35 |
| Premixa | 1.0 |
| Total | 100 |
| Nutrient levels b | Nutrient levels b |
| Metabo lizable energy, MJ/kg | 10.65 |
| Crude protein | 18.10 |
| Crude fiber | 3.07 |
| Calcium | 3.37 |
| Total phosphorus | 0.63 |
| Lysine | 0.92 |
| Methionine | 0.27 |
| Methionine + cysteine | 0.61 |
## 2.3.1. Production performance
The daily feed intake was recorded according to replicates during the experiment. The ducks were fed at 7:30 and 17:00 daily, and duck eggs were collected every morning before feeding, numbered and weighed. The egg-laying ducks were weighed every Sunday at 8:00 (fasting ducks for 12 h before weighing). At the same time, the number of deaths of laying ducks was recorded for calculating the average daily feed intake [ADFI (g/d) = cumulative feed intake/(number of birds × number of days)], average egg weight (AEW = total daily egg mass/laying number), laying rate [LR (%) = (laying number/layer number) × 100], and feed conversion ratio [FCR = total feed intake/total egg weight] of the laying ducks. Used to calculate the production performance of egg ducks.
## 2.3.2. Serum biochemical indices
On day 70 of the experiment, 2 laying ducks with similar body condition were randomly selected from each replicate (12 laying ducks in each group) for blood collection, and the ducks drank freely and fasted for 12 h before blood collection. The blood samples were collected from the vein under the wing of common collecting blood vessel for 5 mL, left at room temperature for 2 h, centrifuged at 1,487 xg/min for 15 min, and the serum was extracted, stored at −80°C for determination of serum biochemical parameters. Serum biochemical indicators include total cholesterol (TC, COD-PAP method), triglyceride (TG, GPO-PAP enzymatic method), high-density lipoprotein cholesterol (HDL-C, direct method), low-density lipoprotein cholesterol (LDL-C, direct method), glutathione catalase (GSH-Px, colorimetric method), total superoxide dismutase (SOD, extraction method), total antioxidant capacity (T-AOC, ratio of Color method), malondialdehyde (MDA, TBA method). Determination of duck serum immunoglobulin G (IgG, Elisa method), immunoglobulin A (IgA, Elisa method), and immunoglobulin M (IgM, Elisa method) using Elisa kits. The kit was sourced from Nanjing Jiancheng Bioengineering Research Institute Co., Ltd., China, and the assay methods and steps were operated in accordance with the kit instructions. The detection instrument used in the experiment was PowerWaveXS type full-wavelength microplate reader (Bio-tek Instruments. Inc.USA).
## 2.3.3. Egg quality index
On the 69th day of the experiment, three fresh duck eggs with similar morphology were randomly selected in each replicate for egg quality determination (18 eggs per group), the following indicators were measured according to the method of “Performance ferms and measurement for poultry” (NY/T 823-2004) [20]. The measuring instruments are listed as follows: Egg weight (DJ-A1000 electronic balance, Connecticut HZ Electronics Co., Ltd., USA), egg shell strength (EFA-01 egg shell strength tester, Orka, Israel), egg shell thickness (MNT-150T digital Vernier caliper, Shanghai Minette Industrial Co., Ltd., China), egg shape index (calculated by vernier caliper), egg yolk specific gravity (calculated by electronic balance weighing), egg white height (EA-01 egg quality tester, Orka, Israel), egg yolk Color (EA-01 egg quality tester, Orka, Israel), Haugh Units (EA-01 egg quality tester, Orka, Israel).
Three fresh duck eggs were randomly selected from each replicate for the determination of yolk cholesterol on the 69th day of the experiment. First, break the fresh duck egg to separate the yolk, weigh 1 g of egg yolk in the middle of the egg yolk and place it in a 10 mL centrifuge tube, add 10 mL of anhydrous ethanol, and mix thoroughly to obtain the sample to be tested, which is used to determine the total protein and total cholesterol of the egg yolk, and then the cholesterol content of the egg yolk was calculated using the formula. Determination formula: Yolk cholesterol (mmol/gprot) = (Asample-Ablank)/(Acalibration-Ablank)×Ccalibrators×Cstandard/(W/V), (Asample, sample OD value; Acalibration, calibrate the OD value; Ablank, blank OD value; Ccalibrators, calibrator concentration, mmol/L; Cstandard, protein concentration of the sample to be tested, gprot/L; W, sample quality, g; V, the total volume of ethanol added, L; prot, protein.). Wavelengths of 570 and 440 nm.
## 2.3.4. Detection of amino acids and fatty acids in eggs
On the 70th day of the experiment, three fresh duck eggs were randomly selected from each replicate (18 duck eggs in each group, fully mixed, and three samples taken for testing). At first, the fresh duck eggs were dried in a vacuum freeze-dryer (LYOQUEST-85PLUS, Telstar Electromechanical Equipment Shanghai Co., Ltd., China) until Constant weight was reached, secondly, the egg powder samples were obtained by grinding with high-speed multi-function grinder (model 800Y, Wuyi County Hainer Electric Co., Ltd., China).
Amino acids in whole eggs were analyzed according to the national standard for food safety GB/T5009.124-2016 [21]. The brief steps are as follows: add 10–15 mL 6 mol/L hydrochloric acid solution and 4 drops of phenol into the hydrolysis tube, after freezing the hydrolysis tube for 5 min and then connected to the suction tube of the vacuum pump, evacuated (close to 0 Pa), filled with nitrogen gas. The vacuum-nitrogen filling step was repeated three times. The sealed tube was hydrolyzed in a 110°C hydrolysis furnace for 22 h, then cooled to room temperature and detected by an automatic amino acid analyzer (Biochrom 30, Biochrom Ltd., UK) at wavelengths of 570 and 440 nm. The 16 amino acids are as follows: aspartic acid (Asp), threonine (Thr), serine (Ser), glutamic acid (Glu), glycine (Gly), alanine (Ala), valine (Val), methionine (Met), isoleucine (Ile), leucine (Leu), tyrosine (Tyr), phenylalanine (Phe), histidine (His), Lysine (Lys), arginine (Arg), and proline (Pro). All experimental steps were completed in strict accordance with the standard instructions, and the difference of the measurement results did not exceed $12\%$ of the arithmetic mean.
The fatty acids in whole eggs were analyzed according to GB5009.168-2016 [22], the national standard for food safety. Brief steps are as follows: weigh 2 g of sample into a 50 mL test tube, add 20 mL of chloroform and 10 mL of methanol, sonicate for 10 min, and shake for 2 h. Add 6 mL of $0.9\%$ sodium chloride aqueous solution, shake for 30 s, and let stand at 4°C for 22 h, then centrifuge at 3,500 r/min for 10 min, collect the lower chloroform layer solution, filter with filter paper. The filtrate was placed in a dry flask and dried in a vacuum drying oven. The single fatty acid methyl ester standard solution and the fatty acid methyl ester mixed standard solution were injected into the gas chromatograph separately to characterize the peaks. The individual fatty acids were analyzed by gas chromatograph (Agilent GC 6890N, Agilent, USA). The gas chromatographic conditions were as follows: the capillary column was a poly (dicyanopropyl siloxane) strongly polar stationary phase (100 m × 0.25 mm × 0.2 μm). The injector temperature was set at 270°C; the detector temperature was set at 280°C. The initial temperature program was 100°C for 13 min, 10°C/min to 180°C, duration 6 min, 1°C/min to 200°C, duration 6 min, and 4°C/min to 230°C, duration 6 min. The carrier gas is nitrogen, the split ratio was 100:1, and the injection volume was 1.0 μL. All experimental steps were performed in strict accordance with the standard requirements, and the difference of the measurement results did not exceed $10\%$ of the arithmetic mean.
## 2.4. Statistical analysis
All data were analyzed by analysis of variance using the general linear model program of SPSS 25 (One-way ANOVA, LSD), and Duncan's multiple comparison test was used. A p-value of <0.05 was considered statistically significant. The experimental results of each group were expressed using the mean and standard error of the mean (SEM).
## 3.1. Production performance
The effect of dietary SBE on production performance is listed in Table 2, the addition of 1.0 g/kg SBE significantly increased ($P \leq 0.05$) the average egg weight and feed conversion ratio of laying ducks. However, the addition of SBE to the diet had no significant ($P \leq 0.05$) effect on the average daily feed intake and laying rate of laying ducks. Although the difference in egg production rate was not significant, it can be found from Figure 1A that feeding 0.5 g/kg of SBE had a tendency to increase egg production rate in the middle part of the experiment, indicating that SBE has some effect in increasing egg production rate of ducks. Combined with the Figure 1B, the average daily feed intake was generally not very different between groups and the trend was not obvious.
## 3.2. Serum biochemical indices
Effect of dietary SBE on serum biochemical indexes is presented in Table 3, compared to the control group the addition of 1.0 and 1.5 g/kg SBE significantly reduced ($P \leq 0.05$) the serum TC and TG levels, while serum GSH-Px and SOD levels were significantly higher ($P \leq 0.05$), and 1.5 g/kg SBE group significantly reduced ($P \leq 0.05$) levels of serum LDL-C. However, 0.5 g/kg SBE group significantly increased ($P \leq 0.05$) levels of serum T-AOC, IgG, IgA, and IgM than control group. No significant ($P \leq 0.05$) effect on the levels of HDL-C and MDA were detected among all groups.
**Table 3**
| Itemsb | SBE add levels, g/kg | SBE add levels, g/kg.1 | SBE add levels, g/kg.2 | SBE add levels, g/kg.3 | SEM | P-value |
| --- | --- | --- | --- | --- | --- | --- |
| Itemsb | | | | | | |
| | Control | 0.5 | 1.0 | 1.5 | | |
| Serum lipid index | Serum lipid index | Serum lipid index | Serum lipid index | Serum lipid index | Serum lipid index | Serum lipid index |
| TC, mmol/L | 4.074a | 3.434ab | 3.235b | 3.301b | 0.144 | 0.014 |
| TG, mmol/L | 4.040a | 3.126ab | 2.161b | 2.762b | 0.226 | 0.019 |
| HDL-C, mmol/L | 3.208 | 3.520 | 3.975 | 3.353 | 0.122 | 0.181 |
| LDL-C, mmol/L | 5.824a | 5.313ab | 4.893ab | 3.787b | 0.331 | 0.001 |
| Serum antioxidant index | Serum antioxidant index | Serum antioxidant index | Serum antioxidant index | Serum antioxidant index | Serum antioxidant index | Serum antioxidant index |
| SOD, U/mL | 81.781b | 89.478a | 88.597a | 85.619ab | 0.936 | 0.046 |
| T-AOC, U/mL | 2.282b | 2.544b | 3.679a | 3.402a | 0.157 | 0.002 |
| MDA, nmol/mL | 8.824 | 10.441 | 9.471 | 8.721 | 0.518 | 0.546 |
| GSH-Px, U/L | 136.559b | 155.544a | 147.401ab | 140.396b | 2.502 | 0.043 |
| Serum immunity index | Serum immunity index | Serum immunity index | Serum immunity index | Serum immunity index | Serum immunity index | Serum immunity index |
| IgG, g/L | 1.807b | 2.342a | 1.543c | 1.618bc | 0.065 | < 0.001 |
| IgA, g/L | 0.220b | 0.251a | 0.158d | 0.185c | 0.007 | < 0.001 |
| IgM, g/L | 0.384b | 0.537a | 0.283c | 0.238d | 0.019 | < 0.001 |
## 3.3. Egg quality
Effect of dietary SBE on egg quality is given in Table 4, Compared to the control group, supplementation with 0.5 g/kg SBE to the diet significantly increased ($P \leq 0.05$) the egg shell strength of duck eggs, adding of 0.5 and 1.0 g/kg SBE significantly increased ($P \leq 0.05$) the yolk weight of duck eggs, and 1.5 g/kg SBE group significantly increased ($P \leq 0.05$) the egg white height and haugh unit of duck eggs. In addition, feeding 1.0 and 1.5 g/kg SBE significantly reduced ($P \leq 0.05$) yolk cholesterol. However, SBE did not affect ($P \leq 0.05$) the egg shape index, shell thickness, yolk color and yolk specific gravity of duck eggs in comparison with the control group.
**Table 4**
| Itemsb | SBE add levels, g/kg | SBE add levels, g/kg.1 | SBE add levels, g/kg.2 | SBE add levels, g/kg.3 | SEM | P-value |
| --- | --- | --- | --- | --- | --- | --- |
| Itemsb | | | | | | |
| | Control | 0.5 | 1.0 | 1.5 | | |
| Average egg weight, g | 64.661b | 65.154b | 68.383a | 64.476b | 0.356 | < 0.001 |
| Egg-shaped index | 1.357 | 1.368 | 1.361 | 1.351 | 0.006 | 0.789 |
| Eggshell strength, kgf/cm2 | 45.517b | 53.419a | 48.506ab | 47.489b | 0.988 | 0.037 |
| Eggshell thickness, mm | 0.231 | 0.234 | 0.239 | 0.231 | 0.002 | 0.515 |
| Yolk weight g | 19.001c | 20.544ab | 20.846a | 19.714bc | 0.182 | 0.001 |
| Yolk specific gravity | 0.300 | 0.309 | 0.310 | 0.303 | 0.002 | 0.090 |
| Albumen height, mm | 6.293b | 6.444ab | 6.473ab | 6.867a | 0.103 | 0.036 |
| Yolk color | 11.278 | 11.222 | 11.444 | 10.611 | 0.14 | 0.167 |
| Haugh unit | 75.993b | 76.750b | 77.479ab | 80.571a | 0.613 | 0.041 |
| Yolk cholesterol, mmol/gprot | 0.516a | 0.459ab | 0.416b | 0.444b | 0.012 | 0.026 |
## 3.4. Amino acids in eggs
The effect of addition of SBE to the diet on amino acids in eggs is listed in Table 5. The inclusion of SBE significantly increased ($P \leq 0.05$) the content of essential amino acids and total amino acids compared with the control group, while the content of non-essential amino acids in eggs was significantly increased ($P \leq 0.05$) when adding of 0.5 and 1.0 g/kg SBE. In addition, SBE significantly increased ($P \leq 0.05$) the contents of threonine, serine, leucine, phenylalanine, histidine, lysine, aspartic acid, and methionine in eggs. Interestingly, the addition of 0.5 and 1.0 g/kg of SBE significantly increased ($P \leq 0.05$) the content of glutamic acid and tyrosine in the fresh tasting amino acids of eggs. SBE did not affect ($P \leq 0.05$) the content of arginine in eggs.
**Table 5**
| Items, %b | SBE add levels, g/kg | SBE add levels, g/kg.1 | SBE add levels, g/kg.2 | SBE add levels, g/kg.3 | SEM | P-value |
| --- | --- | --- | --- | --- | --- | --- |
| Items, %b | | | | | | |
| | Control | 0.5 | 1.0 | 1.5 | | |
| Asparagine* | 3.852b | 4.088a | 4.176a | 4.040a | 0.04 | 0.005 |
| Threonine | 2.450b | 2.587a | 2.629a | 2.619a | 0.026 | 0.019 |
| Serine | 3.328b | 3.464a | 3.494a | 3.444a | 0.024 | 0.032 |
| Glutamic acid* | 5.786b | 6.197a | 6.192a | 5.803b | 0.065 | 0.001 |
| Glycine* | 1.431b | 1.477ab | 1.525a | 1.510a | 0.013 | 0.010 |
| Alanine* | 2.117b | 2.127b | 2.222a | 2.176ab | 0.015 | 0.025 |
| Valine | 2.640c | 2.734bc | 2.795ab | 2.826a | 0.024 | 0.004 |
| Methionine | 1.308c | 1.728a | 1.386b | 1.674a | 0.055 | < 0.001 |
| Isoleucine | 1.982c | 2.013bc | 2.093a | 2.067ab | 0.015 | 0.012 |
| Leucine | 3.524b | 3.665a | 3.731a | 3.695a | 0.029 | 0.027 |
| Tyrosine* | 2.010b | 2.190a | 2.151a | 2.075b | 0.024 | 0.005 |
| Phenylalanine* | 3.182b | 3.334a | 3.390a | 3.334a | 0.028 | 0.015 |
| Histidine | 0.965b | 1.009a | 1.030a | 1.013a | 0.009 | 0.025 |
| Lysine | 3.078b | 3.214a | 3.286a | 3.237a | 0.027 | 0.011 |
| Argnine | 2.371 | 2.428 | 2.476 | 2.461 | 0.016 | 0.089 |
| Proline | 1.634b | 1.670b | 1.755a | 1.718ab | 0.009 | 0.009 |
| TAA | 41.660b | 43.926a | 44.331a | 43.693a | 0.363 | 0.011 |
| EAA | 19.130b | 20.284a | 20.341a | 20.465a | 0.185 | 0.007 |
| NEAA | 22.530c | 23.642ab | 23.990a | 23.228bc | 0.192 | 0.011 |
| UAA | 18.379c | 19.414ab | 19.656a | 18.939bc | 0.168 | 0.006 |
| EAA/TAA | 45.92 | 46.18 | 45.88 | 46.84 | | |
| EAA/NEAA | 84.91 | 85.80 | 84.79 | 88.10 | | |
## 3.5. Fatty acid profile in eggs
The effect of addition of SBE to the diet on fatty acids in eggs is listed in Table 6. Compared to the control group, feeding of 1.0 g/kg SBE significantly reduced ($P \leq 0.05$) the content of saturated fatty acids (SFA) in eggs. In addition, duck eggs with SBE displayed significantly decrease ($P \leq 0.05$) in the contents of pentadecanoic acid (C15:0), heptadecanoic acid (C17:0), heneicosan ic acid (C21:0), elaidic acid (C18:ln9t), heptadecenoic acid (C17:1), linoleic acid (C18:2n6c), linolelaidic acid (C18:2n6t), eicosadienoic acid (C20:2), α-linolenic acid (ALA: C18:3n3), docosapentaenoic acid (DPA: C20:5), and eicosatrienoic acid (C20:3n6). However, the contents of total MUFA of eggs in SBE groups were higher than ($P \leq 0.05$) that in the control group. Among them, the contents of oleic acid (C18:ln9c) and gondoic acid (C20:ln9) in MUFA were significantly increased ($P \leq 0.05$). Although SBE reduced the content of total polyunsaturated fatty acids (PUFA), but significantly increased ($P \leq 0.05$) the content of docosahexaenoic acid (DHA: C22:6n-3) when adding 1.0 and 1.5 g/kg SBE to the diets. In contrast, SBE had no obvious ($P \leq 0.05$) effect on the content of lauric (C12:0) and myristic (C14:0) acids in eggs.
**Table 6**
| Items, %b | SBE add levels, g/kg | SBE add levels, g/kg.1 | SBE add levels, g/kg.2 | SBE add levels, g/kg.3 | SEM | P-value |
| --- | --- | --- | --- | --- | --- | --- |
| Items, %b | | | | | | |
| | Control | 0.5 | 1.0 | 1.5 | | |
| Saturated fatty acid | Saturated fatty acid | Saturated fatty acid | Saturated fatty acid | Saturated fatty acid | Saturated fatty acid | Saturated fatty acid |
| Lauric acid, C12:0 | 0.019 | 0.016 | 0.017 | 0.016 | 0.0006 | 0.115 |
| Myristic acid, C14:0 | 0.420 | 0.456 | 0.432 | 0.468 | 0.009 | 0.159 |
| Myristoleate, C15:0 | 0.028a | 0.022b | 0.021c | 0.020c | 0.0009 | < 0.001 |
| Palmitate, C16:0 | 23.686b | 23.577b | 23.618b | 24.203a | 0.086 | 0.005 |
| Margaric acid, C17:0 | 0.097a | 0.069b | 0.066b | 0.057c | 0.005 | < 0.001 |
| Stearic acid, C18:0 | 5.909a | 5.849a | 5.320b | 5.267b | 0.094 | < 0.001 |
| Arachidic acid, C20:0 | 0.036a | 0.037a | 0.032b | 0.026c | 0.001 | < 0.001 |
| Heneicosanoic acid, C21:0 | 0.016b | 0.019a | 0.019a | 0.019a | 0.0005 | 0.041 |
| Behenic acid, C22:0 | 0.279b | 0.303a | 0.281b | 0.284b | 0.003 | 0.001 |
| lignoceric acid, C24:0 | 0.041a | 0.040a | 0.033b | 0.034b | 0.001 | < 0.001 |
| Monounsaturated fatty acid | Monounsaturated fatty acid | Monounsaturated fatty acid | Monounsaturated fatty acid | Monounsaturated fatty acid | Monounsaturated fatty acid | Monounsaturated fatty acid |
| Myristoleate, C14:1 | 0.034b | 0.042b | 0.041b | 0.059a | 0.003 | 0.001 |
| Palmitoleate, C16:1 | 2.675bc | 2.474c | 2.704b | 2.997a | 0.067 | 0.001 |
| Heptadecenoic acid, C17:1 | 0.078a | 0.067bc | 0.070b | 0.065c | 0.002 | 0.001 |
| Elaidic acid, C18:ln9t | 0.208a | 0.075c | 0.163b | 0.137b | 0.020 | < 0.001 |
| Oleic acid, C18:ln9c | 55.165c | 56.338b | 57.082a | 56.357b | 0.225 | 0.001 |
| Eicosenoic acid, C20:ln9 | 0.020c | 0.235a | 0.125b | 0.105b | 0.044 | < 0.001 |
| Polyunsaturated fatty acid | Polyunsaturated fatty acid | Polyunsaturated fatty acid | Polyunsaturated fatty acid | Polyunsaturated fatty acid | Polyunsaturated fatty acid | Polyunsaturated fatty acid |
| Linolelaidic acid, C18:2n6t | 0.083a | 0.006c | 0.056b | 0.064b | 0.011 | < 0.001 |
| Linoleic acid, C18:2n6c | 9.162a | 8.322b | 7.834bc | 7.585c | 0.197 | 0.001 |
| Linolenic acid methyl ester, C18:3n3 (ALA) | 0.370a | 0.105c | 0.216b | 0.193b | 0.044 | < 0.001 |
| Methyl linolenate, C18:3n6 | 0.147b | 0.145b | 0.165a | 0.164a | 0.003 | < 0.001 |
| Eicosadienoic acid, C20:2 | 0.190a | 0.184b | 0.174c | 0.142d | 0.006 | < 0.001 |
| Eicosatrienoic acid, C20:3n6 | 0.211a | 0.136bc | 0.118c | 0.164b | 0.021 | < 0.001 |
| Arachidonic acid, C20:4n6 | 1.357a | 1.261b | 1.152c | 1.307ab | 0.025 | 0.001 |
| Diphenylamine, C22:5n3 (DPA) | 0.150a | 0.103b | 0.104b | 0.097b | 0.006 | < 0.001 |
| Docosahexaenoic Acid, C22:6n3 (DHA) | 0.037b | 0.036b | 0.089a | 0.090a | 0.012 | < 0.001 |
| Total SFAs | 32.941b | 32.773b | 32.458c | 33.319a | 0.102 | 0.001 |
| Total MUFAs | 63.729b | 65.005a | 65.343a | 64.851a | 0.105 | 0.003 |
| Total PUFAs | 9.435a | 8.513b | 8.064bc | 7.790c | 0.036 | 0.001 |
| Total n-3 PUFAs | 0.557a | 0.245c | 0.410b | 0.379b | 0.053 | < 0.001 |
| Total n-6 PUFAs | 10.961a | 9.871b | 9.325b | 9.283b | 0.020 | 0.001 |
## 4. Discussion
In laying duck farming production, economic efficiency is usually increased by increasing the average egg weight or improving the feed conversion ratio. Egg white is one of the main factors affecting the weight of eggs. Egg white are reported to consist of ovalbumin and oval mucin, and are secreted and synthesized in the enlarged portion of the oviduct (also known as the protein-secreting portion) [23]. The synthesis and secretion of ovalbumin is regulated by estrogens [24]. Among them, yolk protein is the main protein in egg yolk, and estrogen induces the synthesis of yolk protein [25]. It has been reported that quercetin in flavonoids can increase the synthesis of estrogen [26], thus regulate yolk protein synthesis, increasing yolk and egg white weights, and it is noteworthy that the increased yolk weight in this study was an important cause of the increased egg weight. Chand et al. [ 27] confirmed that the addition of sea buckthorn seeds to the ration increased the weight of eggs. However, SBE does not consistently increase egg weight, and one study showed that estrogen promotes calcium absorption [28], but excessive flavonoids inhibit estrogen production [29], therefore, the lack of calcium leads to egg weight loss. There was no significant difference in average daily feed intake between the groups in this study, however, the increase in egg weight led to a decrease in feed conversion ratio, this was confirmed by the findings of BenMahmoud et al. [ 10].
TG in egg yolk is synthesized by the liver, transported to the ovary via the bloodstream, and absorbed into the developing follicle via receptor-mediated endocytosis [4]. Studies have shown that flavonoids can downregulate several adipogenic gene transcription factors, thereby reducing TG levels [30], and Yang et al. [ 31] showed that SBF can reduce serum TG levels. The source of TC is mainly through two routes: in vivo synthesis and dietary intake, with in vivo synthesis being mainly by the liver and, to a lesser extent, by the ovaries. Dietary intake is obtained through food. In addition to the TC required for the maintenance of the body, the remaining $\frac{2}{3}$ of the TC in female birds is transported by the carrier LDL-C through the blood to the ovary, where it enters the follicle through receptor-mediated endocytosis, is deposited in the yolk, and is finally excreted by egg laying [30]. During the synthesis of TC, HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) reductase serves as a key rate-limiting enzyme in the TC synthesis pathway, and SBF inhibits the synthesis of HMG-CoA reductase, thereby inhibiting TC synthesis [32]. Some studies have confirmed that consumption of sea buckthorn fruit flavonoids can reduce blood TG and TC levels [18]. In the current study, addition of 1.5 g/kg SBE, reduced serum LDL-C levels, which is consistent with the results of Krejcarová et al. [ 33] and Ma et al. [ 34], confirming the ability of SBE to reduce lipids. This indicates that the addition of SBE to the diet can reduce cholesterol deposition in Egg.
Studies have demonstrated that quercetin can reduce oxidative stress in follicular granulosa cells and ensure normal ovarian development [35]. Moreover, antioxidants can delay ovarian decline and increase the useful life of laying hens [36]. The metabolism of the body is accompanied by an oxidative process that generates free radicals along with the formation of reactive oxygen species (ROS) and reactive nitrogen species (RNS) that are harmful to the body, such as superoxide and hydrogen peroxide, and the body maintains the oxidative and antioxidant balance by scavenging free radicals [37]. And the antioxidant effect is by enhancing the activity of antioxidant enzymes and inhibiting the activity of related oxidative enzymes, SOD and GSH-Px among antioxidant enzymes can induce the production of ROS scavenging enzymes. Studies have shown that SBF regulates Peroxidase through the Nrf2-ARE (nuclear related factor 2-antioxidant response element) signaling pathway, and SOD is responsible for the breakdown of superoxide anions into H2O2 and O2. GSH-Px further reduces the active peroxide to harmless alcohol and water [38]. T-AOC reflects the body's ability to resist oxidation. In the present study, SBE could increase the content of SOD, GSH-Px, and T-AOC in serum, which indicated that SBE may improve the anti-oxidation ability of laying ducks and slow down the damage of oxidative stress. However, the content of SOD and GSH-Px in serum were decreased with further addition of SBE, indicating that excessive SBE could not further improve the antioxidant capacity of laying ducks.
The immunity of the organism is related to immune factors. Sea buckthorn fruit flavonoids improve the immunity of the organism by modulating immune-related regulatory factors in vitro and stimulating pro-inflammatory factors (IL-6, interleukin-6) and tumor necrosis factor (TNF-α, tumor necrosis factor-α) [39]. The intestine is the largest immune organ of the animal organism [40]. Attri et al. [ 41] found that sea buckthorn juice increased the diversity of Lactobacillus and Bacteroides in the colonic site, and a substantial increase in probiotics such as Bifidobacterium was found in the descending colonic site, and Bifidobacterium inhibited harmful bacteria, improve gastrointestinal barrier function, maintain intestinal microecological stability, and regulate intestinal immune homeostasis [42]. Bifidobacteria can also promote the growth of B lymphocytes and regulate immune function [43]. Organismal immunoglobulins are mainly composed of IgG, IgA, and IgM and are synthesized and secreted by B lymphocytes [44]. It can be speculated that SBE may promote the growth of B lymphocytes by increasing the number of intestinal bifidobacteria, thereby increasing the content of IgG, IgA, and IgM in serum immunoglobulins. In this study, the serum IgG, IgA, and IgM contents of ducks were significant at 0.5 g/kg SBE addition, but with the increase of SBE addition, the IgG, IgA, and IgM contents decreased instead, probably because the high SBE addition changed the intestinal microbial structure of ducks in a direction unfavorable to the improvement of immunity, and even decreased immunity. In addition, methionine and cysteine have the effect of enhancing immune function [45], and the addition of SBE in this study increased the content of methionine and cysteine in eggs, which in turn improved the immunity of laying ducks.
Eggshell strength, egg white height, and haugh units are important indicators for evaluating egg quality. Calcium has a great influence on eggshell indicators, and increasing calcium absorption can improve eggshell strength and eggshell thickness [46]. Estrogen has been shown to promote calcium absorption [28], the intestine and kidney contain a large number of estrogen receptors [47]. When flavonoids bind to estrogen receptors on the small intestine and kidney, they promote calcium absorption in the small intestine and calcium reabsorption in the kidney. Flavonoids can improve eggshell strength by modulating estrogen and thus calcium metabolism [48]. In the current study, supplementation with SBF to the ration could improve the eggshell strength of duck eggs, excess flavonoids inhibited estrogen synthesis [29], were responsible for the decrease in eggshell strength. A previous study showed that antioxidant properties are critical to maintaining the antioxidant protection of the oviduct during eggshell formation [49], so improving the antioxidant capacity is also an important reason for the improvement of the eggshell strength. Therefore, the amount of SBE added to the ration should not be too high in production. In addition SBF can also increase the synthesis and secretion of egg mucin by regulating estrogen [50], and β-ovalmucin in eggmucin combines with O-glycoside carbohydrates to form a gel structure that makes egg white sticky and directly increases egg white height [51]. Egg white height was positively correlated with haugh unit, and increased egg white height in this study led to increased haugh units.
In addition to the apparent egg quality, the content and type of amino acids and fatty acids in duck eggs are also important indicators of egg quality. It has been shown that genistein flavonoids activate MAPK signaling pathway (activating Ser/Thr-protein kinase) and up-regulate insulin signaling pathway and glycolysis process in chicken liver to promote the conversion of glucose to amino acids [52], thus it was hypothesized that the addition of SBF to the diet could increase the TAA content in eggs. The higher the UAA content in the egg, the better the egg taste. When the expression of umami substance regulatory genes was high, the content of umami substance in meat also increased [53]. In addition, antioxidants also reduced the loss of UAA, and it was speculated that SBE could regulate the expression of umami substance genes, which needed further verification. In the present study, the total UAA content in the eggs increased, especially the content of Asp, Glu, and Tyr increased significantly. The highest UAA content was found when 1.0 g/kg SBE was added, and the UAA content decreased when 1.5 g/kg SBE was added. Therefore, the addition of SBE could increase the content of EAA in duck eggs, and the moderate amount of SBE may increase the content of TAA, NEAA, and UAA in duck eggs, while the excessive amount of SBE may result in the decrease of content of TAA, NEAA, and UAA in duck eggs.
Most fatty acids in birds are synthesized and metabolized by the liver [54] and deposited in eggs via blood transport. In this study, the levels of SFA, PUFA, n-3 PUFA, and n-6 PUFA were reduced in eggs. On the one hand, peroxisome proliferator-activated receptor-alpha (PPARα) are important transcription factors for hepatic fatty acid metabolism [55], SBF can up-regulate the expression of PPARα [56], promote fatty acid oxidation and inhibit fatty acid synthesis [57], thus reducing fatty acid content [58]. Additionally, flavonoids inhibit fatty acid synthase (FAS) activity to reduce fatty acid synthesis [59], thus suggesting that SBE reduces fatty acid content in eggs. Furthermore, fatty acids are the main component of TG [60], and in this study, SBF reduced serum TG levels, which resulted in lower fatty acid levels in eggs. In the present study SBE reduced the content of ALA and DPA in eggs, but interestingly inclusion of 1.0 and 1.5 g/kg SBE increased the content of DHA in eggs, which was 2.4 times higher than the control group. It has been shown that the human body can convert ALA to DHA by prolonging enzymes and desaturases [3], and DPA in laying hens can be efficiently converted to DHA [61]. It is speculated that SBE may promote the conversion of ALA and DPA to DHA in laying ducks.
## 5. Conclusion
The addition of 1.0 g/kg SBE to the diet may increase the egg weight of laying ducks, improve the utilization of feed nutrients. Appropriate addition of SBE to the diet can improve the quality of eggs and the content of amino acids in eggs. SBE could also reduce blood lipids and yolk cholesterol, improve the fatty acid profile of eggs, and increase the antioxidant capacity and immunity of laying ducks. Therefore, these results suggest that SBE can be used as an effective feed additive in laying duck production.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by Guizhou University Sub-committee of Experimental Animal Ethics (Guiyang, China; No. EAE-GZU-2021-E012).
## Author contributions
B-nY: data collation and draft writing. S-lY: revise the first draft and supervise the completion of the test. F-yL, J-yY, AL, B-gZ, and GF: assist with feeding trials and writing. All authors have read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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---
title: Genetic liability to mental disorders in relation to the risk of hypertension
authors:
- Ning Huangfu
- Yunlong Lu
- Hongchuang Ma
- Ziwei Hu
- Hanbin Cui
- Fangkun Yang
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10008891
doi: 10.3389/fcvm.2023.1087251
license: CC BY 4.0
---
# Genetic liability to mental disorders in relation to the risk of hypertension
## Abstract
### Background
Observational studies have indicated that psychosocial factors contribute to hypertension; however, the causality of these associations remains unclear due to reverse causality and confounders. We aim to assess the causal associations of mental health disorders with hypertension.
### Methods
Instrumental variables of anxiety disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, depression, obsessive–compulsive disorder, post-traumatic stress disorder, schizophrenia, and subjective well-being measure were obtained from the corresponding largest genome-wide association studies. Summary statistics for the association of essential hypertension were obtained from the FinnGen Study (42,857 cases and 162,837 controls) and UK Biobank cohort (54,358 cases and 408,652 controls). The multiplicative random-effects inverse-variance weighted method was utilized as the primary analysis and three other statistical methods were conducted in the supplementary analyses. The results were combined using the fixed-effects method.
### Results
In the pooled analyses, genetic liability to depression was associated with higher risk of hypertension (odds ratio [OR], 1.25; $95\%$ confidence interval [CI], 1.17–1.35; $p \leq 0.001$). Besides, a suggestive association was found between genetically predicted higher weighted neuroticism sum-score and increased risk of hypertension (OR, 1.16; $95\%$ CI, 1.02–1.33; $p \leq 0.05$). No associations were found for other mental health disorders. Sensitivity analyses revealed consistent evidence as the main results.
### Conclusion
We provide consistent evidence for the causal effect of genetic liability to depression on hypertension, which highlights the importance of blood pressure measurement and monitoring in patients with depression.
## Introduction
Approximately, 1.2 billion people worldwide suffer from hypertension [1], which is an important risk factor for stroke, ischemic heart disease, and kidney disease. Although many observational studies have indicated that psychosocial factors may contribute to hypertension (2–4), these findings may be subjected to incomplete adjustment for confounding factors and divergent definitions of mental health, which therefore hinders the causal inference in these associations. Furthermore, as patients with hypertension often have mental health morbidities [5], it appeared to be difficult to diminish reversal causality in observational studies.
Mendelian randomization (MR) analysis sets the basis on Mendel’s second law of inheritance, which can strengthen the casual inference in an association between an exposure and an outcome by using genetic variants as instrumental variables [6]. Resembling randomized controlled trials, MR randomizes participants into groups since genetic variants are randomly assigned to offspring and perpetually maintains stable and MR analysis can thus reduce confounders and reverse causation bias.
In recent years, a variety of genome-wide association studies (GWAS) have been published for identification of genetic risk loci for psychosocial factors (7–15), which provides a promising basis to evaluate the contribution of mental health to hypertension from a genetic perspective. Here, we resorted to the two-sample MR design to investigate the causal associations between genetic liability to mental health disorders, including anxiety disorder, attention deficit/hyperactivity disorder, autism spectrum disorder, depression, obsessive–compulsive disorder, post-traumatic stress disorder, schizophrenia, and subjective well-being measure with hypertension risk.
## Study design
The current study was a two-sample MR study to assess the causal associations between mental health and the risk of hypertension using genetic data obtained from the publicly available datasets (Figure 1). Instrumental variables for the mental health disorders should satisfy the following three key assumptions: (I) Relevance assumption, i.e., the genetic variants should be strongly associated with mental health, (II) Independence assumption, i.e., the genetic variants should be independent of potential confounders, and (III) Exclusion restriction, i.e., the genetic variants should only be associated with the risk of hypertension only through the change of mental health. Each study included in this analysis was approved by the corresponding ethics committee.
**Figure 1:** *Design of the current two-sample Mendelian randomization study. Three core assumptions were as follows: (α) Relevance assumption; (β) Independence assumption; (γ) Exclusion restriction. IVs, instrumental variables.*
## Genetic instrument selection
For the mental health disorders (regarded as exposures in the current study), single nucleotide polymorphisms (SNPs) associated at the genome-wide significance threshold of $p \leq 5$ × 10−8 were selected. The SNPs were pruned for linkage disequilibrium tests at r2 < 0.1, and the SNP with the lowest p value were retained as the instrumental variable. The estimates were queried in the outcome (hypertension) GWAS by matching for SNPs of exposure-related instrumental variables. If no corresponding match was found, a proxy at linkage disequilibrium (r2 > 0.8) was used to approximate it through an online tool named SNiPa (available at http://snipa.helmholtz-muenchen.de/snipa3/).
Following the approach described above, we identified $\frac{5}{5}$ SNPs (FinnGen/UK Biobank) for anxiety disorder [7], $\frac{9}{8}$ SNPs for attention deficit/hyperactivity disorder (ADHD) [8], $\frac{2}{3}$ SNPs for autism spectrum disorder [9], $\frac{100}{101}$ SNPs for depression [10], $\frac{81}{84}$ SNPs for neuroticism [11], no SNP for obsessive–compulsive disorder [12], $\frac{2}{2}$ SNPs for post-traumatic stress disorder (PTSD) [13], $\frac{109}{108}$ SNPs for schizophrenia [14], and $\frac{3}{3}$ SNPs for subjective well-being [15] (Figure 2). Characteristics of the genetic instruments for MR analyses were shown in Table 1. The use of pleiotropic instruments might affect the reliability of the results. Therefore, we compared the instrumental variables among the mental health disorders, and no overlap was observed. A more detailed description of these mental health disorders can be found in corresponding previous publications. The strength and bias of the mental health-related instrumental variables was evaluated by using the F statistics [16]. For mental health disorders including in the study, range of the F statistics of genetic instruments was provided in Table 1, all above the recommended threshold of F > 10 in the MR analysis [16].
**Figure 2:** *Mendelian randomization associations of genetically determined mental health with hypertension using different statistical models. ADHA indicates attention deficit/hyperactivity disorder; IVW, inverse-variance weighted; MR-PRESSO, Mendelian randomization-pleiotropy residual sum and outlier; NA, not available; PTSD, post-traumatic stress disorder; SNP, single nucleotide polymorphism; and WM, weighted median.* TABLE_PLACEHOLDER:Table 1
## Data sources
Summary statistics for the associations of hypertension were obtained from the FinnGen Study (fifth release) and UK Biobank. The FinnGen Study builds on samples collected by a nationwide network of Finnish biobanks, and matches the genome data with digital health care data from national health registries [17]. No overlap was observed of any exposure GWAS with the FinnGen Study (Table 1). There were 42,857 patients with essential (primary) hypertension and 162,837 controls in the FinnGen Study. Essential hypertension was defined according to the following International Classification of Disease (ICD) codes: ICD-8 codes 401–404, ICD-9 codes 4,019X, 4039A, and ICD-10 code I10.
UK *Biobank is* a prospective cohort study which consists of more than 500,000 men and women from the UK general population aged 40 to 69 [18]. There was substantial overlap between several exposure GWAS and UK Biobank (Table 1). Hypertension was defined based on discharge registries using the secondary ICD-10 code I10: essential (primary) hypertension, including 54,358 cases with essential hypertension and 408,652 controls. The data was obtained from the MR-Base platform (UKB-b:12493) [19]. In addition, we used summary statistics of self-reported hypertension (199,731 cases; 343,202 controls) from UK Biobank (UKB-b:14057) as supplementary analyses. All the published GWASs had already received ethical approval from relevant institutional review boards. In the current study, we used summary-level genetic data which were publicly available. This information did not include personal, identifiable information. Thus, no additional ethics approval was required.
## Statistical analysis
For each cohort, the random-effects inverse-variance weighted (IVW) method was used to assess the associations of mental health disorders with hypertension [20]. A fixed-effects meta-analysis was performed to combine the estimate from FinnGen and UK Biobank. Heterogeneity between the two cohorts was evaluated by the Cochran Q-derived p and I2 statistic ($p \leq 0.1$ or I2 > $50\%$ as significant heterogeneity) [21].
To further validate the robustness of results, we also performed weighted median [22], MR-Egger regression [23], and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) method [24], as sensitivity analyses. The weighted median method provided consistent estimates as long as more than half instrumental variables were effective [22]. MR-Egger regression explored the potential pleiotropy based on the hypotheses of independent association between genetic variants and their pleiotropic effects [23], and MR-Egger intercept test was conducted to detect the presence of directional pleiotropy [23]. MR-PRESSO method was performed to detect and remove outliers, thus, correcting for horizontal pleiotropy [24].
A two-sided p value <0.05 was set as suggestive for significance, and we further adjusted the threshold by Bonferroni correction for number of mental health exposures ($p \leq 0.05$/8 = 6.25 × 10−3). Statistical analyses were conducted in R software (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria). MR analyses and pooled analyses in the current study were performed using the TwoSampleMR,1 MR-PRESSO,2 and metafor3 R packages.
## Results
The main results of MR associations of genetically predicted mental health with the risk of hypertension are shown in Figure 3. After correction for multiple testing, genetic liability to depression was significantly associated with the risk of hypertension. In the pooled analyses, genetic liability to depression was also associated with higher risk of hypertension (odds ratio [OR], 1.25; $95\%$ confidence interval [CI], 1.17–1.35; $p \leq 0.001$) (Figure 3; Supplementary Figure S1). The MR analyses also showed a suggestive association that higher weighted neuroticism sum-score (1 SD increase) was associated with higher risk of hypertension (OR, 1.16; $95\%$ CI, 1.02–1.33; $p \leq 0.05$), even though not reaching the Bonferroni-corrected threshold of $p \leq 6.25$ × 10−3. No significant relationships were found with genetic liability to anxiety disorder, ADHD, autism spectrum disorder, PTSD, schizophrenia, and higher subjective well-being measure score (Figure 3). Significant heterogeneity between the FinnGen and UK Biobank was observed only in the meta-analysis on ADHD (I2 = $67.3\%$; $$p \leq 0.080$$) and neuroticism (I2 = $68.4\%$; $$p \leq 0.075$$).
**Figure 3:** *Mendelian randomization associations of mental health with hypertension in different data sources. Results are derived from the fixed-effects inverse-variance weighted analysis. ADHA indicates attention deficit/hyperactivity disorder; CI, confidence interval; OR, odds ratio; and PTSD, post-traumatic stress disorder.*
The main results remained stable in the weighted median and MR-PRESSO analyses. The MR-Egger intercept analyses provided no evidence of heterogeneity for the associations between mental health and hypertension (all $p \leq 0.05$) (Supplementary Table S1). Consistent with principal findings, the sensitivity analyses of genetically predicted mental health with self-reported hypertension in UK Biobank also presented similar results (Supplementary Table S2). Especially, considering the sample overlap (~$45\%$, UK Biobank mainly) between the GWAS of depression and hypertension, we further conducted the supplementary analysis that including of 56 significant SNPs in 23andMe Replication and consistent results were obtained for the associations of genetically predicted depression with essential and self-reported hypertension (OR, 1.31; $95\%$ CI, 1.13–1.51; $p \leq 0.001$ and OR, 1.13; $95\%$ CI, 1.12–1.26; $p \leq 0.05$, respectively) (Supplementary Figure S2).
## Discussion
A comprehensive framework of MR methodologies was applied to investigate the associations of genetic liability to 8 mental health disorders with hypertension, based on genetic data from the largest published GWAS. We provided consistent evidence for the causal effect of depression on the increased risk of hypertension. The association pattern remained when it was repeated in the further supplementary analyses. Besides, a potential association of genetic liability to neuroticism with hypertension was found.
Meng et al. [ 2] enrolled 9 prospective studies of 22,367 normotensive participators to assess the association between depression and risk of hypertension, with a mean follow-up period of 9.6 years. Results showed that depression significantly increased risk of hypertension (OR, 1.42; $95\%$ CI, 1.09–1.86), which was attenuated when adjusting for multiple variables. Such association was also confirmed in subsequent published work [3, 4]. However, some limitations existed for the studies discussed above. First, these studies did not correct for common risk factors for hypertension fully. In addition, antidepressants were also reported to be associated with the increased risk of hypertension [4]. Second, limited by the follow-up time, an inadequate duration of follow-up might underestimate the actual incidence values.
A wealth of evidence from clinical and MR study has suggested the role of depression in higher body mass index [25, 26], smoking [27, 28], excessive drinking [29, 30], physical inactivity [31], type 2 diabetes [32, 33], and lipid metabolism disorder [34]. There are also studies assessing the impact of depression from a biologic mechanism perspective, finding that depression is associated with autonomic dysfunction [35], impaired endothelial function [36], platelet dysfunction [37], and elevated inflammation markers (C-reactive protein, interleukin 6, tumor necrosis factor-α, etc.) [ 38]. In brief, the etiologic model of depression on hypertension is quite complex and cannot be explained by a single mechanism.
Our results found a suggestive association between higher weighted neuroticism sum-score and the risk of hypertension, which was consistent with reports from some longitudinal observational studies [39]. However, such potential association was only observed in UK Biobank cohort. Besides, the population of neuroticism GWAS and UK Biobank was completely overlap, which may result in potential bias, and inflate the Type 1 error rate [40]. Given the reasons above, it was generally insufficient to draw firm conclusion.
No evidence was found in the current study for the causal association between anxiety and hypertension. However, a recent meta-analysis that included 14 prospective studies of 686,362 participants revealed a significant anxiety-hypertension association (OR, 1.40; $95\%$ CI, 1.23–1.59) [41]. The difference between the results of this study and ours might result from the potential bias and reverse causality in observational study. Another possible reason was the low proportion of variance explained in anxiety, which would weaken the statistical power. Taking the consistent results of the vast majority of prospective studies into account [41], its potential causal effect cannot be definitively rule out.
Disagreement remained in the clinical researches regarding the effects of schizophrenia [4, 42] and subjective well-being (43–45) on hypertension. For schizophrenia, although a recent meta-analysis demonstrated no relationship with hypertension, there was a substantial heterogeneity (I2 = $90.7\%$) cross studies [46]. Likewise, it was unclear whether subjective well-being was a protective factor of hypertension. While subjective well-being phenotype was broadly defined in the GWAS study [15], the variety of definitions among cohort studies also limited the comparison of our findings with results of these reports.
The null effects with other mental health fitted with the prospective studies, demonstrating that ADHD [47], autism spectrum disorder [3], and PTSD [3] were not associated with hypertension. It should be noted that the association of ADHD and hypertension was observed in clinical research but did not reach statistical significance after adjusting for body mass index [47]. We also acknowledge that the analyses of ADHD, autism spectrum disorder, PTSD and subjective well-being suffered from insufficient statistical power and hence might report false-negative associations. Further GWAS with larger sample were needed to identify more significant loci.
The major strength of the current study was the design of MR study, which strengthened the causal inference to estimate the non-biased causal effect compared with observational studies. Besides, most studies included in the current analysis were with large sample sizes, which may guarantee the reliability of results. In addition, combining sensitivity analyses based on multiple statistical models with further supplementary analyses from different datasets, we provided more solid and reliable genetic evidence for the causal association between mental health and hypertension. Meanwhile, several limitations should be acknowledged. First, there was substantial overlap between several exposure GWAS and hypertension in UK Biobank. However, the association pattern of depression with hypertension remained when using variants significant in 23andMe replication. In addition, robust instruments and large-scale consortia also decreased the bias and the chance of Type 1 error to a certain degree [40]. Second, the power for certain analysis, like the analysis for anxiety might be inadequate given a small number of used instrumental variables which explains a limited phenotypic variance. Besides, the strength of association was not very strong, especially for neuroticism. Third, the lack of individual-level genotyping data made it impossible to assess the association of mental health and hypertension across age groups and genders. Fourth, the population of GWAS used in this study was mainly of European descent, which reduced the population stratification bias, but at the same time limited the generalizability of the results to other populations.
## Conclusion
In summary, the current study provides consistent evidence for the causal effect of genetic liability to depression on hypertension, which shows the clinical significance regarding blood pressure measurement and monitoring in patients with depression.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
## Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
NH, YL, FY, and HC contributed to the conception or design of the work. FY, YL, and HM contributed to the acquisition, analysis, or interpretation of data for the work. NH, YL, and FY wrote the manuscript. HM, ZH, NH, and HC revised the manuscript and gave critical suggestions. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.
## Funding
This work was supported by grants from Key Laboratory of Precision Medicine for Atherosclerotic Diseases of Zhejiang Province, China (Grant No. 2022E10026), Major Project of Science and Technology Innovation 2025 in Ningbo, China (Grant No. 2021Z134), Public Science and Technology Projects of Ningbo (Grant No. 202002 N3175) and Key research and development project of Zhejiang Province, China (Grant No. 2021C03096).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1087251/full#supplementary-material
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|
---
title: Body size and brain volumetry in the rat following prolonged morphine administration
in infancy and adulthood
authors:
- Milo Taylor
- Anya Brooke Cheng
- Duncan Jack Hodkinson
- Onur Afacan
- David Zurakowski
- Dusica Bajic
journal: Frontiers in Pain Research
year: 2023
pmcid: PMC10008895
doi: 10.3389/fpain.2023.962783
license: CC BY 4.0
---
# Body size and brain volumetry in the rat following prolonged morphine administration in infancy and adulthood
## Abstract
### Background
Prolonged morphine treatment in infancy is associated with a high incidence of opioid tolerance and dependence, but our knowledge of the long-term consequences of this treatment is sparse. Using a rodent model, we examined the [1] short- and [2] long-term effects of prolonged morphine administration in infancy on body weight and brain volume, and [3] we evaluated if subsequent dosing in adulthood poses an increased brain vulnerability.
### Methods
Newborn rats received subcutaneous injections of either morphine or equal volume of saline twice daily for the first two weeks of life. In adulthood, animals received an additional two weeks of saline or morphine injections before undergoing structural brain MRI. After completion of treatment, structural T2-weigthed MRI images were acquired on a 7 T preclinical scanner (Bruker) using a RARE FSE sequence. Total and regional brain volumes were manually extracted from the MRI images using ITK-SNAP (v.3.6). Regions of interest included the brainstem, the cerebellum, as well as the forebrain and its components: the cerebral cortex, hippocampus, and deep gray matter (including basal ganglia, thalamus, hypothalamus, ventral tegmental area). Absolute (cm3) and normalized (as % total brain volume) values were compared using a one-way ANOVA with Tukey HSD post-hoc test.
### Results
Prolonged morphine administration in infancy was associated with lower body weight and globally smaller brain volumes, which was not different between the sexes. In adulthood, females had lower body weights than males, but no difference was observed in brain volumes between treatment groups. Our results are suggestive of no long-term effect of prolonged morphine treatment in infancy with respect to body weight and brain size in either sex. Interestingly, prolonged morphine administration in adulthood was associated with smaller brain volumes that differed by sex only in case of previous exposure to morphine in infancy. Specifically, we report significantly smaller total brain volume of female rats on account of decreased volumes of forebrain and cortex.
### Conclusions
Our study provides insight into the short- and long-term consequences of prolonged morphine administration in an infant rat model and suggests brain vulnerability to subsequent exposure in adulthood that might differ with sex.
## Introduction
There is a plethora of evidence linking untreated pain to both physical [1, 2] and psychological symptoms [3, 4] that are detrimental to neonates. Opioids such as morphine have been shown to both relieve acute pain in infants and reduce procedural complications [5, 6] and have become the “gold standard” for pain treatment in pediatric procedural and perioperative settings [7]. In select cases, critically ill neonates and children receive prolonged opioid treatment as part of sedation, which facilitates ventilation and reduces anxiety, agitation, and stress (7–9). Although treatment of acute pain is the standard of care, our understanding of the effects of prolonged opioid administration in the developing brain on possible long-term sequelae is limited. Results from the Neurological Outcomes and Preemptive Analgesia in Neonate (NEOPAIN) trial [10] strongly suggest long-lasting adverse effects on body weight and head circumference, as shown in children ages 5–7 who were born prematurely and required intubation in the first 72 h of life [11]. In addition, morphine-treated children had more social problems and exhibited increased response latency during a short-term memory test. A larger study by De Graaf et al. [ 12] that followed children up to 8–9 years of age did not observe morphine–associated adverse effects reported by the NEOPAIN trial (lower body weight and head circumference, increased social problems, and poorer executive functions). Since morphine administration rates were lower in the study by De Graaf et al. [ 12], it is possible that long-term sequelae related to morphine therapy are dose dependent. These results speak to a lack of consensus on the long-term effects of opioid dosing in infancy on behavioral and physiological markers.
Animal models provide a translational tool to explore the neurobiological effects of morphine treatment with greater control over experimental conditions. Typically, chronic morphine administration in human infants is associated with numerous comorbidities, including but not limited to prematurity and surgical interventions. In the absence of pain or comorbidities, rodents exposed to prolonged morphine administration for 7 days in infancy demonstrated increased nociceptive response to thermal and chemical stimuli in adulthood [13, 14]. Similarly, our previous report showed lower thermal pain thresholds in adult rats following early prolonged morphine administration in the first two weeks of life [15] with no differences in mechanical thresholds. These findings implicate alterations in pain processing resulting from prolonged morphine treatment in infancy. In adult rats previously treated with morphine in infancy, subsequent morphine exposure has been shown to increase the analgesic effect [16], which suggests a modified response to opioid medications following chronic exposure in infancy. Many studies demonstrate not only the chemical but structural consequences of chronic morphine use in infancy, including decreased hippocampal cell division [17] and increased neuronal and microglial apoptosis [18]. However, evidence of intact whole-brain in vivo structural changes that occur as a result of prolonged morphine administration in infancy are limited.
In this study using a rat model, we probe the morphological consequences of prolonged morphine administration in infancy on brain volumes throughout life. For rodent development, it is reported that the proliferation and migration of cells in rats begin at gestational day 9.5 and end at about postnatal day (PD)15 [19]. Both the maturation and function of pain pathways, as well as the mechanisms of prolonged opioid effects in a rat model are age dependent. Specifically, increased excitability of nociceptive circuits peaks at postnatal day PD6 and decreases to an adult-like level by PD21 [20, 21]. Furthermore, some of the mechanisms of opioid tolerance [22] and dependence [23, 24] partially correspond to those of adult rats at PD14 and are equivalent to an adult at the PD21. Therefore, we decided to expose developing rat pups to prolonged postnatal morphine administration during this early period of brain development (PD1–14) when mechanisms of pain perception and opioid treatment differ to those in adult rats. We hypothesized that infant rats treated with prolonged morphine would exhibit smaller total and regional brain volumes. We also hypothesized that these differences would resolve in adulthood and that subsequent morphine administration will not lead to brain size (mal)adaptation. Our objective was to evaluate the [1] short- and [2] long-term effects of prolonged morphine administration in infant rat on body weight and brain volume, and [3] to determine if prolonged morphine exposure in infancy leads to an increased vulnerability to morphine related effects in adulthood.
## Animal care and use
The Institutional Animal Care and Use Committee at Boston Children's Hospital approved the experimental protocols for the use of vertebrate animals in this study. Experiments were conducted according to the U.S. Department of Health and Human Services “Public Health Service Policy on Humane Care and Use of Laboratory Animals” (NIH Publication No. 15-8013, revised 2015) prepared by the National Institute of Health Office of Laboratory Animal Welfare. Our group has previously described the animal care protocol [15, 25, 26]. Briefly, pregnant dams were received on day 18 and handled daily. Cages were checked at 9AM and 5PM daily and pups found at either time were deemed 0 days of age. This study used eight litters, each of which had between 9 and 12 pups. Rat pups were randomly assigned to pharmacological groups in a split-litter (within-litter) design with balanced pharmacological treatment distribution per litter [27, 28]. Pups of both sexes were included in the study. Animals were housed with their litters and kept on a 12-hour light/dark cycle. Food and water were given ad libitum. One set of animals was analyzed in infancy, and a second set of animals was housed until adulthood. The latter group was weaned from dams at 3 weeks of age [postnatal day (PD)18] and sex-separated with 3–4 animals per cage.
## Pharmacological treatment
The method used in this study to model prolonged morphine administration and associated morphine dependence [29] and analgesic tolerance [22] was originally described by Barr's group. The increased analgesic tolerance was subsequently confirmed in our lab [25]. Morphine sulfate (10 mg/kg; Baxter Health Care Corporation, Deerfield, IL) or an equal volume of saline was administered subcutaneously in the upper or lower back. Our group used subcutaneous injections rather than intraperitoneal injections to minimize nociceptive experience from drug administration. We also extended the period of administration from 6½ (1 week) to 13½ days (2 weeks) [15]. The first injection occurred on PD1, and animals received twice-daily injections (at 9 AM and 5 PM) from PD1 to PD14 (14 days). In adulthood, rats within each treatment group were assigned to a secondary pharmacological treatment group and received subcutaneous injections of either morphine or saline from PD56 to PD70. Injections were performed with either a 10- or 100- μl syringe (Hamilton Company, Reno, NV).
## Morphine weaning
Opioid dependence alters many physiological mechanisms, and abrupt discontinuation of morphine dosing results in withdrawal symptoms [30, 31]. After the period of pharmacological treatment in infancy (PD 1–14), rats injected with morphine underwent morphine weaning for 10 days (PD15–24) to reduce potential withdrawal symptoms as described by Craig [15]. Pups received incrementally decreasing morphine dosages: 3 days of twice-daily 5 mg/kg, 3 days of twice-daily 2.5 mg/kg, 2 days of twice daily 1.25 mg/kg, and 2 days of once daily 1.25 mg/kg. Signs of withdrawal were monitored using a scoring rubric developed by Gellert and Holtzman [32]. No withdrawal symptoms were observed as a result of this protocol [Figure 1 in [15]].
**Figure 1:** *Pharmacological groups. Infant rat pups received treatment (Rx) with either morphine [morphine pups (MP); 10 mg/kg subcutaneously twice daily] or equivalent volume of saline [saline pups (SP)] starting on postnatal day (PD)1 for 2 weeks. One set of animals underwent brain MRI scan under light anesthesia on PD14 (A). Morphine-treated infant rats from another set underwent a 10-day weaning period from PD15-PD25 (gray area) and were allowed to growth to young adulthood when they received additional two weeks of treatment from PD56-PD75. The four adult groups were scanned after PD 57 (B) and included rats treated with saline in infancy and saline in adulthood (SSA); morphine in infancy and saline in adulthood (MSA); saline in infancy and morphine in adulthood (SMA); and morphine in infancy and adulthood (MMA).*
## Pharmacological groups
The infant rats were divided into two treatment groups: saline-treated pups (SP, $$n = 6$$ females, $$n = 5$$ males) and morphine-treated pups (MP, $$n = 8$$ females, $$n = 4$$ males). The adult rats were divided into four treatment groups. The first adult group received injections of saline in infancy and adulthood (SSA, $$n = 6$$ females, $$n = 5$$ males). The next received morphine injections in infancy and saline injections in adulthood (MSA, $$n = 7$$ females, $$n = 5$$ males). Third group was treated with saline in infancy and morphine in adulthood (SMA, $$n = 6$$ females, $$n = 5$$ males), while the last group received morphine injection both in infancy and adulthood (MMA, $$n = 8$$ females, $$n = 4$$ males). The schematic Figure 1 illustrates the treatment groups. The immediate impact of pharmacological treatment differences was evaluated by performing an end-point analysis of infant groups (SP vs. MP). Long-term effects of infant morphine administration were explored in adulthood by analyzing the SSA and MSA groups. Lastly, by comparing the SSA, SMA, and MMA groups, we explored the effect of prolonged morphine administration in adulthood on total and regional brain volumes in the context of previous morphine administration in infancy.
## Anesthesia management
Infant rats were each anesthetized and scanned once between PD14 and PD17. Adult rats were scanned between PD69 and PD73. As previously described in detail [33], rats were anesthetized to minimize stress during scanning and reduce motion-related imaging artifacts. Rats were anesthetized with $3\%$ Isoflurane/O2 at 1 L/min (Baxter Healthcare Corp., Deerfield, IL) prior to transportation to the MRI scanner. Once in the scanner, animals were placed on a warmed animal cradle (from a water bath heater at 50.4°C) in a prone position and reconnected to the anesthesia delivery system through a nose cone. The head was secured into a head restrainer with a built-in coil. A respiratory rate monitor was placed on the ventral chest and secured with paper tape. The whole system was advanced into the magnet. Once in the scanner, anesthesia was decreased and maintained at the lower level, (<$1\%$ Isoflurane/O2 at 1 L/min). Administration of O2 via a nasal cone at 1 L/min provided an estimated $24\%$ fraction of inspired oxygen (FiO2). During imaging sessions, the level of anesthesia was gauged based on the respiratory rate using the Small Animal Monitoring and Gating System (Model 1025-S-50; Instruments Inc., San Diego, CA). The level of Isoflurane was titrated to a respiratory rate between 45 and 50 breaths per minute. Following completion of the scan, Isoflurane was discontinued, and animals were placed on a warming pad to recover (Hot Dog Patient Warmer; Augustine Biomedical and Design, Eden Prairie, MN).
## Brain MRI
Animals were scanned with a Bruker BioSpec $\frac{70}{30}$USR 7 T MRI scanner (Bruker, Billerica, MA) at the Small Animal Imaging Laboratory at Boston Children's Hospital. For both infant and adult rats, we used a Bruker transmit-only volume coil with an inner diameter of 85 mm in combination with a 4-channel phased array receive-only surface rat brain coil (10–20 mm internal diameter; Bruker, Billerica, MA). We also used an anatomically shaped mouse brain array for infant rats, which in their third week are similarly sized to adult mice. T2-weighted structural images were acquired using a RARE (Rapid Acquisition with Relaxation Enhancement) FSE sequence [TE = 35 msec, TR = 4,000, FA = 90 degrees, RARE factor 8, FOV = 20 × 20 mm, matrix = 256 × 256, slice thickness = 0.5 mm, slice gap of 0.1 mm, 34 slices, voxel size = 0.078 × 0.078 mm]. For the additional rationale of infant rat model brain scanning at PD14 in relation to resting-state functional MRI, please refer to our previous publication [33].
## Preprocessing
Raw MRI data was exported in dicom format and was transformed into Nifti files using the software dcm2nii. All images had their pixel dimensions scaled up in the Nifti header by a factor of 10 to avoid scale-dependent issues when using the FMRIB Software Library (FSL; http://www.fmrib.ox.ac.uk/fsl). T2-weighted images were first oriented using the software Freeview (v. 6.0; http://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferWiki). All images were rotated in the axial and coronal planes to achieve a uniform, upright, horizontal alignment to aid in subsequent manual segmentation.
## Segmentation
A single investigator with neuroanatomical expertise blindly performed data analysis, which was subsequently checked by a senior researcher. Although automatic adult rat brain extraction software is available (34–37), issues of reliably are important in the segmentation of the infant rat brain [38]. This relates to the low image contrast as a result of poor myelination in the infant rat brain [39] which poses a challenge for automatic segmentation protocols [40]. To ensure consistency in analysis, both infant and adult rat images were manually segmented using the structural MRI tool ITK-SNAP (v.3.6.0; www.itksnap.org). Figure 2 illustrates major brain regions that were segmented, including the brainstem, cerebellum, and forebrain. The forebrain was further subdivided into the cortex, hippocampus, and deep gray matter. The deep gray matter comprises the basal ganglia and thalamus, structures known to be affected by chronic morphine administration [41, 42]. Figure 3 illustrates coronal sections of the infant rat brains that aided in consistent segmentation of the infant brain regions [see also MRI Atlas of the Infant Rat Brain [43]]. Anatomical delineations were done according to the The Rat Brain atlas by Paxinos and Watson [44]. Criteria for manual segmentation of rat brain tissue were as follows: the most caudal section includes cerebellar tissue while the most rostral section includes cortical tissue (Figure 3). Coronal slices containing only the spinal cord and olfactory bulb tissue (the most caudal and rostral, respectively) were excluded from the brain regional masks. Extra-axial cerebrospinal fluid and large vasculature exterior to the brain, as well as small areas of the ventricular system, were excluded from segmented regions. Lastly, the trigeminal ganglia were included as part of the brainstem segmentation according to the stereotaxic rat brain atlas by Schwarz's group [45].
**Figure 2:** *Brain regions of interest. Figure illustrates representative infant rat brain with selected brain region masks in 3D view (A) and in sagittal section (B). Brain regions analyzed included brainstem (blue), cerebellum (CB; green) and forebrain. The latter was further sub-divided into cortex (CTX; red), deep gray matter (DGM; yellow), and hippocampus (HIPP; teal). A, anterior; D, dorsal (posterior); P, posterior (caudal), V, ventral (inferior).* **Figure 3:** *Regional brain segmentation. Figure systematically outlines representative T2-weighted coronal sections with its 5 regional brain segmentations in an infant rat at 2 weeks of age. Coronal brain sections are organized from the most rostral (top left) to the most caudal (bottom right) ends. The most anterior section included the last section-containing cortex and excluded those that solely contained olfactory bulbs (Ob; not shown). Similarly, the most caudal section for the masking included the last tissue from the cerebellum (CB) and excluded more caudal sections solely with medulla (bottom coronal section without any masks). We used ITK-SNAP (v.3.6.0; www.itksnap.org) for regional brain segmentation that included cortex (CTX; red), deep gray matter (DGM; yellow), hippocampus (HIPP; teal), cerebellum (green), and brainstem (BS; blue). DGM included all deep structures of the forebrain (e.g., basal ganglia (BG), thalamus (TH), hypothalamus (HY), ventral tegmental area (VTA). D, dorsal (posterior); IC, inferior colliculus; P, posterior (caudal), SC, superior colliculus; V, ventral (inferior).*
## Body weight
We previously showed that repeated morphine treatment in infant rats is associated with smaller body weight in comparison to controls [Figure1 in [15]]. We also reported that by adulthood, both sexes overcame the weight difference, although male adult rats weighed more than female adult rats. We extend those findings in the current report to assess the impact of subsequent pharmacological treatment in adulthood.
## MRI data volumes
ITK-SNAP was used to obtain the absolute (cm3) regional brain volumes of both infant and adult rats. Total brain volume was calculated as the sum of all 5 regions analyzed. Volumes were not only reported as absolute volumes (cm3) but also as normalized values (% total brain volume) to correct for possible individual data variations [46]. Normalizing also allows for a better understanding of how a brain region might respond differently than the rest of the brain under specific pharmacological conditions.
## Statistical analysis
Due to the split-litter design, the analysis unit was based on the number of individuals per treatment group [28]. As in previous behavioral studies [15], we did not find any significant differences between male and female infant rats (6 female and 5 male saline-treated pups, 8 female and 4 male morphine-treated pups), so data for each infant pharmacological treatment group were collapsed for clarity. In contrast, due to volumetric differences found between male and female adult rats within pharmacological groups, data for the two sexes were analyzed separately. We used either a two-tailed independent Student's t-test or a one-way analysis of variance (ANOVA) with Tukey's Honestly Significant Difference (HSD) post-hoc tests to account for multiple treatment comparisons, to minimize type 1 error; Therefore, to adjust for 3 planned comparisons between treatment conditions, we used a conservative p-value <$\frac{0.05}{3}$ ($p \leq 0.017$) as the criteria for statistical significance. All statistical analyses were done with VassarStats (http://vassarstats.net/), a website for statistical computation.
## Weight differences with sex
Similarly to our previous work [15, 25, 26], there were no significant body weight differences in infant rats across the sexes in either saline-treated [t[9] = 0.2, $$p \leq 0.665$$] or morphine-treated [t[10] = 0.13, $$p \leq 0.726$$] groups. Based on the lack of differences in body weight, data analysis of male and female infant rats was combined for simplicity of data presentation (Figure 4A). This contrasts with weight analysis in adult rats where females always had lower body weight compared to males irrespective of pharmacological treatment (Figures 4B,C; Statistics not shown). Therefore, all analyses for adult rats were done separately for males and females.
**Figure 4:** *Average body weight at brain MRI scan. Graphs represent average body weight at brain MRI scan in grams (g) for infant (A) and adult rats (B,C). Infant rats pups were treated either with saline [saline pup (SP)] or morphine [morphine pup (MP)] twice daily for 2 weeks, which did not lead to weight differences in sex/group (SP (F vs. M), F(1,9) = 0.2; p = 0.66; MP (F vs. M), F(1,10) = 0.13; p = 0.73). Infant rats treated with morphine were smaller than those treated with saline [F(1,21) = 70.7, p < 0.001] without sex differences (A). Separate group of animals subsequently received additional 2 weeks of treatment in adulthood comprising total of 4 pharmacological groups (see also Figure 1): saline in infancy and saline in adulthood (SSA); morphine in infancy and saline in adulthood (MSA); saline in infancy and morphine in adulthood (SMA); and morphine in infancy and morphine in adulthood (MMA). Due to obvious sex differences in weight in adulthood, average weight of adult rats is separated by sex (B,C). Although trend in average body weight per pharmacological group was similar, group differences were found for male F(3,15) = 4.9, p = 0.015) but not female rats [F(3,23) = 1.1, p = 0.39]. ANOVA: *p < 0.05, **p < 0.01.*
## Weight differences with pharmacological treatment
Following two weeks of twice daily pharmacological treatment (Figure 1), morphine-treated infant rats had significantly smaller body mass (23.17 g ± 2.17) than saline-treated infant rats [33.82 g ± 5.37; t[21] = 70.74, $p \leq 0.001$]. In adult female rats, no weight differences were observed irrespective of infant or adult pharmacological treatment [F[3,23] = 1.06, $$p \leq 0.385$$, Figure 4B]. In contrast, we found significant differences in weight between pharmacological groups in adult male rats [F[3,15] = 4.88, $$p \leq 0.015$$, Figure 4C]. Specifically, male rats treated with morphine in infancy show lower body weight compared to saline control, regardless of treatment in adulthood ($p \leq 0.05$).
## Prolonged morphine effects on total and regional brain volumes in an infant rat
Since no sex-related brain volume differences were observed in infant rat groups, sex data was combined for clarity. As illustrated in Figure 5A, infant rats treated with morphine ($$n = 12$$) had consistently smaller total and regional brain volumes in comparison to controls ($$n = 11$$). Specifically, average total brain volume (cm3 ± SD) was significantly smaller in the morphine-treated group (1.23 ± 0.045) in comparison to the saline group [1.44 ± 0.079; t[21] = 41.7, $p \leq 0.001$]. Similar findings of smaller absolute volumes were observed across all regions analyzed: forebrain [t[21] = 36.92, $p \leq 0.001$], cortex [t[21] = 33.33, $p \leq 0.001$], deep gray matter [t[21] = 25.4, $p \leq 0.001$], hippocampus [t[21] = 0.005], cerebellum [t[21] = 25.53, $p \leq 0.001$], and brainstem [t[21] = 30.13, $p \leq 0.001$]. Collectively, these findings implicate globally lower total and regional brain volumes following prolonged morphine treatment in infancy. Indeed, this conclusion is supported by a lack of differences in normalized regional brain volumes (as % of total brain volume) between the two treatment groups. Specifically, we report no differences in normalized regional brain volumes between the two infant pharmacological groups (Figure 5B): forebrain [F[21] = 0.17, $$p \leq 0.68$$], cortex [F[1, 21] = 0, $$p \leq 1.00$$], deep gray matter [F[1, 21] = 0.01, $$p \leq 0.92$$], hippocampus [F[1, 21] = 0.55, $$p \leq 0.47$$], cerebellum [F[1, 21] = 4.56, $$p \leq 0.05$$], and brainstem [F[1, 21] = 1.81, $$p \leq 0.19$$].
**Figure 5:** *Immediate effects of prolonged morphine treatment on total and regional brain volumes in an infant Rat. Graphs show average absolute (cm3; A) and normalized brain volumes as a percent of total brain volume (%TBV; B) for the 2 groups of infant rats treated with saline [saline pup (SP); n = 11] or morphine [morphine pup (MP); n = 12] twice daily for 2 weeks. Absolute brain volumes of morphine-treated pups (MP) were smaller in comparison to saline-treated pups (SP) for total brain volume (TBV) and across all regions analyzed (A). No differences were observed in normalized brain volumes between groups (B). BS, brainstem; CB, cerebellum; CTX, cerebral cortex; DGM, deep gray matter; FB, forebrain; HIPP, hippocampus; ANOVA: *p < 0.05, **p < 0.01.*
## Long-term effects of prolonged morphine administration in infancy on total and regional brain volumes in adult rat
Due to previously established sex differences in weight of adult rats (Figure 4), we separated the brain volume analyses by sex. We compared two groups of adult animals who underwent either saline or morphine treatment in infancy followed by saline treatment in adulthood (SSA vs. MSA; Figure 1) to assess the long-term impact of morphine treatment in infancy. We report no differences in either absolute or normalized total and regional brain volumes in either female or male adult rat groups (Figure 6).
**Figure 6:** *Long-term effects of prolonged morphine treatment in infancy on adult Rat total and regional brain volumes. Average absolute (cm3) and normalized brain volumes as a percent of total brain volume (%TBV) for female (A and A′, respectively) and male (B and B′, respectively) adult rats for the 2 groups of animals that underwent 2-week periods of treatment in infancy and adulthood: saline in infancy and saline in adulthood (SSA; n = 6 female; n = 5 male) and morphine in infancy and saline in adulthood (MSA; n = 7 female; n = 5 male). There were no significant differences in either absolute or normalized total or regional brain volumes between pharmacological groups for either sex using t-test. BS, brainstem; CB, cerebellum; CTX, cerebral cortex; DGM, deep gray matter; FB, forebrain; HIPP, hippocampus.*
## Absolute volumes
There were no significant differences in total brain volumes of adult female rats treated with morphine in infancy (MSA $$n = 7$$; 1.92 cm3 ± 0.087) compared to those treated with saline [SSA $$n = 6$$; 1.95 cm3 ± 0.075; t[11] = 0.3, $$p \leq 0.59$$]. As further illustrated in Figure 6A, there were no differences in volumes of any of the brain regions analyzed: forebrain [t[11] = 0.07, $$p \leq 0.80$$], cortex [t[11] = 0.03, $$p \leq 0.87$$], deep gray matter [t[11] = 0.26, $$p \leq 0.62$$], hippocampus [t[11] = 0.68, $$p \leq 0.43$$], cerebellum [t[11] = 2.96, $$p \leq 0.11$$], and brainstem, [t[11] = 0.05, $$p \leq 0.83$$]. Similarly, male rats showed no difference in total brain volume between morphine- (MSA $$n = 5$$; 2.02 cm3 ± 0.097) and saline- (SSA $$n = 5$$; 2.12 cm3 ± 0.097) treated groups in infancy [t[8] = 2.66, $$p \leq 0.14$$]. Figure 6B further shows a lack of regional volume difference between SSA vs. MSA male groups: forebrain [t[8] = 1.68, $$p \leq 0.23$$], cortex [t[8] = 3.14, $$p \leq 0.11$$], deep gray matter [t[8] = 0.43, $$p \leq 0.53$$], hippocampus [t[8] = 0.13, $$p \leq 0.73$$], cerebellum [t[8] = 4.84, $$p \leq 0.059$$], and brainstem [t[8] = 2.0, $$p \leq 0.20$$].
We report smaller total brain volumes in adult female rats twice treated with morphine [F[2, 17] = 9.06, $$p \leq 0.002$$] but not males [F[2, 11] = 5.49, $$p \leq 0.022$$] compared to the adult saline-treated groups, irrespective of the pharmacological treatment in infancy. Furthermore, prolonged morphine administration in adulthood was associated with smaller total brain volume of female rats due to decreased absolute volumes of the forebrain [F[2, 17] = 8.73, $$p \leq 0.003$$] and cortex [F[2, 17] = 6.83, $$p \leq 0.007$$]—but only in case of previous exposure to morphine in infancy (Figure 7A). Other regions showed no difference in absolute volumes between treatment groups: deep gray matter [F[2, 17] = 5.4, $$p \leq 0.015$$, group comparisons were not significant], hippocampus [F[2, 17] = 2.86, $$p \leq 0.085$$], cerebellum [F[2, 17] = 3.37, $$p \leq 0.059$$], and brainstem [F[2, 17] = 3.58, $$p \leq 0.05$$]. Although male rats showed consistently lower regional brain volumes following morphine treatment in adulthood, we failed to detect any group differences (Figure 7B): forebrain [F[2, 11] = 2.08, $$p \leq 0.17$$], cortex [F[2, 11] = 1.37, $$p \leq 0.29$$], deep gray matter [F[2, 11] = 3.13, $$p \leq 0.08$$], cerebellum [F[2, 11] = 5.49, $$p \leq 0.022$$], hippocampus [F[2, 11] = 1.42, $$p \leq 0.28$$], and brainstem [F[2, 11] = 1.58, $$p \leq 0.25$$]. Data for cerebellar volumes was not considered significant due to conservative p-value to account for multiple comparisons (significance $p \leq 0.017$).
## Normalized volumes
Morphine treatment in infancy had no impact on adult measures of normalized regional brain volumes for either sex. Female rats treated with morphine in infancy and saline in adulthood (MSA) did not have significantly different normalized regional brain volumes compared to those twice treated with saline (SSA), as shown in Figure 6A′: forebrain [t[11] = 0.79, $$p \leq 0.39$$], cortex [t[11] = 1.4, $$p \leq 0.26$$], deep gray matter [t[11] = 0.06, $$p \leq 0.81$$], hippocampus [t[11] = 0.39, $$p \leq 0.55$$], cerebellum [t[11] = 1.9, $$p \leq 0.20$$], or brainstem [t[11] = 0.04, $$p \leq 0.85$$]. Similarly, there was no significant differences in the normalized brain volumes measures for any of the regions analyzed in the male rat group either (Figure 6B′): forebrain [t[8] = 0.14, $$p \leq 0.72$$], cortex [t[8] = 1.14, $$p \leq 0.32$$], deep gray matter [t[8] = 0.61, $$p \leq 0.46$$], hippocampus [t[8] = 5.43, $$p \leq 0.05$$], cerebellum [t[8] = 0.39, $$p \leq 0.55$$], or brainstem [t[8] = 0.02, $$p \leq 0.89$$].
Based on described region-specific brain volume differences, we would expect group differences in normalized volumes correlated with the significant results outlined above. However, we observed no significant normalized volume differences for any of the regions for female rats between treatment groups: forebrain [F[2, 17] = 0.57, $$p \leq 0.58$$], cortex [F[2, 17] = 0.47, $$p \leq 0.63$$], deep gray matter [F[2, 17] = 0.09, $$p \leq 0.91$$], hippocampus [F[2, 17] = 0.31, $$p \leq 0.74$$], cerebellum [F[2, 17] = 0.81, $$p \leq 0.46$$], and brainstem [F[2, 17] = 0.28, $$p \leq 0.76$$] (Figure 7A′). Similarly, adult male rats displayed no differences in normalized volumes between treatment groups for any region: forebrain [F[2, 11] = 0.85, $$p \leq 0.453$$], cortex [F[2, 11] = 0.03, $$p \leq 0.971$$], deep gray matter [F[2, 11] = 0.72, $$p \leq 0.508$$], hippocampus [F[2, 11] = 1.58, $$p \leq 0.249$$], cerebellum [F[2, 11] = 3.02, $$p \leq 0.09$$], and brainstem [F[2, 11] = 0.84, $$p \leq 0.458$$] (Figure 7B′).
## Effect of prolonged morphine administration in adulthood on total and regional brain volumes in the setting of previous morphine administration in infancy
The impact of morphine treatment in adulthood following morphine exposure in infancy was explored through the comparison of three groups: [1] morphine-treated adults who had undergone morphine treatment in infancy, [2] morphine-treated adults who received saline treatment in infancy, and [3] twice saline-treated control group (MMA, SMA, and SSA, respectively; Figure 1). Prolonged morphine administration in adulthood was associated with smaller total and regional brain volumes that vary by sex– but only in case of previous exposure to morphine in infancy (Figure 7).
**Figure 7:** *Total and regional brain volumes following prolonged morphine administration in adult rats ± previous exposure to morphine in infancy. Average absolute (cm3) and normalized brain volumes as a percent of total brain volume (%TBV) for female (A and A′, respectively) and male (B and B′, respectively) adult rats for the 3 groups of animals that underwent 2-week periods of treatment in infancy and adulthood: saline in infancy and saline in adulthood (SSA; n = 6 female; n = 5 male), saline in infancy and morphine in adulthood (SMA; n = 6 female; n = 5 male), and morphine in infancy and morphine in adulthood (MMA; n = 8 female; n = 4 male). There were no significant differences in average absolute total or regional brain volumes for either of sex following prolonged morphine administration only in adulthood (SSA vs. SMA). However, prolonged morphine administration in adulthood was associated with smaller total brain volume of female rats due to decreased absolute volumes of the forebrain and cortex. Cerebellum volume differences in males [F(2, 11) = 5.49, p = 0.022] are not considered significant due to conservative p value to account for multiple comparisons (significance p < 0.017). Abbreviations: BS, brainstem; CB, cerebellum; CTX, cerebral cortex; DGM, deep gray matter; FB, forebrain; HIPP, hippocampus; TBV, total brain volume. ANOVA: *p < 0.05, **p < 0.01.*
## Discussion
We report smaller body weight and globally lower absolute brain volumes in infant rats following prolonged morphine treatment. These differences in both body weight and brain volume resolved in adulthood. However, upon initial exposure or re-exposure to morphine in adulthood, a decrease in the volume of select brain regions was observed that differed by sex. Lack of normalized volume differences in adult female rats could, in part be explained by small data differences between groups and relatively gross regional brain analysis.
## Study limitations
This study should be interpreted with a few limitations in mind. The morphine-treated infant rat group included twice as many female pups ($$n = 8$$) as males ($$n = 4$$), which similarly translated to twice as many twice morphine-treated adult female rats ($$n = 8$$) as males ($$n = 4$$). Future studies with greater power are needed to confirm our results. Furthermore, our method of manual tissue segmentation as well as the more challenging anatomical borders in infant rats prevented the delineation of smaller, specific structures of interest previously shown to exhibit structural changes in response to morphine administration in infancy, such as the amygdala [26]. With higher resolution imaging, and the advent of more powerful automated segmentation techniques, future studies of structural differences are needed to evaluate vulnerability of specific brain areas beyond gross brain regional divisions.
## Body mass with age, sex, and pharmacological treatment
As previously reported by our group [15, 26], we observed slower body weight gain during prolonged morphine administration in infancy. The lower body weight of infant rats treated with morphine could, in-part be attributed to the fact that morphine-treated animals were asleep longer following injections. Due to the split-litter design, saline-treated pups were therefore conferred a nursing advantage over their morphine-treated siblings within the same litter. Such restrictions in diet and feeding schedules have previously been shown to induce rapid weight loss [47]. Interestingly, recent study by O'Meara et al. [ 48] reported that morphine treated juvenile rats treated twice daily for 7 days (PD18–24) gained less weight than those treated with repeated benzodiazepine (viz. midazolam) or saline control, suggesting opioid behavioral effects on appetite and/or resource acquisition. Future studies could employ a different method of assigning pharmacological groups to determine the degree to which nursing advantage affects body weight. In addition, future studies should consider comparison of pharmacological effect of morphine to other sedating agents (e.g., midazolam, dexmedetomidine) in this early neurodevelopmental period (PD1–14) to better assess disrupted rodent diurnal patterns and reduced nighttime eating with associated weight loss—as was previously described in rats [49] and other species [50, 51].
Furthermore, we previously reported no difference in percent body mass gain during the morphine-weaning period between morphine- and saline-treated rats [see Figure 1B in [15]]. These findings indicate a recovery of body mass growth following the cessation of morphine dosing that may translate to the compensatory growth we observe before adulthood. Our novel results show weight differences in adulthood only for male rats following morphine exposure irrespective of the time-period of administration (Figure 4). Presented sex differences of prolonged morphine administration on body weight warrant future studies.
## Immediate effect of prolonged morphine administration in infancy
Our data showed globally smaller brain volumes in infant rats that received prolonged morphine administration compared to saline-treated pups. These findings are in line with previous studies that (i) implicate decreased somatic development in morphine-treated rat pups as measured by body weight and brain weight and size [52, 53], as well as (ii) reported region-specific apoptosis in infant rats treated with morphine [26]. We expanded on these studies by analyzing gross brain regions by MRI, and we report a uniform rather than localized reduction in brain volume in morphine-treated rat pups. Though all brain regions had lower absolute volumes in morphine-treated pups, only the normalized cerebellum volumes were close to significantly smaller ($$p \leq 0.05$$), indicating that the developing cerebellum may have an increased vulnerability to morphine. One possible explanation comes from a study by Zwicker, who found smaller cerebellum volumes in morphine-treated preterm infants [54]. The authors concluded that morphine's effect on the cerebellum is likely mediated by the death of Purkinje cells, as morphine localizes to the nerve terminals surrounding these cells [55] and that this effect may be limited in rodents to the first few days of life [56]. Certainly, future studies should explore changes in opioid receptors throughout the brain [57, 58] following prolonged morphine exposure as described in used infant rat model. Such inquiry would implicate pre/post-synaptic (mal)adaptations driven by opioid receptors that could potentially be linked to increased cortical apoptosis demonstrated previously [26].
## Long-term effects of prolonged morphine administration in infancy
Adult rats previously treated with morphine for a prolonged time in infancy did not show differences in total or regional brain volume implicating neuroplasticity following prolonged morphine exposure during early brain development. Using the same rodent model, our group previously reported subtle long-term behavioral effects in adult rats at PD55–56 following prolonged morphine administration in infancy [15]. Specifically, we reported selective long-term neurobehavioral differences in thermal but not mechanical sensory processing. Importantly, our results suggested a lack of long-term alterations on drug reward/reinforcement behavior [via locomotor testing [59, 60]], affective processing [using swim test [61]], and short-term memory [via novel-object recognition evaluation [62]]. Similarly, reduced nociceptive thresholds to thermal and mechanical testing [13], or formalin injections [14] at PD60 have been reported following daily administration of morphine (5 mg/kg from PD7–14). Future studies are needed to address the potential neuroplastic alterations at the molecular, cellular, and/or brain networks level using resting-state functional MRI to explore possible, subtle differences of thalamo-cortical pathways responsible for long-term morphine effects on sensory processing.
## Prolonged morphine administration in adulthood in the setting of previous exposure in infancy
This study reveals decreased total and regional brain volumes in rats treated with morphine in adulthood, regardless of morphine treatment in infancy, that differ based on sex. It has been previously established that morphine treatment in infancy can have lasting behavioral effects that differ by sex, specifically with increased analgesia in adult females and decreased analgesia in males [63]. This may implicate a higher degree of potency in adult female rats than males, which may be hormonal as suggested by gonadectomy studies [64, 65]. Our morphological data provides further evidence to the sex based morphological differences in adult rats following prolonged morphine administration. Future studies are needed to address underlying cellular integrity (e.g., neuronal count, apoptosis) and possibly network level analysis using functional brain MRI. Previous studies have also described decreased morphine potency in adulthood, resulting from a reduced density of mu-opioid receptors after PD21 [66]. Previous literature also reported reduced effectiveness of high doses of morphine in adult rats [67, 68], which supports our findings that morphine-treated pups had globally smaller brain volumes while morphine-treated adults only had regionally smaller brain volumes. Longitudinal studies performed at the cellular level are needed to determine if the mechanisms of morphine-induced structural changes in infancy may be impacted on subsequent exposure in adulthood.
## Translational aspects: animal-human correlation
The association of rat and human developmental stages has been previously described and extensively discussed in the literature. In fact, the infant rat model at PD7 and PD14 has been extensively used in relation to early (premature, neonatal, and infant) and childhood development in humans, respectively [69, 70]. Such difficult species developmental associations depend on several endpoints, such as number of brain cells, degree of myelination, brain growth rate, synaptogenesis, and measures related to more contemporary neuroinformatics [71, 72]. In rodents, this critical period of neuronal differentiation and synaptic development is limited to a time window up to a 4th postnatal week [PDs 1–28; (73–75)]. In humans, the described brain growth spurt characterized with synaptogenesis and accompanied by dendritic and axonal growth, as well as myelination of the subcortical white matter, extends from the last trimester of pregnancy up to the first few years of postnatal life [76].
## Brain size
In humans, low birth weight has been correlated with morphological changes throughout life including lower total brain volume and smaller cortical surface area, even in children born full term [77]. These findings indicate a correlation between weight and brain volume that may help explain our findings of smaller absolute brain volumes in morphine-treated rat pups. However, adolescents with low birth weights who later attained a normal body weight showed no significant difference in brain volume measures compared to control participants [78, 79], which aligns with our lack of observed differences in adult rat brain volumes. Other studies have found significant differences in regional brain volumes of school-aged children independent of birth weight following prenatal opioid exposure [80], which suggests a weight-independent mechanism of lower brain volumes because of opioid treatment. Our results build on these findings in a postnatal model, suggesting that morphine administration in infancy does not result in permanently smaller brain regions, but that such treatment may impose a vulnerability to reduced regional brain volumes when treated again with morphine in adulthood.
## Long-term effects of opioid exposure in infancy
While many studies probe the effects of prenatal opioid exposure [81, 82], those that focus on postnatal opioid exposure are scarce [81, 82]. Studies of school-aged children who were prenatally exposed to opioids reported lower volumes of specific brain regions, including the basal ganglia, thalamus, and cerebellum [80, 83, 84]. Furthermore, compared executive function and attention have been reported in human children exposed to morphine prenatally (85–87), but confounding factors make it difficult to isolate morphine treatment as a risk factor. Indeed, postnatal opioid exposure that is prolonged and associated with developmental of tolerance and iatrogenic dependence to drugs of sedation (viz. opioids and benzodiazepines) occurs primarily in the perioperative settings [88] that implicates more than pharmacological impact on the brain in the context of other confounders (e.g., gestational age at birth, underlying disease severity, pain in the context of surgery, cumulative anesthesia exposure, etc.).
## Conclusion
Considering opioids are still largely considered the “gold standard” of pain management for infants and children, it is important to elucidate the immediate and long-term neurodevelopmental impact of prolonged administration of opioids in developing brain. Our study shows for the first-time short- and long-term pharmacological effects of prolonged morphine administration in an infant rat model on the effects on the total and regional brain volumes in adulthood and its vulnerability to subsequent morphine administration.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by The Institutional Animal Care and Use Committee at Boston Children's Hospital.
## Author contributions
Authorship credit was based on substantial contribution to [1] the conception and manuscript design (MT, DJH, DB); [2] acquisition (DB), analysis (MT, ABC, DJH, DZ, DB) and interpretation of data (all authors); [3] drafting the article (MT, DB) or critical revision for important intellectual content (all authors) and proofs (MT and DB); [4] final approval of the version to be published (all authors); and [5] are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved (all authors). All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Microbiota-induced regulatory T cells associate with FUT2-dependent susceptibility
to rotavirus gastroenteritis
authors:
- Emmanuelle Godefroy
- Laure Barbé
- Béatrice Le Moullac-Vaidye
- Jézabel Rocher
- Adrien Breiman
- Sébastien Leuillet
- Denis Mariat
- Jean-Marc Chatel
- Nathalie Ruvoën-Clouet
- Thomas Carton
- Francine Jotereau
- Jacques Le Pendu
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10008897
doi: 10.3389/fmicb.2023.1123803
license: CC BY 4.0
---
# Microbiota-induced regulatory T cells associate with FUT2-dependent susceptibility to rotavirus gastroenteritis
## Abstract
The FUT2 α1,2fucosyltransferase contributes to the synthesis of fucosylated glycans used as attachment factors by several pathogens, including noroviruses and rotaviruses, that can induce life-threatening gastroenteritis in young children. FUT2 genetic polymorphisms impairing fucosylation are strongly associated with resistance to dominant strains of both noroviruses and rotaviruses. Interestingly, the wild-type allele associated with viral gastroenteritis susceptibility inversely appears to be protective against several inflammatory or autoimmune diseases for yet unclear reasons, although a FUT2 influence on microbiota composition has been observed. Here, we studied a cohort of young healthy adults and showed that the wild-type FUT2 allele was associated with the presence of anti-RVA antibodies, either neutralizing antibodies or serum IgA, confirming its association with the risk of RVA gastroenteritis. Strikingly, it was also associated with the frequency of gut microbiota-induced regulatory T cells (Tregs), so-called DP8α Tregs, albeit only in individuals who had anti-RVA neutralizing antibodies or high titers of anti-RVA IgAs. DP8α Tregs specifically recognize the human symbiont Faecalibacterium prausnitzii, which strongly supports their induction by this anti-inflammatory bacterium. The proportion of F. prausnitzii in feces was also associated with the FUT2 wild-type allele. These observations link the FUT2 genotype with the risk of RVA gastroenteritis, the microbiota and microbiota-induced DP8α Treg cells, suggesting that the anti-RVA immune response might involve an induction/expansion of these T lymphocytes later providing a balanced immunological state that confers protection against inflammatory diseases.
## Introduction
Epithelial cell surfaces are lined by a thick layer of glycans called the glycocalyx. The outermost part of the glycocalyx of various epithelial cell types presents inter- and intra-species variability that constitutes the so-called histo-blood group antigens (HBGAs), based on their initial discovery on erythrocytes (Marionneau et al., 2001; Cooling, 2015). These include the ABO and Lewis antigens that are synthesized by sequential addition of several monosaccharides, including fucose residues. The FUT2 α1,2fucosyltransferase is central to the synthesis process of these carbohydrate structures. The FUT2 gene is highly polymorphic and represents one of the few human genes under frequency-dependent selection, strongly indicative of a major role in relation with environmental factors (Ferrer-Admetlla et al., 2009; Silva et al., 2010). The presence of a functional (or wild-type) FUT2 allele generates the so-called secretor phenotype that is characterized by the expression of the A, B or H and Lewisb antigens according to the ABO and Lewis phenotypes. FUT2 mutant null alleles are responsible for the lack of these antigens which characterizes the nonsecretor phenotype (Marionneau et al., 2001). The frequency of the nonsecretor phenotype is quite variable, ranging from over $40\%$ to less than $10\%$ according to human ethnicity (Faden and Schaefer, 2021). Consistent with a role of the FUT2 gene polymorphism, the secretor/nonsecretor phenotype has been associated with either resistance or susceptibility to several pathogens, most strikingly with noroviruses and rotaviruses that together are responsible for the vast majority of gastroenteritis cases, leading to the death of several hundred thousand young children yearly, the latter occurring mostly in low-income countries (Ramani et al., 2016; Bányai et al., 2018; Troeger et al., 2018; Nordgren and Svensson, 2019; Sharma et al., 2020). The role of FUT2 polymorphisms in determining the susceptibility to these viruses is rather well understood. Both human noroviruses and rotaviruses use HBGAs as attachment factors to initiate infection in a strain-specific manner (Ruvoën-Clouet et al., 2013; Tan and Jiang, 2014; Schroten et al., 2016; Ramani and Giri, 2019; Tenge et al., 2021). Distinct strains attach to a variable set of glycan motifs defined by HBGA polymorphisms such that individual strains cannot infect every person in the population, consistent with a host-pathogen co-evolution process (Le Pendu et al., 2014). In other words, it results from an arms race in glycan-mediated host–microbe interactions, that involves a frequency-dependent selection, as previously discussed (Le Pendu and Ruvoën-Clouet, 2020).
The FUT2 gene polymorphism has additionally been associated with a diverse set of inflammatory and autoimmune diseases, including Crohn’s disease, celiac disease, Behcet’s disease, type 1 diabetes, and autoimmune neutropenia of early childhood (Franke et al., 2010; McGovern et al., 2010; Miyoshi et al., 2011; Rausch et al., 2011; Smyth et al., 2011; Ellinghaus et al., 2012; Forni et al., 2014; Tang et al., 2014; Maroni et al., 2015; Xavier et al., 2015; Ihara et al., 2017; Kløve-Mogensen et al., 2022). Overall, the null FUT2 alleles appear to associate with an increased risk for reasons that are not yet understood. Nonetheless, associations with the microbiota composition and FUT2 polymorphisms have been reported (Rausch et al., 2011; Wacklin et al., 2011; Tong et al., 2014; Wacklin et al., 2014; Gampa et al., 2017; Kumbhare et al., 2017; Rodríguez-Díaz et al., 2017; Pan et al., 2021; Rühlemann et al., 2021; Lopera-Maya et al., 2022). Although these remain debated and not fully consistent across studies (Davenport et al., 2016; Turpin et al., 2018), there are strong indications that microbiota composition is partly dependent on gut mucosal glycan composition through either bacterial adhesion molecules or through bacterial use of fucosylated glycans as nutrients (Coyne et al., 2005; Watanabe et al., 2010; Kashyap et al., 2013; Pickard et al., 2014; Wu et al., 2021).
Regulatory T cells are essential to control immune responses and the development of inflammatory and autoimmune diseases. In mice, FoxP3 + Treg cells induced by microbiota species of the *Clostridium clusters* are the dominant Treg cell subset in the gut (Atarashi et al., 2011; Nutsch and Hsieh, 2012). In humans, a likely counterpart has been identified, which we named double positive CD8α (DP8α Tregs), based on their expression of low CD8α levels together with CD4. Indeed, although they lack Foxp3, DP8α Tregs share with mouse clostridia-induced Tregs both the master transcription factor RORγt and a T Cell Receptor (TCR) reactivity to a clostridium species, namely *Faecalibacterium prausnitzii* (*Clostridium cluster* IV), supporting the role of this or a related bacterium in their induction (Jotereau et al., 2022). Abundant in the colonic lamina propria, DP8α Treg cells are also present in blood where they can be identified by their Foxp3−/CD4+/CD8αlow/CxCR6+/CCR6+ phenotype (Godefroy et al., 2018). Faecalibacterium prausnitzii is one of the most abundant gut-associated clostridium-cluster’s member in healthy individuals and its decrease is associated with pathologies such as Inflammatory Bowel Diseases (IBD; Miquel et al., 2013). Interestingly, we also reported the striking and specific decrease of DP8α Treg cells in IBD patients, as compared to healthy controls or infectious colitis (Sarrabayrouse et al., 2014; Godefroy et al., 2018).
Considering this prior knowledge, we hypothesized that a link may exist between the risk of viral gastroenteritis and the abundance of microbiota-induced Treg cells so that individuals most susceptible to the virus infection, those with a FUT2 wild-type allele (secretors), would acquire higher frequencies of Tregs that may contribute to their lower susceptibility to inflammatory diseases in comparison with those with two null alleles (nonsecretors). In an effort to start testing this hypothesis, we looked for a potential relationship between the presence of anti-rotavirus antibodies, the FUT2 gene polymorphism, F. prausnitzii abundance and the level of peripheral DP8α Tregs in healthy young adults. We observed that serum anti-rotavirus antibodies were associated with the wild-type FUT2 allele, as expected from earlier studies, and that the levels of DP8α Treg cells also associated with the wild-type allele, albeit only in individuals with high anti-rotavirus levels.
## Study design, participants and collection of samples
Peripheral blood, saliva and stool samples were obtained from healthy young adults, from 18 to 30 years old. Volunteers were recruited following a medical interview to ascertain that they had no known diseases, no known history of allergies, that they were not under medication, had no drugs intake, no recent alcohol intake and were non-smokers. The GOMMS PRL12009 project was part of the biobank of Biofortis SAS and was designed to enroll 80 participants. Of these, full sample collection and complete data could only be obtained from 72 individuals. This biocollection is registered at the French Research Ministry (AC-2013-1792) and the PRL12009 project was approved by the French Ethic Committee (CPP Ouest IV). All volunteers were aware of the study protocol and fulfilled the informed consent form.
## Analysis of the FUT2 genetic polymorphism
The FUT2 genetic polymorphism was analyzed by a combination of genotyping and of phenotyping of FUT2-dependent histo-blood group antigens (HBGAs). The major single Nucleotide Polymorphisms (SNPs) in the FUT2 gene were investigated as described previously (Marionneau et al., 2005). HBGAs phenotypes were determined from buccal swabs specimens by enzyme-linked immunosorbent assay (ELISA), as described earlier (Loureiro Tonini et al., 2020). Briefly, saliva samples were first boiled for 10 min and then used at a dilution of 1:1,000 in a 0.1 M carbonate/bicarbonate buffer (pH 9,6) to coat 96-well microtiter plates (Maxisorp Nunc-Immuno plates, Thermo Scientific, CA, United States). Primary anti-carbohydrate monoclonal antibodies anti-A (ABO1 9113D10, Diagast, Loos, France), anti-B (B49), anti-Lea (7LE) and anti-Leb (2-25LE; Thermo Scientific, CA, United States) diluted at 1:400 in $5\%$ milk/PBS were incubated for 1 h at 37°C. The lectin biotin-conjugated UEA-1 (Ulex Europaeus Agglutinin I—Vector Laboratories, CA, United States) was additionally used to detect the H antigen. Peroxidase–conjugated secondary reagents were used (Vector Laboratories, CA, United States) and reactions were developed with a 3,3′,5,5′-Tetramethylbenzidine kit (BD OptEIA, BD Biosciences). The cutoff value was defined as a twofold increase in absorbance value compared to the mean of two negative control samples.
## Analysis of circulating anti-RVA antibodies
Neutralizing antibodies (NAbs): The assay was performed as previously described (Barbé et al., 2018). Briefly, serum samples diluted from 1:20 to 1:320 in serum-free medium were pre-incubated with 2 × 103 FFU of trypsin-activated human G1P[8] RVA strain Wa for 1.5 h at 37°C prior to inoculation of MA104 cells. Plates were then incubated at 37°C for 45–90 min. After allowing virus attachment, the inoculum was removed and serum-supplemented medium was added. The infection was left to proceed for 14–15 h. Infected cells in methanol-fixed cell monolayers were detected by staining using a goat polyclonal anti-RV serum (Bio-Rad Antibodies) and FITC-labeled rabbit anti-goat IgG (Fc) antibody (Bio-Rad Antibodies), both diluted at 1:400 in PBS containing $3\%$ BSA. Cell nuclei were stained with DAPI. Plates reading was performed on an ArrayScan HCS (ThermoScientific). The presence of neutralizing antibodies was considered when an inhibition > $50\%$ in comparison with controls (absence of serum preincubation) was detected.
Anti-rotavirus serum IgA: Nunc Maxisorp Immunoplates were coated with a sheep anti-human rotavirus (Bio-Rad) diluted $\frac{1}{500}$ in carbonate buffer pH 9.5 overnight at 4°C. Following a blocking step with $5\%$ BSA/PBS for 1 h30 min at 37°C, plates were incubated for 1 h45 min at 37°C with trypsin-treated Human G1P[8] RVA strain Wa purified as previously described (Barbé et al., 2018) diluted in $1\%$ BSA/PBS. Serum samples were then serially diluted from$\frac{1}{50}$ in $1\%$ BSA/PBS and incubated for 1 h at 37°C. Then, detection of bound IgA was performed using a biotinylated anti-human IgA (Novus Biologicals) incubated for 1 h at 37°C, followed by peroxidase-conjugated streptavidin (Vector Labs) for 1 h at 37°C and reactions were developed with a 3,3′,5,5′-Tetramethylbenzidine kit (BD OptEIA, BD Biosciences). Between each step, plates were washed three times with $0.05\%$ Tween/PBS. Titers were defined as the last dilution giving an OD405 value three times above the background obtained in absence of coating.
## Quantification of circulating DP8α Tregs
Peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll gradient centrifugation. After isolation, PBMCs were stained for 45 min at 4°C in PBS $0.1\%$ bovine serum albumin with the following antibodies: anti-human CD3-PE-Cy7 (clone UCHT1, BD Biosciences), anti-human CD4-FITC (clone 13B8.2, Beckman Coulter), anti-human CD8α-BV421 (clone RPA-T8, BD), anti-human CCR6-PE (clone G034E3, Biolegend), and anti-human CXCR6-APC (clone K041E5, Biolegend). Fluorescence was measured on a BD LSR II flow cytometer (BD Biosciences) and analyzed using FlowJo or DIVA softwares. DP8α Tregs (CD3+CD4+CD8αloCCR6+CXCR6+ cells) were then quantified among total CD3+ T cells, as described previously (Godefroy et al., 2018). The gating strategy is shown in Supplementary Figure S2.
## Quantification of Faecalibacterium prausnitzii and 16S RNA metabarcoding
Microbiota analysis: Whole stools were collected in a fecotainer and immediately stored at 4°C upon transmission to the local lab (in less than 48 h) where 2 g of fresh feces were aliquoted and frozen at −80°C for molecular biology analyses. Genomic DNA of gut microbiota was released by a double lysis step: mechanical in a FastPrep24, MPBiomedicals and chemical with the Maxwell® 16 Tissue DNA Purification Kit (Promega Corporation, Madison, WI, United States). DNA extraction was performed from one aliquot of 200 mg of frozen fecal sample and total genomic DNA was collected in a final volume of 200 μl. Double-stranded DNA (dsDNA) concentrations were measured by fluorimetry using the Qubit® 2.0 Fluorometer and the Qubit® dsDNA broad range assay (Invitrogen by Life Technologies, Carlsbad, CA, United States).
Polymerase chain reaction amplification was performed using 16S universal primers 341F and 785R targeting the V3–V4 region of the bacterial 16S ribosomal genes (Klindworth et al., 2013). The 16S V3–V4 amplicon size was verified by capillary electrophoresis (Agilent 2,100 Electrophoresis Bioanalyzer Instrument, Agilent technologies, Santa Clara, CA, United States). All amplicons were purified with magnetic beads using Agencourt AMPure XP beads (Beckman coulter, Brea, CA, United States). Then, for each sample, a sequencing library was generated by addition of dual indices and Illumina sequencing adapters, using a Nextera XT Index kit (Illumina, San Diego, CA, United States). Each library was cleaned with magnetic beads and its size was determined by capillary electrophoresis. After quantification by fluorimetry (Qubit® 2.0 Fluorometer), libraries were normalized and pooled. The pool of libraries was further denatured and sequenced on the Illumina MiSeq platform, using a 2 × 250 paired-end Miseq kit V2 (Illumina, San Diego, CA, United States). Read sequences from fecal microbiota were analyzed using an in-house bioinformatic pipeline based on mothur v1.33.3 software (Schloss et al., 2009). Briefly, sequences were trimmed and aligned to the V3–V4 region of the 16S gene of the Greengenes database that had been formatted with mothur (gg_13_5_99 release). Chimera sequences were removed using the UCHIME algorithm. Reads were classified using a naive Bayesian classifier against RDP database release 11 with a bootstrap cut-off of $60\%$. Sequences were then clustered into operational taxonomic units (OTUs) using furthest-neighbor clustering at a similarity threshold of $97\%$.
qPCR analysis: Quantifications of F. prausnitzii were performed by qPCR using SYBR Green PCR Master Mix (Applied Biosystems) in a StepOnePlus apparatus (Applied Biosystems). Each reaction was done in duplicate in a final volume of 20 μl with 0.2 μM of each primer and 5 μl of the appropriate dilution of DNA. F. prausnitzii was |quantified using specific primers: sense, 5′-CCATGAATTGCCTTCAAAACTGTT-3′, and antisense, 5′-GAGCCTCAGCGTCAGTTGGT-3′ (Sokol et al., 2008). Appropriate dilution of stool DNA was assessed by performing an Internal Positive Control (AppliedBiosystems, Ref 4308323).
Amplifications were performed with the following temperature steps: 1 cycle at 95°C for 10 min. to denature DNA and activate polymerase, followed by 40 cycles of 95°C for 30 s., 60°C for 1 min. A dissociation step was added to control amplification specificity.
## Statistical analysis
GraphPad Prism v 9.0 (GraphPad Software, San Diego, CA, United States) was used for data analysis. Frequency distributions were analyzed by either Chi2 for trend or Fisher’s exact test. Comparisons of individual values between groups were performed using the Mann–Whitney test for continuous variables. Differences were considered statistically significant when the level of two-tailed significance was $p \leq 0.05.$
## The FUT2 genotype associates with anti-RVA antibodies
Since the presence of antibodies may reflect the history of infection by RVA, we sought to quantify anti-RVA serum antibodies in a cohort of healthy young French adults. Neutralizing antibodies (NAbs) are likely important, but do not appear to provide a correlate of protection (Desselberger, 2014; Caddy et al., 2020), we therefore additionally quantified serum IgA that constitutes a correlate of protection at the population level (Angel et al., 2012). The Wa strain was chosen as a target since it represents a dominant circulating genotype in western Europe (Desselberger, 2014) and since a vaccine based on an attenuated virus of the same genotype shows high efficacy in developed countries (Burnett et al., 2018). We observed that the distribution of NAbs was not homogeneous (Supplementary Figure S1, upper panel). It showed a group of individuals with NAbs, albeit at variable titers and a group lacking detectable NAbs. Therefore, in order to test a potential association with the FUT2 genotype, serum samples were subdivided into two categories, those with or without NAbs, respectively. The distribution of anti-RVA IgAs appeared more normal all individuals, but one showing IgA responses with titers >$\frac{1}{50}$ (Supplementary Figure S1, lower panel). Samples were accordingly grouped into high and low titers based on their position above or below the median value, respectively. Analysis using the Fisher’s exact test indicated that the FUT2 wild-type allele was associated with both the presence of neutralizing antibodies and high IgA titers, although the latter did not reach significance (Figures 1A,B). Some se/se individuals (nonsecretor phenotype) had either neutralizing antibodies or high IgA titers, indicating prior infection, which contrasted with earlier reports that showed a strong association with resistance to infection of nonsecretor children (Imbert-Marcille et al., 2014; Nordgren et al., 2014; Van Trang et al., 2014; Kambhampati et al., 2016; Zhang et al., 2016; Yang et al., 2017; Pérez-Ortín et al., 2019; Farahmand et al., 2021; Wang et al., 2021).
**Figure 1:** *Relationship between the presence of anti-RVA antibodies and the FUT2 genotype in healthy young adults. (A) Neutralizing antibodies (NAbs) titers against the RV strain Wa (G1P[8]) was determined, defining two groups of individuals according to the presence (black bars) or absence (white bars) of neutralizing antibodies, as depicted on Supplementary Figure S1. Chi-square test for trend was used to compare distributions according to the number of wild-type alleles (p = 0.026). (B) Serum anti-Wa IgA titers were classified as high (black bars) or low (white bars) as shown in Supplementary Figure S1 lower panel. Chi-square test for trend was used to compare distributions (p = 0.053). (C) Relationship between neutralizing antibodies and IgA titers against the Wa strain. Bars represent individuals with high (black) and low (white) IgA titers, respectively. Fisher’s exact test, p = 0.0007. (D) Individuals anti-RVA status was defined as strong in the presence of both NAbs and high IgA titers (black bars); intermediate either in absence of NAbs but high IgA or in presence of NAbs but low IgA (gray bars); weak in absence of NAbs and with low IgA titers (white bars). Chi-square test for trend was used to compare distributions (p = 0.013). SE/SE = FUT2 homozygote wild-type; SE/se = FUT2 heterozygotes; se/se = FUT2 null homozygotes.*
Infection episodes may not necessarily result in the generation of both NAbs and IgA. We therefore tested whether there existed an association between these two components of the anti-RVA immune response. As shown in Figure 1C, they were strongly associated. Nonetheless, a fair proportion of individuals presented divergent antibody profiles, having either high IgA titers but no NAbs or the opposite. Since a solid anti-RVA response likely comprises both neutralizing and IgA antibodies, we grouped individuals into three categories, namely those who have both NAbs and high IgA titers, those who have only one of these two components and those who have none of them. The distribution of these three categories of serum samples was then analyzed according to the FUT2 genotype, which confirmed the association between the wild-type allele and a stronger anti-RVA immune status (Figure 1D). The difference in distribution of the three categories of serum samples within the three FUT2 groups is striking. The wild-type homozygotes (SE/SE) presented an inverse distribution in comparison with mutant homozygotes (SE/SE; $p \leq 0.05$, Chi-square test), while heterozygotes (SE/se) showed an intermediate distribution. In addition, when considering phenotypes, that is comparing secretors (SE/SE + SE/se) versus nonsecretors (se/se), individuals who had both NAbs and high IgA titers were significantly over-represented among secretors in comparison with those who were classified as having either one or none of these two types of antibodies ($p \leq 0.05$, two-sided Fisher’s test). These data reveal that the wild-type FUT2 allele (SE) is a risk factor for contracting RVA infections of sufficient magnitude to generate strong anti-viral immune responses (or inversely that null (se) alleles are protective).
## The FUT2 genotype associates with DP8α Treg cells and Faecalibacterium prausnitzii
Since the presence of FUT2 null alleles correlate with the risk of several inflammatory or autoimmune diseases, and since F. prausnitzii-reactive DP8α Tregs appear to protect against intestinal inflammation (Touch et al., 2022), we tested whether the frequency of circulating DP8α Tregs or fecal F. prausnitzii abundance associated with the FUT2 status. The gating strategy used to quantify DP8α *Tregs is* shown in Supplementary Figure S2. Within T cells, double positive CD4+/CD8low expressing both CCR6 and CxCR6, which phenotype targets F. prausnitzii-reactive cells (Godefroy et al., 2018), were analyzed. Despite expected high individual variations, FUT2 wild-type homozygous individuals appeared to present significantly higher frequencies of both DP8α Treg cells and their TCR-specific bacteria species. However, no difference between heterozygotes (SE/se) and null homozygotes (se/se) was apparent (Figures 2A,B). Quantification of F. prausnitzii by PCR (absolute quantification) rather than by 16S RNA analysis (relative quantification) yielded the same result (Supplementary Figure S3).
**Figure 2:** *Relationship between the FUT2 status and either DP8α Treg cells or the Faecalibacterium prausnitzii proportions. (A) Frequencies of circulating DP8α Treg cells quantified as described in the methods section (per 10.000 total CD3 + cells). (B) Relative abundance of the bacterium are given as percentage of the whole microbiota based on 16S metabarcoding. Comparisons were performed by two-sided Mann–Whitney test: *p < 0.05; **p < 0.01. SE/SE = FUT2 homozygote wild-type (green symbols); SE/se = FUT2 heterozygotes (red symbols); se/se = FUT2 null homozygotes (black symbols).*
We next sought to determine whether the above-described association was influenced by the anti-RVA immune status. To this aim, the analyses shown in Figure 2 were replicated after splitting groups of individuals of each genotype according to either the presence of NAbs or the IgA titers. It appeared that the association between the frequency of DP8α Tregs and FUT2 wild-type allele homozygosity was visible only among volunteers who had either NAbs (Figure 3A) or high titered anti-RVA IgAs (Figure 3B). Similar observations, albeit less clear-cut, were made for the relative abundance of F. prausnitzii that tended to be higher among serum samples from individuals with the FUT2 wild-type allele who had either NAbs (Figure 3C) or high anti-RVA IgAs (Figure 3D).
**Figure 3:** *Tripartite relationships between the FUT2 status, the anti-RVA status and either the Treg cells or the Faecalibacterium prausnitzii frequency. (A) Frequencies of circulating DP8α Treg cells (per 10.000 total CD3+ cells) according to the FUT2 status and the presence or absence of NAbs; (B) or the high versus low IgA titers; (C) Relative abundance of Faecalibacterium based on 16S metabarcoding according to the FUT2 status and the presence or absence of NAbs; (D) or the high versus low IgA titers. Comparisons were performed by two-sided Mann–Whitney test: *p < 0.05; **p < 0.01. SE/SE = FUT2 homozygote wild-type (green symbols); SE/se = FUT2 heterozygotes (red symbols); se/se = FUT2 null homozygotes (black symbols).*
## Discussion
Previous studies indicated that the risk for P[8] RVA gastroenteritis of sufficient severity to lead to hospital visit was strongly associated with the wild-type FUT2 allele (Monedero et al., 2018; Ramani and Giri, 2019; Le Pendu and Ruvoën-Clouet, 2020; Sharma et al., 2020; Faden and Schaefer, 2021). We observed here that nonsecretors (se/se) had anti-RVA antibodies and often showed neutralizing antibodies, indicating that they can be infected. This is consistent with the observation that antibody responses to live vaccines containing a P[8] coding gene are present, but lower in nonsecretor children in comparison with secretor children (Kazi et al., 2017; Bucardo et al., 2018; Lee et al., 2018; Armah et al., 2019; Magwira et al., 2020). The reported near complete absence of nonsecretor children among those visiting at the hospital indicates that these children do get infected, but remain either asymptomatic or have mild disease only.
FUT2 null alleles and the nonsecretor phenotype have been consistently associated with autoimmune and inflammatory diseases through many studies. Since dysbiosis is a hallmark of inflammatory diseases, it has been hypothesized that the FUT2 gene would contribute to microbiota composition (Xavier et al., 2015; Imhann et al., 2018; Giampaoli et al., 2020; Zhou et al., 2020). Fucosylation would support a protective microbiota, whereas lack of fucosylation would allow the overgrowth of inflammatory bacteria. Several reports showed associations between gut microbiota composition and FUT2 polymorphisms. However, it appeared that initial studies were underpowered since later studies that included larger number of individuals failed to reproduce the effect and since bacterial species reportedly increased or decreased according to the expressed FUT2 alleles were not consistent across studies (Davenport et al., 2016; Turpin et al., 2018). It does not mean that these associations do not exist, but they may be obscured by the large diversity of the human gut microbiota and by the possibility that FUT2 or HBGAs influences take place at the strain level rather than at the genus or species levels. Thus, a human gut symbiont, *Ruminococcus gravis* was recently reported to display a strain-specific repertoire of glycosidases, one of them showing specificity for a blood group A tetrasaccharide (Wu et al., 2021). Microbiota composition could also be affected by the strain-specific display of HBGA-specific bacterial adhesins such as the blood group A and B-specific adhesin of a *Lactobacillus mucosae* strain (Watanabe et al., 2010). Recent studies including over 7,000 participants found strong associations at the taxa and metabolic pathways levels with the ABO blood group. The associations were relying on the secretor phenotype (Rühlemann et al., 2021; Lopera-Maya et al., 2022). Further, animal studies showed convincing associations with HBGAs. A recent study in pigs reported a strong association between blood group A expression and gut microbiota composition (Yang et al., 2022). Likewise, the microbiota composition of Fut2 KO mice diverges from that of their wild-type littermates and functionally increases susceptibility to induced gut inflammation, fucosylation modulating interactions with nonpathogenic resident microbes (Hooper and Gordon, 2001; Rausch et al., 2011; Kashyap et al., 2013; Garber et al., 2021).
Here, we further observed that the wild-type FUT2 allele was associated with higher proportions of circulating DP8α Treg cells and of their target bacterium F. prausnitzii, albeit only among individuals who presented either neutralizing antibodies or high levels of IgA antibodies, linking the anti-RVA immune response to the level of microbiota-induced Treg cells. The association was detected among homozygote secretors (SE/SE), whilst heterozygotes (SE/se) presented similar levels of these Treg cells as nonsecretors (se/se), suggesting a difference between homozygote and heterozygote secretor individuals. Indeed a higher level of the fucosylated H type 1 epitope in SE/SE saliva samples in comparison with SE/se samples was previously reported, indicating a dose effect of the wild-type allele on fucosylation (Marionneau et al., 2005). Furthermore, we observed here that the levels of anti-rotavirus antibodies in SE/se serum samples were intermediate between those of the homozygous SE/SE and se/se (Figure 1D), suggesting that the strength of the anti-rotavirus immune response is influenced by the FUT2 genotype and not only by the secretor phenotype. Homozygote secretors (SE/SE) might have an initial higher viral load than heterozygotes, and/or develop a more severe disease upon infection. Further studies are required to clarify this issue. In any case, it appears that infection that generates a strong anti-RVA immune response, likely because of a rather severe gastroenteritis, is associated with the presence of higher levels of microbiota-induced Treg cells. Interestingly, our team recently showed that these human Treg cells stimulate IgA synthesis in vitro (Jotereau et al., 2022), suggesting that they might represent an important player of the anti-viral response in case of infection by enteric viruses. Moreover, supporting a role for DP8α Tregs in intestinal homeostasis, low levels of these cells are associated with IBD (Godefroy et al., 2018), and we documented their ability to protect against intestinal inflammation in murine models (Touch et al., 2022; and unpublished results). Similarly, fecal F. prausnitzii levels are diminished in patients with IBD, as compared to healthy individuals (Sokol et al., 2006, 2009) and the anti-inflammatory effect of the bacterium has been well documented (Sokol et al., 2008; Martín et al., 2014; Zhang et al., 2014; Quévrain et al., 2016). Based on these observations, we hypothesized that the immune response against symptomatic RVA infection in early childhood would involve the development/expansion of microbiota-induced Treg cells that could be maintained in adulthood, thereby contributing to a balanced immunological state protective against both enteric viral infection and inflammatory diseases. How modulation of Treg cells levels is induced during the course of the anti-viral immune response remains to be determined, but it likely involves microbiota species abundance since their generation requires presentation of specific bacteria-derived epitopes by dendritic cells (Alameddine et al., 2019).
The limited number of volunteers enrolled in the present study did not allow to analyze an additional potential contribution of the ABO and Lewis polymorphisms (ABO and FUT3 genes), since there were too few individuals in the resulting subgroups for meaningful comparisons. Studies involving larger cohorts of healthy adults would be required for confirmation and to study the effect of the combined FUT2, ABO and FUT3 polymorphisms. Also, it would be interesting to analyze whether norovirus infection associates similarly with the frequency of Treg cells. An additional issue is that the present observations were obtained from young adults whilst RVA gastroenteritis is mainly occurring before 5 years of age. It is therefore unclear if the detected antibodies reflect early childhood infections or more recent asymptomatic reinfections. Studies on children with acute gastroenteritis and autoimmune diseases are thus warranted.
If our hypothesis nonetheless proved true, that is if immune response against symptomatic RVA infection in early childhood involves the development/expansion of microbiota-induced Treg cells, it would be important to ask whether RVA vaccines expand these Tregs, akin to symptomatic infections. If that was not the case, vaccinated children being protected from symptomatic gastroenteritis would fail to sufficiently expand microbiota-induced Treg cells and therefore might be at a higher risk of developing inflammatory and autoimmune diseases in adulthood. Comparing the levels of microbiota-induced Treg cells between vaccinated and nonvaccinated children or young adults would be a first step to explore this possible long-term side effect of rotavirus vaccines.
In conclusion, we observed an association between the FUT2 genotype, anti-RVA antibodies and microbiota-induced Tregs frequency, suggesting that symptomatic RVA infection leads to the development of these Treg cells that will later contribute to immune homeostasis. It is interesting to observe that genetic polymorphisms selected in the context of a host-pathogen co-evolution involving young children might affect susceptibility to inflammation much later in life. This could have important implications for the development of future vaccines, warranting further studies.
## Data availability statement
The data presented in the study are deposited at the European Nucleotide Archive repository, accession number ERP144000: https://www.ebi.ac.uk/ena/browser/view/PRJEB58917.
## Ethics statement
The studies involving human participants were reviewed and approved by French Ethic Committee (CPP Ouest IV). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JLP conceived and designed the study and wrote the manuscript. JLP, NR-C, and FJ supervised the study. EG, LB, BM-V, JR, AB, TC, SL, DM, and J-MC carried out the experiments and discussed results. EG, TC, and JLP analyzed the data. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by an ANR-DGOS grant, program CE17, grant #15-0007-01 and by a Mérieux Research Grant, GOMMs, to JLP.
## Conflict of interest
SL and TC are employed by Biofortis Merieux Nutrisciences.
The remaining authors declare that the research was conducted in absence of any commercial or financial relationship that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1123803/full#supplementary-material
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|
---
title: Predictive values of clinical data,molecular biomarkers, and echocardiographic
measurements in preterm infants with bronchopulmonary dysplasia
authors:
- Huawei Wang
- Dongya Yan
- Zhixin Wu
- Haifeng Geng
- Xueping Zhu
- Xiaoli Zhu
journal: Frontiers in Pediatrics
year: 2023
pmcid: PMC10008901
doi: 10.3389/fped.2022.1070858
license: CC BY 4.0
---
# Predictive values of clinical data,molecular biomarkers, and echocardiographic measurements in preterm infants with bronchopulmonary dysplasia
## Body
Key messages •*Bronchopulmonary dysplasia* seriously affects the treatment and long-term prognosis of preterm infants.•Preventing BPD is important in clinical practice.•Tricuspid regurgitation flow rate (m/s), NT-proBNP (pg/ml), ventilator-associated pneumonia, days of FiO2 ≥ $40\%$, red blood cell suspension infusion volume (ml/kg), and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth are BPD risk factors.
## Abstract
### Objective
We aimed to use molecular biomarkers and clinical data and echocardiograms that were collected during admission to predict bronchopulmonary dysplasia (BPD) in preterm infants with gestational age ≤32 weeks.
### Methods
Eighty-two patients (40 with BPD, BPD group and 42 healthy as controls, non-BPD group) admitted to the Department of Neonatology of the Children's Hospital of Soochow University between October 1, 2018, and February 29, 2020, were enrolled in this study at the tertiary hospital. Basic clinical data on the perinatal period, echocardiographic measurements, and molecular biomarkers (N-terminal-pro-B-brain natriuretic peptide, NT-proBNP) were collected. We used multiple logistic regression analysis to establish an early predictive model for detecting BPD development in preterm infants of gestational age ≤32 weeks. We also used a receiver operating characteristic curve to assess the sensitivity and specificity of the model.
### Results
No significant differences were found between the BPD and non-BPD groups in terms of sex, birth weight, gestational age, incidence of asphyxia, maternal age, gravidity, parity, mode of delivery, premature rupture of membranes >18 h, use of prenatal hormones, placental abruption, gestational diabetes mellitus, amniotic fluid contamination, prenatal infections, and maternal diseases. The use of caffeine, albumin, gamma globulin; ventilation; days of FiO2 ≥ $40\%$; oxygen inhalation time; red blood cell suspension infusion volume (ml/kg); and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth were higher in the BPD group than in the non-BPD group. The levels of hemoglobin, hematocrit, and albumin in the BPD group were significantly lower than those in the non-BPD group. The total calorie intake was significantly lower in the BPD group on the 3rd, 7th, and 14th day after birth than in the non-BPD group ($P \leq 0.05$). The incidence rates of patent ductus arteriosus (PDA), pulmonary hypertension, and tricuspid regurgitation were significantly higher in the BPD group than in the non-BPD group ($P \leq 0.05$). The serum level of NT-proBNP 24 h after birth was significantly higher in the BPD group than in the non-BPD group ($P \leq 0.05$). Serum NT-proBNP levels were significantly higher in infants with severe BPD than in those with mild or moderate BPD ($P \leq 0.05$).
### Conclusion
As there were various risk factors for BPD, a combining clinical data, molecular biomarkers, and echocardiogram measurements can be valuable in predicting the BPD. The tricuspid regurgitation flow rate (m/s), NT-proBNP (pg/ml), ventilator-associated pneumonia, days of FiO2 ≥ $40\%$ (d), red blood cell suspension infusion volume (ml/kg), and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth were the most practical factors considered for designing an appropriate model for predicting the risk of BPD.
## Introduction
Bronchopulmonary dysplasia (BPD) is a common chronic lung disease and one of the most severe sequelae of respiratory system in preterm infants [1]. More than $40\%$ of extremely premature and extremely low birth weight infants (gestational age <28 weeks/birth weight <1000 g) progress to BPD in developed countries, and this rate has not decreased substantially in the past 20 years [2, 3]. Infants with BPD who survive may experience several neurodevelopmental impairments and respiratory problems, and some serious influences on neural development and cardiopulmonary function can extend into adolescence and adulthood [4, 5, 6].
BPD develops from the interaction of various types of damage influenced by inflammation related to chorioamnionitis, infections, ventilation, and high-concentration oxygen [7]. However, there are currently no effective treatments for preventing the development of BPD. Furthermore, some existing therapeutic measures, such as systemic glucocorticoids, can cause adverse effects, including neurodevelopmental impairment [8].
Preterm and immature lung tissues are the key factors contributing to BPD development. Medical treatments, such as mechanical ventilation and the inhalation of high concentrations of oxygen, which are sometimes used to rescue preterm infants, may also cause, or aggravate BPD [9]. Currently, there are no effective treatments for premature BPD. Therefore, the strategies to prevent BPD are crucial in clinical practice, and it is essential to explore relevant indicators for BPD. In a previous study, we collected perinatal clinical data and the neonatal critical illness score (NCIS) and certain identified molecular biomarkers to isolate risk factors for BPD. Another study showed that clinical data, echocardiographic measurements, and molecular biomarkers may assist in predicting the patients who will be subjected to the worst grades of BPD [10]. Accordingly, we collected perinatal clinical and echocardiographic data and measured N-terminal-pro-B-brain natriuretic peptide (NT-pro BNP) levels to predict BPD in preterm infants [11]. We explored the effectiveness of using clinical data, echocardiograms, and molecular biomarkers to predict BPD in preterm infants.
## Patients and methods
This prospective study enrolled neonates admitted to the Department of Neonatology of the Children's Hospital of Soochow University from October 1, 2018 to February 29, 2020. The inclusion criteria for the participants were preterm infants with a gestational age ≤32 weeks and a hospital stay of ≥28 d. Clinical data and echocardiographic variables were recorded at various time points. The exclusion criteria were an admission age older than 24 h, infection at admission, major congenital abnormalities, surgical intervention requirements during the NICU stay, an unplanned discharge, and incomplete clinical data.
This study has already obtained an approvement from the Ethics Committee of the Children's Hospital of Soochow University. The parents of all infants provided written informed consent.
## Clinical variables
Clinical data were collected from medical records and the following maternal, infant, and prenatal factors were included: [1] maternal conditions, including maternal age, delivery mode, antenatal corticosteroid use, premature rupture of membranes (duration >18 h), placental abruption and placenta previa, gestational diabetes, gestational hypertension, gestational anemia, preeclampsia, amniotic fluid contamination, prenatal infection (fever, chorioamnionitis), and other maternal diseases. [ 2] General conditions of the infants, including sex, gestational age, birth weight, age at admission, incidence of asphyxia, conception through in vitro fertilization, twins, or multiple births. [ 3] Primary diseases and complications that occurred during the hospitalization of preterm, including neonatal respiratory distress syndrome (NRDS), pneumonia, pneumothorax, ventilator-associated pneumonia (VAP), feeding intolerance, parenteral nutrition-associated cholestasis (PNAC), brain injury in premature infants (BIPI), periventricular/intraventricular hemorrhage (PVH/IVH), retinopathy of prematurity (ROP), hemodynamically significant patent ductus arteriosus (hs-PDA), frequent apnea, sepsis, bacterial meningitis, pulmonary hemorrhage, and necrotizing enterocolitis (NEC). [ 4] Treatment(s) during hospitalization: caffeine, albumin, gamma globulin, complete total enteral nutrition later than 24 d, more than three blood transfusions, invasive ventilation time, days of FiO2 ≥ $40\%$, non-invasive ventilation time, oxygen inhalation time, and red blood cell suspension infusion volume (ml/kg). [ 5] Laboratory tests administered upon admission: white blood cell count (WBC, 109/L), red blood cell count (RBC, 1012/L), platelets (PLT, 109/L), hemoglobin (Hb, g/L), neutrophil absolute value (NE, 109/L), lymphocyte absolute value (LY, 109/L), red blood cells deposited (Hct, L/L), average red blood cell volume (MCV, fL), albumin levels (propagated, g/L), and prealbumin levels (Pa, mg/L). [ 6] Oral fluid volume 3, 7, 14, 21, and 28 d postnatal, fluid intake, caloric intake (enteral, parenteral, and total), and the times for beginning feeding and reaching total enteral nutrition.
## Diagnostic criteria
We firstly define the important clinical indicators for the sake of understanding. [ 1]Definition of BPD and Clinical Grading [12]The diagnostic criteria of BPD adopted in our study was based on the standard of the National Institute of Child Health and Human Development (NICHD) published in 2001, which defines BPD as follows: (i) preterm low birthweight infants treated with oxygen (FiO2 > 0.21) for at least 28 days; (ii) persistent or progressive respiratory insufficiency; (iii) lungs with typical x-ray or CT scan findings (e.g., bilateral lungs with enhanced texture, reduced permeability, ground glass-like, localized emphysema, or cystic changes); (iv) exclusion of congenital cardiopathy, pneumothorax, pleural effusion, and sputum. The clinical grading was based on the supplemental O2 of the infants at 36 weeks postmenstrual age or discharge (GA <32 weeks) and at 56 days postnatal age or discharge (GA ≥ 32 weeks).
The clinical grading was classified as follows: Mild: breathing room air; moderate: a fraction of inspired oxygen (FiO2) < 0.3; severe: FiO2 ≥ 0.3 and/or positive pressure ventilation or mechanical ventilation. [ 2]Definition of UEGR [13]Extrauterine growth restriction (EUGR) is a common condition in very low birth weight (VLBW) preterm infants (≤1,500 g). Most affected infants have a birth weight that is average for gestational age, but by the time of hospital discharge have a weight that is less than the tenth percentile for corrected gestational age. [ 3]Definition of VAP [14]VAP was defined as a nosocomial infection happening 48 h after mechanical ventilation. [ 4]Diagnosis of NRDS [15]NRDS was defined as the presensce of respiratory distress and increased oxygen requirement (FiO2 > 0.4), which cannot be explained by other causes via chest x-ray and lab findings. [ 4]Definition of BIPI [16]BIPI refers to various pathologies due to prenatal, intrapartum, or/and postnatal conditions factors that lead to varying degrees of cerebral ischemia and/or hemorrhagic loss in preterm infants, it can lead to long-term nervous system sequelae and even death. [ 5]Definition of PNAC [17]PNAC was defined as cholestasis attributable to PN use, with other parameters excluded. [ 6]Definition of has-PDA [18]Echocardiographic evidence of a hs-PDA met one of the following criteria: ductal diameter ≥1.5 mm, unrestrictive pulsatile ductal flow (ductus arteriosus peak velocity <2.0 m/s), left heart volume loading (left atrium to aortic ratio >1.5), left heart pressure loading (early passive to a late atrial contractile phase of transmittal filling ratio >1.0 or isovolumic relaxation time ≥50).
## Echocardiographic measurements
All included patients underwent echocardiography on the first day, and those with congenital heart disease were excluded. Patients were also examined for PDA, atrial septal defects, pulmonary vein stenosis, tricuspid regurgitation (TR), TR velocity, left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), and pulmonary hypertension (PH). The echocardiography protocol and examination results were discussed and agreed upon by pediatric cardiologists working for >10 years at our hospital. All parameters and indicators that could be quantified, including right atrial and right ventricular dilation, were recorded.
## Analytical biomarker determination
Blood samples were collected and preserved on the first day. The levels of NT-proBNP in plasma were analyzed using a commercial enzyme-linked immunosorbent assay [ELISA, Roche Diagnostic Products (Shanghai) Co. Ltd, China].
## Statistical analysis
SPSS 24.0 was used for analyzing the data in this study. Categorical variable data were analyzed using either the chi-square test or Fisher's exact test; normally distributed variable data are represented as the mean ± standard deviation and processed using independent t-test. Non-normally distributed variable data were represented as the median value ± interquartile range [M (P25, P75)] and analyzed using non-parametric tests. Significant factors were selected and recruited in the next step of logistic regression analysis to explore the independent risk factors for BPD occurrence in premature infants. The sensitivity and specificity of the predictive models were evaluated via the AUC. Statistical significance was set at $P \leq 0.05.$
## Clinical data of preterm infants
From October 1, 2018, to February 29, 2020, 232 preterm infants ≤32 weeks were admitted to our hospital. Of these infants, 12 patients were discharged automatically, 32 were hospitalized for less than 28 d, and 15 underwent surgery during hospitalization. Among these, 40 were diagnosed with BPD. A total of 42 preterm infants with no differences in general information from the BPD group were randomly matched to the non-BPD group. In total, 82 preterm infants were enrolled in this study. In the BPD group, 19, 15, and 6 cases were classified as mild, moderate, and severe, respectively. There were no significant differences in basic and clinical data between the two groups ($P \leq 0.05$, Table 1), including gestational age, sex, birth weight, incidence of asphyxia, maternal age, chorioamnionitis, use of prenatal steroids, oligohydramnios, gestational hypertension, gestational diabetes, and placental abruption.
**Table 1**
| Unnamed: 0 | BPD group (n=40) | Non-BPD group (n=42) | χ2/U/t | P |
| --- | --- | --- | --- | --- |
| Gender [male, n (%)] | 25 (62.50) | 25 (59.52) | 0.076 | 0.782 |
| Weight at birth (g, x̄±s) | 1353 ± 198.18 | 1410 ± 196.75 | −1.300 | 0.197 |
| Gestational age (week, x̄±s) | 29.96 ± 1.10 | 30.34 ± 0.60 | −1.944 | 0.060 |
| Neonatal asphyxia, n (%) | 11 (27.50) | 5 (11.90) | 2.992 | 0.084 |
| Mother’s age < 20 or > 35 years, n (%) | 10 (25.00) | 8 (19.05) | 0.424 | 0.598 |
| Chorioamnionitis, n (%) | 4 (10.00) | 0 | — | 0.052 |
| Prenatal steroids, n (%) | 26 (65.00) | 29 (69.05) | 0.152 | 0.697 |
| Oligohydramnios, n (%) | 5 (12.50) | 1 (2.38) | — | 0.105 |
| Gestational hypertension, n (%) | 11 (27.50) | 9 (21.43) | 0.410 | 0.522 |
| Gestational diabetes, n (%) | 6 (15.00) | 4 (9.52) | — | 0.514 |
| Placenta abruption, n (%) | 1 (2.50) | 3 (7.14) | — | 0.616 |
| Neonatal pneumonia, n (%) | 39 (97.50) | 40 (95.24) | — | 1.000 |
| NRDS, n (%) | 16 (40.00) | 9 (21.43) | 3.334 | 0.068 |
| Pneumothorax, n (%) | 1 (2.50) | 1 (2.38) | — | 1.000 |
| VAP, n (%) | 12 (30.00) | 1 (2.38) | — | 0.001 |
| Feeding intolerance, n (%) | 22 (55.00) | 10 (23.81) | 8.376 | 0.004 |
| BIPI, n (%) | 14 (35.00) | 5 (11.90) | 6.139 | 0.013 |
| PVH-IVH, n (%) | 10 (25.00) | 3 (7.14) | — | 0.035 |
| Apnea, n (%) | 14 (35.00) | 4 (9.52) | — | 0.007 |
| Sepsis, n (%) | 7 (17.50) | 1 (2.38) | — | 0.027 |
| CNS infection, n (%) | 3 (7.50) | 0 | — | 0.112 |
| Pneumorrhagia, n (%) | 3 (7.50) | 0 | — | 0.112 |
| NEC, n (%) | 0 | 0 | — | — |
| ROP, n (%) | 12 (30.00) | 3 (7.14) | — | 0.010 |
| PNAC, n (%) | 14 (35.00) | 2 (4.76) | — | 0.000 |
| EUGR, n (%) | 21 (52.50) | 10 (23.81) | 7.172 | 0.007 |
| Caffeine, n (%) | 21 (52.5) | 8 (19.0) | 10.030 | 0.002 |
| Albumin, n (%) | 28 (70) | 10 (23.8) | 17.579 | 0.000 |
| Intravenous immunoglobulin, n (%) | 30 (75) | 10 (23.8) | 8.721 | 0.003 |
| Invasive ventilation, n (%) | 20 (50.00) | 6 (14.29) | 12.068 | 0.001 |
| Duration of invasive ventilation [d, M (P25, P75)] | 7.5 (5, 16.25) | 3 (1.50, 3) | 481.5 | 0.000 |
| Duration of non-invasive ventilation [d, M (P25, P75)] | 18.5 (12.25,26.75) | 2.5 (0,7) | 144 | 0.000 |
| Days of FiO2 >40% [d, M (P25, P75)] | 2 (2, 3) | 3 (3, 3.75) | 158 | 0.000 |
| Days of oxygen inhalation [d, (x̄±s)] | 46.35 ± 14.83 | 19.39 ± 7.76 | 9.749 | 0.000 |
| Red blood cells Transfusion, n (%) | 29 (72.50%) | 8 (19.05%) | 23.64 | 0.000 |
| Proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥ 24 days, n (%) | 31 (77.5) | 9 (21.4) | 25.781 | 0.000 |
| Time when enteral nutrition starts (d) | 2 (2, 4) | 2 (1,2) | 498.5 | 0.001 |
## Risk factors for BPD in preterm infants
The incidence of VAP, feeding intolerance, BIPI, PVH-IVH, apnea, sepsis, ROP, PNAC, and extrauterine growth restriction (EUGR) was significantly more frequent in the BPD group ($P \leq 0.05$, Table 1) than in the non-BPD group. The use of caffeine, albumin, and intravenous immunoglobulin and ventilation (including the invasive and non-invasive modes) was more frequent in the BPD group ($P \leq 0.05$) than in the non-BPD group. Days on oxygen inhalation (FiO2 > $40\%$), the proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth, and the duration of enteral nutrition was longer in the BPD group ($P \leq 0.05$) than in the non-BPD group. The number of red blood cell transfusions during the stay in the NICU was higher in the BPD group ($P \leq 0.05$, Table 1) than in the non-BPD group. The hemoglobin, hematocrit, and serum albumin levels were significantly different between the two groups ($P \leq 0.05$, Table 2). VAP (OR = 14.443, $95\%$ CI: 1.045–199.522), days of FiO2 > $40\%$ (OR = 1.943, $95\%$ CI: 1.047–3.608), the red blood cell transfusion volume (ml/kg) (OR = 1.108, $95\%$ CI: 1.044–1.175), and the proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth (OR = 7.683, $95\%$ CI: 1.320–44.714) were identified as possible risk factors for BPD development using multiple regression analysis (Table 3).
## Echocardiographic evaluations in preterm infants
All preterm infants underwent complete echocardiographic examination upon inclusion (day 1), and congenital heart disease was ruled out. We interpreted echocardiograms for PDA, the TR velocity, LVEF, and LVFS and found that TR and Hs-PDA were more frequent in patients with BPD ($p \leq 0.05$) than in those without BPD. Furthermore, TR velocity was higher in the BPD group ($P \leq 0.05$, Table 4) than in the non-BPD group.
**Table 4**
| Unnamed: 0 | BPD (n = 40) | Non-BPD (n = 42) | χ2/U/t | P |
| --- | --- | --- | --- | --- |
| Tricuspid regurgitation [n (%)] | 28 (70.0) | 19 (45.2) | 5.135 | 0.023 |
| Tricuspid regurgitation velocity [m/s, x¯±s] | 2.36 ± 0.77 | 1.33 ± 0.53 | 5.072 | 0.0 |
| LVEF (%, x¯±s) | 70.16 ± 6.84 | 67.74 ± 4.90 | 5.579 | 0.071 |
| LVFS (%, x¯±s) | 36.84 ± 5.26 | 34.95 ± 3.59 | 6.79 | 0.062 |
| Hs-PDA | 26 (65) | 6 (14.3) | 7.25 | 0.007 |
## Serum NT-proBNP levels
Serum NT-proBNP levels were the lowest on the first day in the non-BPD group ($P \leq 0.05$, Figure 1) and were also significantly different from those in the BPD group ($P \leq 0.05$). Serum NT-proBNP levels gradually increased with BPD severity ($P \leq 0.05$, Figure 2).
**Figure 1:** *The flow chart of study design.* **Figure 2:** *Serum NT-proBNP levels of BPD and no-BPD patients.* **Figure 3:** *Serum NT-proBNP levels of different groups based on the severity of BPD (no-BPD, mild BPD, moderate BPD, and severe BPD).*
## Sensitivity and specificity of individual risk factors for BPD
ROC analysis using the TR velocity, NT-proBNP, VAP, days of FiO2 > $40\%$, transfusion volume of red blood cells, and the proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth indicated that these variables can be considered potential predictors or risk factors of BPD. The AUC, sensitivity, specificity, and Youden index values for these variables are shown in Table 5.
**Table 5**
| Variable | Sensitivity (%) | Specificity (%) | AUC | 95%CI | P | Cut-off | Youden index |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Tricuspid regurgitation velocity | 88.10 | 62.50 | 0.735 | 0.623–0.848 | 0.0 | 1.45 | 0.506 |
| NT-proBNP | 69.00 | 80.00 | 0.802 | 0.709–0.896 | 0.0 | 2688.30 | 0.490 |
| VAP | — | — | 0.638 | 0.517–0.760 | 0.031 | — | — |
| Days of FiO2 > 40% | 85.7 | 87.5 | 0.846 | 0.751–0.941 | 0.0 | 1.50 | 0.732 |
| Transfusion volume of red blood cells | 88.10 | 85.00 | 0.903 | 0.832–0.974 | 0.0 | 0.73 | 16.616 |
| Proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 days after birth (%) | — | — | 0.78 | 0.676–0.885 | 0.0 | — | — |
| Prediction model | 97.60 | 92.50 | 0.986 | 0.968–0.999 | | 0.60 | 0.901 |
## Sensitivity and specificity of the BPD prediction model
The TR velocity, NT-proBNP, VAP, days of FiO2 > $40\%$, transfusion volume of red blood cells, and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth were included to yield a predictive model for BPD. The X2-value of the model was 89.203 ($P \leq 0.001$), suggesting these variables may predict the risk of BPD. The Hosmer-Lemeshow test was conducted using the classification interaction table (df = 8, $P \leq 0.05$), which demonstrated that the model was consistent with the indicators well. The test results indicated that the combination of these variables in the model yielded an AUC of 0.986. The sensitivity and specificity were $97.60\%$ and $92.50\%$, respectively. The predictive model yielded a higher AUC value, sensitivity, and specificity than any individual variables (Table 6).
**Table 6**
| Variable | β | SE | Wald | P | OR | 95%CI |
| --- | --- | --- | --- | --- | --- | --- |
| Tricuspid regurgitation velocity (m/s) | 1.726 | 0.847 | 4.157 | 0.041 | 5.619 | 1.069–29.534 |
| NT-proBNP | 0.001 | 0.001 | 3.588 | 0.058 | 1.001 | 1.000–1.002 |
| VAP | 0.821 | 1.351 | 0.369 | 0.543 | 2.273 | 0.161–32.125 |
| Days of FiO2 > 40% (d) | 1.409 | 0.576 | 5.974 | 0.015 | 4.092 | 1.322–12.665 |
| Transfusion volume of red blood cells (ml/kg) | 0.09 | 0.039 | 5.289 | 0.021 | 1.095 | 1.013–1.182 |
| Proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 days after birth (%) | 4.174 | 1.82 | 5.261 | 0.022 | 65.001 | 1.835–2301.904 |
## Discussion
Prenatal and postnatal factors, among others, can influence the development of BPD in preterm infants. In this study, we systematically analyzed the clinical data, echocardiographic measurements, and molecular biomarkers of preterm infants of gestational age ≤32 weeks. We found that the TR flow rate (m/s), NT-proBNP (pg/ml), VAP, days of FiO2 > 0.4, red blood cell suspension infusion volume (ml/kg), and proportion of infants who received total enteral nutrition (120 kcal/kg.d) ≥24 d after birth were practical risk factors contributing to the development of BPD.
BPD is a serious pulmonary disease caused by multiple factors, including volume injury, infection, inflammation, and abnormal repair of the lung [19]. Exploring the risk factors in the prenatal and postpartum periods is a possible area of research. Based on our findings, a neonatologist could anticipate and prevent BPD occurrences and develop therapeutic strategies for neonatal patients to decrease the damage to the pulmonary systems of infants in the future. Several predictive models currently exist, most of which depend on clinical variables, such as gestation, oxygen intake. For example, a study in Korea that included 4,600 very low birth weight preterm infants (VLBWIs, with a birthweight less than 1,500 grams) found that perinatal data, including 5 min Apgar scores, birth weights, necessary resuscitation procedures after birth, were significant indicators for VLBWIs [20]. However, most of these existing models have limitations in terms of predicting the progression of high-risk preterm infants to BPD [21, 22]. The incidence rate of BPD remains high as no single, specific indicator with a high predictive value has been widely accepted, making early intervention challenging [23].
Echocardiography is widely regarded as a useful and valuable screening tool for assessing the possibility of BPD in preterm infants, and echocardiographic measurements can be used to evaluate elevated pulmonary pressure (PAP). Echocardiography can be used to evaluate the elevated pulmonary vascular resistance index (PVRi) and classify severity based on pressure measurements. Mourani et al. [ 24] assessed the clinical value of using echocardiography to diagnose PH in infants with BPD and other lung diseases. We estimated PAP using echocardiography by monitoring the tricuspid valve regurgitation jet velocity in infants with all types of lung disease caused by various etiologies; echocardiographic abnormalities in the TR jet showed a high PH prediction accuracy. In infants with BPD, elevated pulmonary arterial pressure determined using echocardiography is usually associated with serious conditions and a substantial risk of mortality [25]. In the United States, echocardiography is usually considered a less invasive tool for evaluating elevated PAP in preterm infants who have moderate or severe BPD [26, 27]. Further, echocardiography is often used to measure PVR indirectly by calculating the blood flow velocity of the TR to estimate PAP [28].
In this study, the TR velocity was higher in patients with BPD than in those without BPD. Although TR velocity has not been reported to be related to the occurrence of BPD, elevated pulmonary arterial pressure leads to an increased and continuous deterioration of pulmonary circulation resistance and abnormal developments in pulmonary capillaries.
NT-proBNP has been widely used to diagnose heart failure and is often recognized by cardiomyocytes in response to excessive pressure and volume overload [29]. NT-proBNP has a relatively stable chemical structure in vitro and can also remain in stable in blood samples after being drawn or preserved for over 72 h [30]. NT-proBNP levels may also be valuable in predicting severe and moderate BPD, as indicated in a prospective study [31, 32]. In preterm infants, excessive PAP as well as high-concentration oxygen absorption in immature lungs with an ongoing maturation process of the microstructure in the alveolar and microvascular regions may lead to textural anomalies of pulmonary vessels. Neonates with persistent PH show higher serum NT-proBNP levels [33], which can indicate the left ventricular load. After birth, the infant circulatory system transits from intrauterine fetal circulation to postnatal neonatal circulation, which is always accompanied by lung expansion; this may elevate systemic pulmonary vascular resistance and increase pulmonary blood flow volume. These changes may also increase ventricular volumes and pressure loads, which can stimulate BNP synthesis and secretion in the ventricle in the early days after birth [34]. Serum BNP levels are higher after birth and decrease with the maturation of cardiac functions. Serum NT-proBNP levels often fluctuate with the mean pulmonary arterial pressure in the early days after birth in preterm neonates [35]. Several studies have attempted to demonstrate the pathophysiological and clinical applications of serum NT-proBNP levels in patients with BPD. Sellmer et al. conducted a study involving 183 infants born at a gestational age ≤32 weeks and revealed that higher than normal levels of serum NT-proBNP three days after birth were closely related to an increased risk of BPD or higher mortality in preterm infants [36]. Therefore, we speculate that increased NT-proBNP levels in premature infants within 24 h after birth is related to increased PAP, but these levels may also be related to an increase in early infection, inflammatory stimulation, and pro-inflammatory cytokines in premature infants with BPD.
Nutrition supplementation has an important function in the treatment and growth of infants; preterm infants with a higher volume of daily fluid and calorie intake and less body weight loss are at a higher risk for BPD in the early days of the first postnatal week [37]. Greater quantities of fluid and nutrition intake to prevent weight loss may also cause pulmonary edema and worsen lung function in infants. The median age for reaching the desired calorie and energy intake (120 kcal/kg.d) through enteral feeding was 24 d in the 82 preterm infants included in this study. The proportion of infants who received total enteral nutrition (120 kcal/kg) d) ≥24 d after birth was higher in the BPD group; this also represented a potential risk factor for BPD depending on the logistic regression analysis, indicating that reaching the goal energy and calorie intake through enteral feeding for over 24 d was also a risk factor for developing BPD. Therefore, constant improvements and continuous optimization of the administered nutritional formula may improve the treatment and prognoses of infants and reduce the morbidity of BPD.
Our study found that the number of preterm infants with oxygen inhaled days of FiO2 > $40\%$ and the proportion of preterm infants diagnosed with VAP was significantly higher in the BPD group; this result is consistent with previously reported results [38]. High concentrations and long durations of oxygen intake may be harmful and toxic. Preterm infants requiring an oxygen supply concentration of over $30\%$ during resuscitation, regardless of duration, were at a lower risk for developing BPD than those in the $90\%$ or higher concentration oxygen group [39].
VAP is a severe mechanical ventilation complication that represents the second most common and difficult-to-cure infection in NICUs [40]. Neonatal VAP seems to be significantly correlated with increased mortality, a longer duration of invasive mechanical ventilation, and longer hospital and NICU stays, especially in extremely preterm neonates [41, 42]. Lung tissue inflammation and injury caused by VAP may substantially negatively influence lung alveolar and pulmonary alveolarization development in the early and critical stage in the postnatal period, and this pathophysiological process may partially explain the persistently high incidence rate of BPD [43, 44].
Anemia is commonly observed in preterm infants. Transfusions of RBC represent one of the most important methods of treating preterm infants; however, RBC transfusions are related to serious illnesses, especially BPD and cerebral hemorrhage, and they can also cause other diseases, such as NEC [45]. Patel et al. suggested that serious diseases, such as BPD and NEC, in low gestational and birth weight infants are more likely to be associated with severe anemia rather than complications from the transfusion itself [46]. In our study, the volume of RBC transfusions was one of the potential risk factors for BPD. In future, medical professionals should consider using pharmacological treatments to replace blood transfusions.
BPD is a multifactorial disease that is evolved by a complex combination of prenatal risk factors. Therefore, the present study aimed to establish a multifactorial prediction model for early detecting the BPD using a combination factors. The model that including molecular biomarkers and clinical data and echocardiograms can help to predict the development of BPD.
## Limitations
This study has many limitations, including the small number of cases considered from a hospital and single clinical center. Although the incorporated multi-factor multifactorial model may help predict the occurrence and development of BPD in preterm infants with a relatively high sensitivity and specificity, it also displayed some disadvantages in that it could not predict the severity of BPD in preterm infants. Multicenter studies with variable grades of hospitals as well as a large sample size are needed to improve and enhance the BPD prediction models; this research could potentially increase prediction accuracy and improve effectiveness in preventing the progression of BPD.
## Conclusions
In summary, inflammation, hyperoxia, blood transfusion, and malnutrition can lead to the development of BPD. TR flow rate and NT-proBNP levels were positively correlated with the occurrence of BPD. No single factor was effective in predicting BPD, but a combined regression model constructed using multiple indicators may predict the occurrence of BPD with an increased accuracy.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by All parents or legal guardians of the participants provided written informed consent. This study was approved by the Ethics Committee of the Children's Hospital of Soochow University (ethics review number: 2020CS023). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
HW and DY designed the study and wrote the manuscript. ZW and HG conducted the clinical data collection and data analysis. XPZ and XLZ supervised the study design and execution, performed the final data analyses, and contributed to the writing of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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